Glossary A

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Keywords and Definitions

A/D converter: This electronic device transforms an analog voltage signal from the physical domain to a digital number in the information domain with a predetermined number of bits.

Accelerometer transfer function: This mathematical representation, typically a ratio of Laplace transforms in the s-domain, describes the relationship between the input acceleration and the sensor output voltage.

Affine function: This mathematical function is a linear transformation with a nonzero offset.

Aliasing: This effect occurs when a sampled signal appears to have a lower frequency than its true value, causing a misrepresentation of the original signal. More specifically, it occurs when a signal is sampled at a rate lower than twice of its highest frequency, as described by the Nyquist-Shannon sampling theorem.

Analog filter: These filters operate on continuous, unsampled signals. They are often constructed using physical components like resistors, capacitors and inductors. One example of an analog filter application is the anti-aliasing filter before the A/D converter.

Analog frequency response: This measure of how a dynamic system reproduces or processes signals of different frequencies can be calculated using the Laplace transform and can be presented in Bode plot.

Analog rotational speed sensing: This sensor produces a continuous, varying analog signal, such as a sine wave or a voltage proportional to speed, to measure the speed of a rotating object.

Analog signal: This continuously variable physical measurement, such as voltage or current, is used to represent real-world phenomena like sound or temperature, differing from digital signals that use discrete binary values.

Anti-aliasing filter (AAF): This analog filter is used before a signal sampler (such as A/D converter) to restrict the bandwidth of the sampled signal so that the Nyquist-Shannon sampling theorem is satisfied over the band of interest.

Anti-windup PID controller: This PID control mechanism is implemented in either a continuous- or discrete-time domain. It limits positive integration when the PID control output is saturated above and negative integration when the PID control output is saturated below.

Arduino IDE software: Arduino IDE (integrated development environment) is an open-source software application designed for writing, compiling, and uploading code to Arduino microcontroller boards. It serves as the primary interface for users to interact with Arduino hardware.

Arduino microcontroller: This microcontroller provides accessibility to beginners for building interactive electronic projects, allowing them to read inputs from sensors and control outputs like LEDs, motors, and other devices family.

Arduino MKR 1000 Wi-Fi: This microcontroller board is designed for internet of things (IoT) projects, featuring a built-in WiFi module for internet connectivity, an ARM Cortex-M0+ processor, a cryptographic chip for secure communications, and an onboard battery charger for easy portability.

Arduino MKR motor carrier board: This add-on board is designed to control a variety of motors (servo, DC, and stepper) and to connect other sensors. Compatible with the Arduino MKR family, it provides connections for four servo motors, four DC motors (two high-performance and two standard), two encoder sensors, and four analog sensors.

Arduino Simulink library: This selection of Simulink blocks is specifically designed for interacting with Arduino hardware. These blocks enable configuration and access to Arduino sensors, actuators, and communication interfaces (e.g., I2C, SPI, serial).

Asymptotical stability: Asymptotic stability refers to a property of a dynamical system where after being disturbed, the system will return to its equilibrium state as time approaches infinity.

Back electromotive force coefficient (\({\boldsymbol{K}}_{\boldsymbol{m}}\)): This constant relates a DC motor rotational speed to the back EMF (electromotive force) voltage generated, which is the voltage that opposes the armature voltage applied.

Band-kill (notch) filter: This filter attenuates signals with its frequency between the selected low and high cutoff (corner) frequencies. It passes signals with frequencies lower than the low cutoff frequency and higher than the high cutoff frequency.

Band-pass filter: This filter passes signals with its frequency between the selected low and high cutoff (corner) frequencies. It attenuates signals with frequencies lower than the low cutoff frequency and higher than the high cutoff frequency.

BIBO stability: It is a system property, where any finite and limited input to a dynamic system will result in a finite and limited output.

Bilinear transform: This method is useful for converting a continuous-time system (in the Laplace transform or \(s\)-domain) into a discrete-time system (in the \(z\)-domain) for applications in signal processing and control systems.

