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Archipp Konovalov
Archipp Konovalov

Learn Applied Control Theory for Embedded Systems with Practical Examples and Exercises



Applied Control Theory for Embedded Systems: A Practical Guide for Engineers




Are you an embedded engineer or programmer who needs to implement basic process or motion control as part of your product design? Do you want to learn how to apply control theory to your embedded systems without getting lost in heavy math and complicated theory? If so, this article is for you.




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In this article, you'll learn about applied control theory for embedded systems, a topic that covers the design, implementation, testing and tuning of control systems for embedded applications. You'll also learn how to download a book that provides a comprehensive and practical introduction to this topic, written by an expert in the field.


By reading this article and the book, you'll be able to:


  • Understand the basics of control theory and how it relates to embedded systems



  • Design and implement control systems for embedded systems using various methods and tools



  • Test and tune your control systems for optimal performance and stability



  • Explore advanced topics in control theory such as adaptive control, robust control, nonlinear control and fuzzy logic control



  • Enhance your skills and knowledge in embedded control system design



What is Control Theory and Why is it Important for Embedded Systems?




Control theory is a branch of engineering and mathematics that deals with the analysis and design of systems that perform tasks with minimum effort or error. A system that performs such tasks is called a control system.


A control system consists of three main components:


  • A plant, which is the physical system or process that needs to be controlled



  • A controller, which is a device or algorithm that generates commands or signals to influence the plant



  • A sensor, which is a device that measures the output or state of the plant and provides feedback to the controller



The goal of a control system is to make the plant output or state follow a desired reference, which is a signal or value that specifies what the plant should do. For example, a reference could be a desired temperature, speed, position or angle.


Control systems are ubiquitous in engineering and technology. Some examples of control systems are:


  • A thermostat that regulates the temperature of a room by turning on or off a heater or an air conditioner



  • A cruise control that maintains a constant speed of a car by adjusting the throttle or brake



  • A robotic arm that moves to a desired position by controlling the torque of its joints



  • A camera that focuses on a subject by adjusting the lens aperture or zoom



Control systems are especially important for embedded systems, which are systems that have a dedicated function within a larger system or device. Embedded systems often have limited resources such as memory, processing power, battery life and communication bandwidth. Therefore, they need to perform their tasks efficiently and reliably, while coping with uncertainties and disturbances in their environment.


By applying control theory to embedded systems, you can:


  • Improve the performance, robustness and reliability of your embedded systems



  • Reduce the cost, size and power consumption of your embedded systems



  • Enhance the functionality and usability of your embedded systems



  • Solve complex and challenging problems in embedded system design



The Basics of Control Theory for Embedded Systems




To apply control theory to embedded systems, you need to understand some basic concepts and methods that are commonly used in control system analysis and design. In this section, we'll introduce some of these concepts and methods, such as z transforms, frequency response analysis, stability and performance criteria.


Z Transforms




A z transform is a mathematical tool that converts a sequence of discrete-time values into a function of a complex variable. A z transform is useful for solving linear, shift-invariant difference equations, which are equations that describe the behavior of many discrete-time systems.


A z transform has the following form:


where x[n] is the discrete-time sequence, X(z) is the z transform function, and z is the complex variable.


A z transform has some important properties that make it easy to manipulate and analyze discrete-time systems. Some of these properties are:


  • Delay property: If x[n] is delayed by k samples, then its z transform is multiplied by z. That is,



  • Linearity property: If x[n] and y[n] are two sequences with z transforms X(z) and Y(z), respectively, and a and b are two constants, then the z transform of their linear combination is the linear combination of their z transforms. That is,



  • First difference property: If x[n] is a sequence with z transform X(z), then the z transform of its first difference x[n]-x[n-1] is given by



  • Summation property: If x[n] is a sequence with z transform X(z), then the z transform of its summation is given by



  • Final value theorem: If x[n] is a sequence with z transform X(z) and x[n] approaches a finite value x∞ as n goes to infinity, then



  • Initial value theorem: If x[n] is a sequence with z transform X(z) and x[0] is finite, then



A z transform can be inverted to obtain the original sequence from the z transform function. This can be done by using various methods such as partial fraction expansion, long division, residue theorem or lookup tables.


