control system architecture usually consists of the integration of the following functional units: a data processing and computing unit, an electrical-driven actuating unit, a measuring and detecting unit, a data acquisition (DAQ) and transmitting unit and a signal conditioning unit. The data processing and computing unit can be implemented through devices such as microcontroller (μC), programmable logic controller (PLC) with a control function, digital signal processing (DSP)a and a field-programmable gate array (FPGA).
The design of efficient control systems requires the mathematical modeling of mechatronic systems and process dynamics. This can be achieved in accordance with the operating characteristics (discrete and continuous) and objectives as well as technological constraints of the related instrumentation (signal conversion, transmission, conditioning, measurement, actuation etc.). However, in most of the current engineering literature on the design of digital control systems, the mathematical foundation of discrete time and discrete event systems is usually presented separately from the technological constraints of control instrumentation. For example, the operating time delay models or signal to noise ratio from digital device interfaces are not usually considered. Hence, the theoretical control algorithms proposed have limited practical applicability.
Challenges in the development of a practical design approach for the control of mechatronic systems and electrical-driven processes are: (i) to size and select control instrumentation in accordance with controlled system design objectives; (ii) to develop accordingly the mathematical discrete hybrid model capturing their continuous and discrete event behavioristic characteristics and (iii) to integrate the control systems with respect to technological constraints and operational characterization (discrete and continuous) (e.g. time delays, signal to noise ratios etc.).
This book intends to revisit the design concept for the control of mechatronic systems and electrical-driven processes along with the selection of control instrumentation. By reviewing the theory on discrete-time and discrete event systems as well as various elements of control instrumentation, it offers an integrated approach for: (i) the modeling and the analysis of mechatronic systems dynamics and electrical-driven process operations; (ii) the selection of actuating, sensing and conversion devices and (iii) the design of various controllers for single to multiple function electrical-driven products (mechatronic systems) and processes. Furthermore, it covers some design applications from several engineering disciplines (mechanical, manufacturing, chemical, electrical, computer, biomedical) through real-life digital control system design problems (e.g. a driverless vehicle, newborn incubator, elevator motion) and industrial process control case studies (e.g. a power grid, wind generator, crude oil distillation, brewery bottle filling, beer fermentation).
Through this book, the reader should gain methods for: (i) model formulation, analysis and auditing of single to multiple function electrical-driven products and processes; (ii) model-driven design of software and hardware required for digital control instrumentation; (iii) sizing and selection of electrical-driven actuating systems (including electric motors) along with their commonly used electro-transmission elements and binary actuators; (iv) selection and calibration of devices for process variable measurement and computer interfaces and (v) modeling, operating and integrating a wide variety of sensors and actuators. Hence, the textbook is organized into eight chapters.
1 Introduction to control of mechatronic systems. Chapter 1 gives a brief conceptual definition and classification of mechatronic systems, electrical-driven technical processes and control systems structure. Here, a functional decomposition of the generic control system architecture is presented along with some examples to illustrate control instrumentation for sensing, actuating, computing, signal converting and conditioning. Furthermore, typical functions of generic controlled system for electromechanical product and processes are described along with the interconnection between the control instrumentation. Generic requirements for control systems design are outlined based on challenges to software-based control system integration (design of hybrid architecture) and hardware-based control system integration (instrumentation sizing, compliance and selection). This is summarized within a list of major steps of control design projects.
2 Physics-based system and process dynamics modeling. Chapter 2 presents numerous examples of dynamics modeling for various electrical-driven systems and processes including transportation systems (e.g. a sea port gantry crane, hybrid vehicle, Segway, elevator, driverless car), production systems and processes (e.g. an energy-based wind turbine, drilling machine, cement based pozzolana scratcher), chemical processes (e.g. oil distillation, cake conveyor oven, city water treatment, fermentation, poultry scalding and defeathering), fluidic and thermal systems and processes (mixing tank, purified water distribution, conveyor oven, poultry scalding and defeathering thermal process) or biomedical systems (e.g. infant incubator, human blood glucose insulin metabolism). Systems and process behaviors can be captured through differential equations using an experimental data modeling approach and classical physical laws of conservation and continuity. The resulting models are capable of displaying multiple and nonlinear variables as well as time variant parameter characteristics that can further be simplified according to the system physical properties or operating boundaries. A methodology for physics-based modeling is presented through the deterministic or stochastic behavior models of commonly encountered electrical-driven systems and large-scale processes. A review on linear modeling methods such as stochastic, dynamics response or state space is presented in the Appendices.
3 Discrete time system modeling and signal conversion methods. Chapter 3 focuses on methods to derive discrete approximation of continuous systems and signals using tools, such as the hold equivalent, pole-zero mapping, numerical integration and z-transformation theorems. A technological description of computer control architecture and interface is proposed with respect to DAQ unit operations from the bus structure to data gathering, logging and processing with respect to signal noise reduction and approximation consideration. Critical issues related to signal conversion, such as aliasing effects, along with the methodology for selection of sample period are also covered. A selection methodology of the sample period is also outlined. Overall, the chapter topics include technology and methods for continuous signal digital conversion and reconstruction such as bilinear transformation, discrete-time command sequence generation, computer control interface for data logging, conditioning and processing, sample time selection and computer conversion technology using various conversion techniques (i.e. successive approximation, dual slope ADC, delta-encoded ADC, etc.), as well as processing delay effects.
4 Discrete time analysis methods. Chapter 4 presents methods related to discrete system dynamical analysis in the frequency and time domains. Moreover, stability definition and tests for discrete time system are discussed and controlled system performance assessment tools are outlined. This chapter aims to present discrete controller design specifications. Chapter topics include frequency analysis tools such as (DTFT, FFT, DFT), discrete zero and pole location plots, stability tests and criterion for discrete time systems (Jury–Marden test, Routh–Hurwitz), steady-state error, performance indices (ITAE, ISE), time and frequency properties for controller design (settling time, percentage overshoot, gain and phase margins).
5 Continuous digital controller design. Chapter 5 presents various approaches to design the PID controller algorithms, such as continuous time design, discrete design and direct design using roots-locus, and frequency response techniques as well as some advanced techniques, such as model predictive control. Hence, using time or frequency domain controller specifications, numerous examples of designing and tuning control algorithms are described ranging from PID family, deadbeat, feedforward and cascade, to non-interacting control algorithms. In addition to stability analysis tests, performance indices and dynamics response analysis are derived in frequency and time domains. Furthermore, the open loop controller design for stepper motors as well as scalar and vector control design for induction motors are described. Model predictive control algorithms suitable for process operations with physical, safety and performance constraints are also presented. Eventually, comparative analyses between classical PID controllers with various