Black-box model: This system model is constructed based upon the system input and output data, assuming no system model architecture (structure).

Bode plot: It provides two frequency response graphs of a system transfer function, which are a combination of a Bode magnitude plot with magnitude in decibels and a Bode phase plot with phase in degrees. For both plots, the horizontal axis of angular frequency is in logarithm of base 10.

Bounded-input and bounded-output (BIBO) stability: In this system property, any finite and limited input to a dynamic system will result in a finite and limited output.

Butterworth filter: This type of signal processing filter (analog or digital) is designed to have a frequency response as flat as possible in the passband. It is also referred to as a maximally flat magnitude filter.

Capacitance-based acceleration sensor: This accelerometer employs the changing capacitance of a moving mass to measure dynamic changes in mechanical variables.

Chebyshev filter: This filter, which is either analog or digital, has a steeper roll-off frequency response than the Butterworth filter and has either a passband ripple (type I) or a stopband ripple (type II). A Chebyshev filter minimizes the error between the idealized and the actual filter characteristic over the operating frequency range of the filter.

Closed-loop control: It uses feedback signals to automatically adjust system inputs to maintain desired output by continuously monitoring and comparing actual outputs against set points.

Complementary filter: This filter combines data from two different sensors to develop a more accurate estimate of a system state, such as orientation.

Continuous-time PID controller: This PID control mechanism is implemented in the continuous-time domain. That is, both integration and derivative calculations are in the continuous-time domain.

Control system architecture: This structure comprises the physical hardware components and the software that organizes and controls them. The hardware components include sensors, microcontrollers, and actuators. The software architecture defines the overall structure.

Coordinate transformation: It is the process of converting coordinates from one reference system to another, which is essential for comparing or aligning different frames of reference.

Coriolis force: This inertia (or fictitious) force applies to objects in motion within a reference coordinate (frame) that rotates with respect to the inertia coordinate.

CPU: Short for central processing unit, this electronic brain of a computer is responsible for executing instructions, performing calculations, and managing most system operations.

Coulomb friction model: This model of dry friction states that friction force is directly proportional to the normal force pressing two surfaces together and is independent of the contact area.

Current source: This electrical circuit provides a constant electric current to other circuits, regardless of the voltage across its terminals.

D/A converter: This electronic device transforms a digital number in the information domain to an analog voltage signal to be used in the physical domain.

DC-AC converter: This electronic device converts direct current (DC) power into alternating current (AC) power. It uses electronic switches to rapidly switch the DC power on and off, reversing the polarity to create an AC waveform.

DC motor: It is an electrical machine that converts direct current (DC) electrical energy into mechanical energy, using magnetic forces to produce rotational motion.

DC-motor torque coefficient (\({\boldsymbol{K}}_τ\)): This constant value is expressed in Newton-meter per Ampere (Nm/A), which represents the relationship between the DC motor armature current and the produced torque (produced torque is equal to the product of \(K_τ\) and armature current).

DC-motor transfer function: This system transfer function has DC motor armature voltage as input and motor speed or displacement as output.

Detailed system model: Also called a digital twin, this dynamic, virtual replica of a mechatronic system is created and validated using real-time data from sensors and other devices.

Digital filter: In digital signal processing (DSP), digital filter is an algorithm that mathematically manipulates sampled, discrete-time signals to achieve desired characteristics, such as separating signals or restoring a signal.

Digital signal: This signal represents data as a sequence of discrete values. At any given time, it can take on, at most, only one of a finite number of values. The signal contrasts with an analog signal, which represents continuous values.

Direct PID gain tuning: This PID control gain turning method is for systems that can be stabilized with a proportional (P) gain. The turning starts with P gain by pushing the closed-loop system to marginally stable as gain increases and then set the P gain around half of the marginally stable one. We then fix the P gain and tune the integral (I) gain in the same way as the P gain; and last fix both P and I gains, tuning the derivative gain in the same way as the P and I gains.

Direct torque control: This advanced AC motor control technique directly regulates the motor’s electromagnetic torque and magnetic flux, rather than indirectly controlling motor currents.

Discrete Fourier transform (DFT): This transform is a discrete version of the continuous Fourier transform. It converts a finite sequence of equally spaced samples of a function into a same-length sequence of equally spaced samples of the discrete Fourier transform, a complex-valued function of frequency.

Discrete solenoid linear actuator: This linear actuator is powered by one or more solenoids with two distinguished states: rest and activated.

Discrete state actuator: This device can be set to a specific, limited number of positions or states, unlike a continuous actuator that can move to any position within a range.

Discrete-time PID controller: This PID control mechanism is implemented in the discrete-time domain. That is, both integration and derivative calculations are in the discrete-time domain.

Dual-loop model-based control strategy: This model-based control architecture has one continuous-time inner-loop controller and one discrete-time outer loop controller.

Dual-PID control of a three-wheel cart: This three-wheel cart control strategy uses two PID controllers. One regulates the right wheel speed to track its reference and the other controls the left wheel speed. The cart forward driving and turning motion can be achieved by adjusting the reference speeds of the two driven wheels independently.

Dual-PID mini-Segway control: This mini-Segway control strategy uses two PID controllers. One stabilizes the pendulum and maintains it at an upright position. The other drives the mini-Segway.

Dual-PID strategy validation: The real-time Simulink model of a dual-PID control strategy is implemented and validated experimentally for the three-wheel cart.

EHVA: This electro-hydraulic magnetic valve actuation (EHVA) is used in internal combustion engines to actuate intake and exhaust valves.

Electric/electronic circuit modeling: It is the process of creating a virtual representation of a circuit for analyzing and simulating its behavior before building a physical prototype.

EMVA: Short for electromagnetic valve actuation, EMVA is used in internal combustion engines to actuate intake and exhaust valves.

EMVA equivalent circuit: This circuit is a model for the electromagnetic valve actuator in an internal combustion engine

Equation of motion (EOM): This mathematical function describes how the position, velocity, and acceleration of a physical system change over time.

Fast Fourier transform (FFT): This highly efficient algorithm analyzes complex signals by breaking them down into their constituent frequencies. It transforms time-domain data into a frequency-domain representation, enabling quick identification of signal components, such as those in audio signals or vibrations. FFT is crucial in many fields, including signal processing, engineering, music, and science, due to its fast transform speed advantage over the standard discrete Fourier transform (DFT).

Feasible conversion region: An NMP continuous-time system can be converted into a MP discrete-time system using a special sample and hold input. The feasible conversion region is the one with the selected sample period and duty-cycle leading to a MP discrete-time system.

Field-oriented control: This method for controlling AC motors mathematically separates the motor stator current into two orthogonal components: one for magnetic flux (\(d\)-axis) and one for torque (\(q\)-axis).

Finite element analysis: Continuous mechanical system modeling.

First order system: The behavior of this dynamical system can be described by a first order ordinary differential equation (ODE). The system is characterized by a single energy storage element, resulting in a transfer function with one pole defined by its time constant.

Fixed-point: In computing, fixed point is a method of representing fractional (non-integer) numbers by storing a fixed number of digits of their fractional part.

Floating-point: This method represents non-integer, decimal values in a computer, similar to scientific notation. Computers use the IEEE 754 standard to store these numbers, dividing the available bits into a sign, an exponent, and a mantissa, which determines magnitude and precision of the number.

Flow sensor: This device measures the rate or quantity of a liquid or gas moving through a pipe or duct.

FM: The frequency modulated (FM) signal has fixed duty-cycle with varying frequency. The single can be in the form of varying frequency sinusoidal or square wave signals.

Gain margin: It measures the absolute stability of a control system, indicating how much extra gain can be added before the system becomes unstable. It can be determined at the frequency where the phase shift reaches \({-180}^{{}^\circ }\), and a higher gain margin (expressed in dB) signifies a more stable system.

Gain- scheduled PID control: This strategy adjusts the proportional (P), integral (I), and derivative (D) gains of a PID controller based on the current operating conditions of a dynamic system.

Generic algorithm (GA): This optimization technique mimics natural selection to find solutions to complex problems, or to a generic programming concept where algorithms are written to be flexible and reusable across various data types.

Gray-box model: This system model is constructed based upon the system input and output data with a given system model architecture (structure).

H-bridge: This actuator drive circuit is capable of driving an actuator (such as a DC motor) in two directions.

Hall-effect sensor: This magnetic sensor can be used for detecting the strength and direction of a magnetic field. The field might be produced from either a permanent magnet or an electromagnet with its output varying in proportion to the strength of the magnetic field being detected. Hall-effect sensors are solid state devices. They are becoming increasingly popular because they can be used in many different types of applications such as sensing position, velocity, and directional movement.

High-pass filter: This filter passes signals with a frequency higher than a selected cutoff (corner) frequency and attenuates signals with frequencies lower than the cutoff frequency.

High-side driver circuit: This circuit controls a power transistor by connecting the load between the transistor drain or collector and the ground with the transistor source or emitter connected to a power supply.

Hardware-in-the-Loop (HiL) simulation: This technique validates mechatronic control systems by connecting real controller hardware to a virtual, real-time simulated environment. It accelerates development and improves safety by enabling testing of control algorithms without needing the full, physical system.

IGBT: Short for insulated-gate bipolar transistor, this is a type of electronic switch used in power electronics. It combines the high input impedance and fast switching of a MOSFET with the high current-handling capability and low saturation voltage of a bipolar junction transistor.

Image/video-based sensor: This device detects and converts light waves or other electromagnetic radiation into electrical signals to form a digital image or video sequence in the information domain.

Impedance: It is the effective resistance of an electric circuit or component to alternating current due to the combined effects of ohmic resistance and reactance, such as inductance and capacitance.

Impulse response: It is the system output with an impulse function as input.

Incremental encoder: This linear or rotary electromechanical device has two output signals, A and B, that issue pulses when the device is moved.

Induction motor: This electric motor uses electromagnetic induction to create rotation, making it the most common type of electric motor used in industrial and household applications.

Inertia-based acceleration sensor: This accelerometer measures the linear acceleration of an object by detecting the force of inertia on a tiny, suspended mass.

Information and physical domain interfaces: These interfaces connect the digital information domain and the analog physical domain. For example, signal conditioning circuits (such as anti-aliasing filters) connect from physical to information domains whereas power amplifiers connect from information to physical domains.

Infrared (IR) camera: Also called a thermal camera, this device detects and captures images of infrared radiation emitted by objects, which is invisible to the human eye.

Infrared (IR) image: This visual representation is created by capturing infrared radiation, which is a form of electromagnetic radiation invisible to the human eye.

Inverse Laplace transform: This integral transform converts a frequency-domain function into a function of a real variable in time domain.

Kalman filter: This filter is an optimal, recursive algorithm that estimates the state of a dynamic system (e.g., position, velocity) by combining noisy sensor measurements with a mathematical model, providing more accurate results than using data source alone.

Lane detection: This is computer vision process identifies and localizes lane markings on a road, typically for autonomous or semi-autonomous driving systems.

Laplace transform: It is an integral transform converting a time-domain function into a function of a complex variable, denoted by “\(s\)”, representing frequency or the Laplace domain.

Least Squares (LS): This statistical and mathematical method is used to find the best-fit model (often a line or curve) for a given set of data by minimizing the sum of the squared differences (residuals) between the observed data points and the values predicted by the model.

Lenz Law: If a wire cuts through a magnetic field, it experiences a force that is in the opposite direction to its velocity, which is an example of Lenz Law. That is, the direction of the induced EMF opposes the change that caused it.

Linear actuator: This device converts energy into linear motion to push, pull, lift, or tilt a load. It is often realized by transforming rotational energy from an electric motor into linear motion via components like lead screws and nuts, by using direct electric-magnetic actuation, or by directly using fluid pressure in hydraulic or pneumatic cylinders.

Linear system model: It is a mathematical representation of a dynamic system with its output as a dynamic linear function of its input, meaning it obeys the principles of superposition (the response to a sum of inputs is the sum of the responses to each input) and homogeneity/scaling (scaling the input by a constant also scales the output by the same constant).

Lorentz force law: This law describes the total electromagnetic force on a charged particle as the sum of electric and magnetic forces.

Low-pass filter: This filter passes signals with a frequency lower than a selected cutoff (corner) frequency and attenuates signals with frequencies higher than the cutoff frequency.

Low-side driver circuit: This circuit controls a power transistor by connecting the load between the power supply and the transistor drain or collector with the transistor source or emitter connected to the ground.

LPV model: Short for linear parameter-varying model, this model’s dynamics vary as a function of certain time-varying parameters called scheduling parameters. An LPV model is often represented in a state-space form with independent model coefficients (parameters).

LQG control: LQG is an acronym for linear quadratic Gaussian. LQG control is a modern state-space control design technique to obtain optimal dynamic regulators and servo controllers. This technique allows us to trade off regulation performance and control effort, considering disturbances and measurement noise.

LQR: LQR is an acronym for linear quadratic regulation, which is a model-based design method for controllers and regulators. It minimizes a weighted quadratic summation of performance and control effort.

LQR mini-Segway pendulum stabilization: This mini-Segway pendulum position regulation control utilizes the LQR (linear quadratic regulation) control technique.

LQR SiL simulation: This SiL simulation in Simulink for the mini-Segway utilizes the LQR state feedback controller with linearized model.

LQR control of three-wheel cart: This three-wheel cart control strategy regulates any initial cart speed to its rest position.

LQTI control of three-wheel cart: This three-wheel cart control strategy tracks both cart and turning speed references so that the cart can be driven at wish.

LQTI control strategy implementation: This LQTI control strategy is realized by replacing the matrix-vector multiplication with entry-by-entry multiplication.

LQTI mini-Segway control: This mini-Segway control strategy stabilizes its pendulum and tracks the desired mini-Segway speed simultaneously, utilizing LQTI (linear quadratic tracking with integration) control technique.

LQTI SiL simulation: This SiL simulation in Simulink for the mini-Segway utilizes the LQTI state feedback controller with a linearized model.

LQTI SiL simulation with Coulomb friction: This SiL simulation in Simulink for the mini-Segway utilizes the LQTI state feedback controller based on a nonlinear model with Coulomb friction.

LQTI strategy with feedforward control: This LQTI strategy combines both open-loop feedforward control with the closed-loop LQTI control to generate control signals for both DC motors in the three-wheel cart.

Mass-damper-spring system: This mechanical system consists of discrete mass nodes interconnected via a network of springs and dampers.

MATLAB and Simulink: These are a pair of software environments from MathWorks used for technical computing and model-based control design.

Mechatronic actuator: A mechatronic actuator is a device that generates force, motion, heat, or other forms of effects based upon a control command produced in the information domain. It utilizes the supplied electric, hydraulic, or other forms of energies.

Mechatronic sensor: A mechatronic sensor is a device that converts physical variables into electronic signals to be input to microcontrollers of a mechatronic system.

Mechatronic system: It consists of mechanical, electrical/electronic, and mechatronic subsystems. A microcontroller (or multiple microcontrollers) in the information domain is often used to collect the sensor and reference signals from a mechatronic system. It then makes decisions based upon the collected signals inside the microcontroller, converts these decisions into actuator commands, and sends them to the related actuators for execution in a closed-loop manner.

Mechatronic system model: It is a mathematical representation of a given mechatronic system using differential equations or frequency domain transfer functions.

MEMS: An acronym for micro-electro-mechanical systems, MEMS is a class of devices that integrates microscopic mechanical components with microelectronic circuitry on a single silicon chip. They produce micrometer-scaled sensors, actuators, and other tools that are too small to be seen with the naked eye.

MEMS architecture: This three-dimensional structure integrates micromechanical components like sensors and actuators with electronic circuits on a single silicon chip.

Microcontroller: This compact, self-contained integrated circuit (IC) functions as a miniature computer on a single chip, housing a central processing unit, memory, and input/output peripherals designed to control specific functions of an electronic device or embedded system.

Microcontroller architecture: This internal design of a microcontroller includes a CPU, memory (RAM and ROM), and input/output (I/O) ports, all integrated onto a single chip.

Microprocessor unit (MPU): This single-chip computer contains the CPU’s core components, such as an arithmetic and logic unit (ALU), control unit, and registers.

Minimal phase (MP) system: This linear system has all its zeroes located at the open-left half complex plane.

Mini-Segway: It is a two-wheeled, self-balancing robot (mechatronic system) that is a miniaturized version of the original Segway human transporter.

Mini-Segway equations of motion: This set of differential equations describe the mini-Segway dynamic and motion.

Mini-Segway hardware architecture: The mini-Segway consists of two geared motors with wheel attached, an Arduino MKR 1000 microcontroller with MKR motor carrier board attached, and the three-axis accelerometer and rate gyro.

Mini-Segway model: It describes the mini-Segway model dynamics with differential equations for the two-wheeled and self-balancing robot.

Mini-Segway software architecture: The mini-Segway software is implemented in a MATLAB and Simulink environment consisting of sensor reading and actuator driving software, dual PID controllers, and a user interface for driving the mini-Segway.

Model-based control gain tuning: This approach adopts a process of designing a sequence of closed-loop controllers with gain low to high and then evaluating them with control gain from low-to-high to find the best closed-loop performance.

MOSFET: Short for metal oxide semiconductor field-effect transistor, this semiconductor device is used for switching. It acts as a voltage-controlled switch with the voltage applied to the gate terminal controlling the current flow between the drain and the source terminals.

Moving average (MA) filter: This filter averages a defined number of consecutive data points to remove high-frequency noise and smooth a signal, acting as a low-pass filter.

MPU 6050: This IMU (inertia measurement unit) integrates both three-axis accelerometers and three-axis gyroscopes on a single chip.

No-load speed: This measure refers to the maximum speed that can be achieved by a DC motor without any load torque applied.

Nonlinear system model: This model describes a relationship with its dynamic output not directly proportional to the input. Thus, it does not follow the principles of superposition and homogeneity/scaling.

Nonlinear three-wheel cart model for SiL simulation: This nonlinear model of three-wheel cart system is developed for SiL simulations with all feedback signals required for dual-PID, triple-PID, and LQTI control strategies.

Non-minimal phase (NMP) system: This linear system has at least one zero located at the open-right half complex plane.

NSGA-II (Non-dominated Sorting Genetic Algorithm II): This popular, elitist algorithm solves multi-objective optimization problems.

Nyquist–Shannon sample theorem: This theorem is an essential principle for digital signal processing; it links the frequency range of a signal and the sample rate required to avoid a type of distortion called aliasing. The theorem states that the sample rate must be more than twice the bandwidth of the signal in order to avoid aliasing. In practice, it is used to select band-limiting filters that keep aliasing below an acceptable amount when an analog signal is sampled.

OCC: Short for output covariance constraint, this optimal control design method minimizes the quadratic control effort subject to multiple quadratic output constraints.

Open-loop control: In this non-feedback control method, the control action is independent of the resulting process output. That is, the control does not use output feedback to adjust its operation.

Operational amplifier (OA): A high-gain voltage amplifier with a differential input and a single output, it is designed to perform mathematical operations in analog circuits.

Operational amplifier circuit modeling: This device measures angular velocity (rotation rate) by using the Sagnac effect to analyze the difference in travel time between two counter-propagating beams of light.

Optical rate gyro sensor: It is a device that measures angular velocity (rotation rate) by using the Sagnac effect to analyze the difference in travel time between two counter-propagating beams of light.

Partial fractional expansion (PFE): This method for decomposing a rational function into a sum of simpler terms makes it easier to find the inverse Laplace transform and determine the time-domain response.

Percentage overshoot (PO): For a step input, the percentage overshoot is a measure of how much a system response exceeds its final steady-state value, expressed as a percentage of the final value. It is calculated as the difference between the maximum peak value and the final value, divided by the final value, then multiplied by \(100\%\).

Permanent-magnet synchronous motor (PMSM): This motor uses permanent magnets embedded in the rotor to create a constant magnetic field. The stator carries windings connected to an AC electricity supply to produce a rotating magnetic field (as in an asynchronous motor). Note that the AC supply can also be created using a power inverter with DC power supply.

Physics-based sensor: This device detects and measures a physical quantity by applying the principles of physics to convert a stimulus, like heat or motion, into a measurable electronic signal.

PID (proportional-integral-derivative) control: This control loop mechanism continuously calculates an error value and applies a correction based on three weighted terms: proportional (P), integral (I), and derivative (D). The mechanism then brings system output to a desired set point and maintains its stability.

PID DC motor speed control: This control utilizes a feedback mechanism based on the difference between a desired motor speed and the actual speed. It continuously adjusts the armature voltage of a DC motor via a PID control signal.

Piezoelectric accelerometer: This sensor measures dynamic acceleration and vibration by utilizing the piezoelectric effect, where materials like quartz or ceramics generate an electric charge under mechanical stress.

Pole assignment: This method, which is used in control system theory, places the closed-loop poles of a dynamic system modeled in its state-space form in predetermined locations in the complex plane.

Pole location: In control systems, it determines the system stability and performance, with locations in the left half of the complex plane indicating stability.

Pressure sensor: This device measures the force of a gas or liquid and converts it into an electrical signal.

PWM: An acronym for pulse-width modulated, the PWM signal is often used as a control signal for DC motors to reduce the energy loss in the power amplifier.

\(\boldsymbol{q}\)-Markov Cover: Short for \(q\)-Markov covariance equivalent realization, this system identification method is based upon system responses associated with either white noise, impulse, or PRBS inputs.

Quantization: In mathematics and digital signal processing, this process maps input values from a large set (often a continuous set) to output values in a (countable or digital) smaller set, often with a finite number of elements.

Rate gyro: This type of gyroscope measures the angular velocity (or angular rate) of an object, rather than its direction.

Rogowski coil: This toroid of wire measures an alternating current \(i(t)\) through a cable encircled by the toroid.

Routh-Hurwitz criterion: It is a mathematical test in control system that determines if a linear time-invariant (LTI) system is stable by examining the coefficients of its characteristic equation without solving for the roots.

Sample and hold: This circuit captures an analog voltage signal at a specific instant and holds it at that constant level until it is triggered to sample again.

Second order system: The behavior of this dynamical system can be described by a second order ordinary differential equation. The system is characterized by a second-order transfer function with two poles defined by undamped natural frequency and damping ratio.

Sensor fusion: This process combines data from multiple sensors to produce more accurate, reliable, and comprehensive information than any single sensor alone.

Settling time: For a unit step input, the system settling time (\(t_s)\) is the time for the system to reach its given error band of \(2\%\) (or \(5\%\)), where the error band is defined as the range between \(1+2\%\) (or \(5\%\)) and \(1-2\%\) (or \(5\%\)). Note that if \(2\%\) (or \(5\%\)) is used, it is called \(2\%\) (or \(5\%\)) settling time.

Signal-to-noise ratio (SNR): This ratio measures the strength of a desired signal compared to the strength of background noise, indicating signal quality. A higher SNR signifies a stronger signal relative to noise, leading to a clearer output.

SiL simulation: Short for software-in-the-loop simulation, this method tests and validates control software with a physical system model in a simulation environment, in order to quickly catch control software bugs and evaluate closed-loop system performance.

Simulation validation in Simulink This SiL simulation technique is implemented in MATLAB and Simulink software platform.

Sinusoidal response: This response is the system output with a sinusoidal function as input.

Slip-angle model: Slip angle is the angle between the direction a tire is pointed and the direction it is traveling. An empirical model was developed in this book.

Special sample and hold input: Different from the conventional zero-order sample and hold input, the special sample and hold input uses a special function, such as square pulse, forward and backward triangles, over the sample period.

Speed calculation using encoder signals: Speed is calculated from encoder signals first by measuring pulses over a set time or by measuring the time between pulses, and then using the encoder’s pulses per revolution (PPR) to convert the data to speed.

Stall torque: This measure is the maximum rotational torque a motor produces when its shaft is completely stopped or locked, meaning that its shaft speed is zero.

State-space model of mini-Segway: This mini-Segway model utilizes a set of first-order differential or difference equations that represent its internal state and its relationship to observable outputs.

State-space model of three-wheel cart: This model linearizes the nonlinear three-wheel cart model at its operational condition without any lateral-slip.

Step motor: Also known as a stepper motor or a stepping motor, brushless DC electric motor rotates in a series of small and discrete angular steps.

Step response: It is the system output with a step function as input.

Step response performance indices: These performance indices include system response time, settling time, system overshoot, system steady state error, etc.

System model linearization: In mathematics, linearizing a nonlinear model is finding a linear model that approximates the nonlinear one at a given operational condition.

Taylor expansion: Also called Taylor series, this expansion mathematically expresses a given function as an infinite sum of terms relative to the function’s derivatives at a given point (called the equilibrium point).

Temperature sensor: This device measures temperature by converting thermal energy into an electrical signal.

Three-wheel cart: A three-wheel cart system has two driving wheels in the front and one swivel caster wheel in the back.

Three-wheel cart model: This dynamic model of the three-wheel cart system is developed based upon Newton’s law, considering wheel lateral slip.

Three-wheel cart SiL simulation: This SiL simulation environment is used to study the three-wheel cart performance for given driving and turning references utilizing dual-PID, triple-PID, and LQTI control strategies.

Time constant: This constant is a fundamental measure of how quickly a system responds to a change (e.g., unit step input), taking the time of one time constant to reach 63.2% of its new steady state value.

Torque ripple: This ripple is the periodic fluctuation in the output torque of an electric motor, causing unsmooth rotation.

Transfer function: This Laplace transform is a mathematical model representing input-output relationship of a linear system as a ratio of polynomials, simplifying complex differential equations into algebraic ones for easier analysis, design, and stability checks in control systems and electronics.

Triple-PID control of a three-wheel cart: This three-wheel cart control strategy uses three PID controllers. The first and second PID controllers regulate the left and right driven wheel speeds to track their references, respectively, and the third PID controller tracks the cart turning speed reference.

Triple-PID strategy validation: The real-time Simulink model of triple-PID control strategy is implemented and validated experimentally for the three-wheel cart.

V-design process: This framework for mechatronic systems combines hardware and software development. Each development phase is paired with either simulation or experimental validation to ensure early identification of design frauds. An iterative process is also embedded in the V-design process to improve system performance.

Vibration-based gyroscope (sensor): This Coriolis vibratory gyroscope uses a vibrating structure (as opposed to rotating) as its orientation reference.

\(\boldsymbol{z}\)-transform: It is a mathematical tool converting a discrete-time signal or system from its time-domain representation into a complex frequency-domain representation called the \(z\)-domain. The discrete-time equivalent of the Laplace transform, it is used in digital signal processing (DSP) and control theory to analyze and design digital systems by simplifying difference equations into algebraic ones.

Ziegler-Nichols method: It is a popular, pragmatic approach for tuning PID controller gains. Note that a PID controller is a type of industrial control system used to maintain desired process variables.