Frequency Response Analysis




Frequency response analysis is a method for studying the behavior of a system in response to sinusoidal inputs. A sinusoidal input is a signal that has a constant amplitude and frequency, and can be represented by


where A is the amplitude, is the angular frequency and is the phase.


A system that is linear and shift-invariant will produce a sinusoidal output when given a sinusoidal input. The output will have the same frequency as the input, but may have a different amplitude and phase. The ratio of the output amplitude to the input amplitude is called the gain, and the difference between the output phase and the input phase is called the phase shift.


The gain and phase shift of a system depend on the frequency of the input. The function that relates the gain and phase shift to the frequency is called the frequency response. The frequency response can be expressed in various forms, such as Bode plots, Nyquist plots or polar plots.


Frequency response analysis can be used to determine the characteristics of a system, such as its bandwidth, resonance, stability and performance. Frequency response analysis can also be used to design controllers that achieve desired specifications in terms of gain and phase margins.


Stability and Performance Criteria




Stability and performance are two important criteria for evaluating and designing control systems. Stability refers to the ability of a system to maintain its output or state within acceptable limits when subjected to disturbances or changes in reference. Performance refers to the ability of a system to achieve its desired output or state quickly and accurately.


There are different ways to define and measure stability and performance for control systems. Some common methods are:


  • Pole-zero analysis: This method uses the locations of the poles and zeros of a system's transfer function to determine its stability and performance. A pole is a value of z that makes the transfer function infinite, and a zero is a value of z that makes the transfer function zero. A system is stable if all its poles are inside the unit circle in the z-plane, and unstable if any pole is outside or on the unit circle. A system's performance depends on how close its poles are to the unit circle and how far its zeros are from the unit circle.



  • Root locus analysis: This method uses a graphical technique to plot the trajectories of the poles of a closed-loop system as a parameter (such as controller gain) varies from zero to infinity. The root locus plot can be used to determine how the stability and performance of a system change with different values of the parameter.



  • Routh-Hurwitz criterion: This method uses an algebraic technique to determine whether all the roots of a polynomial are in the left half of the s-plane, which implies stability for a continuous-time system. The method involves constructing a table of coefficients of the polynomial and checking the signs of the elements in the first column. If all the elements are positive, then the system is stable. If any element is zero or negative, then the system is unstable or marginally stable.



  • Nyquist criterion: This method uses a graphical technique to determine the stability of a closed-loop system based on the shape and location of its open-loop frequency response plot. The method involves counting the number of encirclements of the point -1+j0 by the Nyquist plot as it traverses from low to high frequencies. The number of encirclements is equal to the number of unstable poles minus the number of unstable zeros in the closed-loop system.



  • Bode criterion: This method uses a graphical technique to determine the stability of a closed-loop system based on the magnitude and phase of its open-loop frequency response plot. The method involves finding the frequency at which the magnitude plot crosses -3 dB, which is called the crossover frequency, and checking the phase margin and gain margin at that frequency. The phase margin is the difference between -180 degrees and the phase plot at the crossover frequency, and the gain margin is the difference between 0 dB and the magnitude plot at the frequency where the phase plot crosses -180 degrees. A system is stable if both margins are positive, and unstable if either margin is negative.



Designing and Implementing Control Systems for Embedded Systems




Once you have a basic understanding of control theory, you can use various methods and tools to design and implement control systems for embedded systems. In this section, we'll introduce some of these methods and tools, such as PID controllers, digital filters, state-space methods and discrete-time systems.


PID Controllers




A PID controller is a type of controller that uses three terms to generate a control signal: proportional, integral and derivative. A PID controller has the following form:


where u(t) is the control signal, e(t) is the error signal (the difference between the reference and the output), Kp is the proportional gain, Ki is the integral gain, Kd is the derivative gain, and t is time.


A PID controller works by adjusting its control signal based on three aspects of the error signal: its magnitude (proportional term), its accumulation over time (integral term) and its rate of change (derivative term). By tuning these three gains, a PID controller can achieve various performance objectives such as fast response, low steady-state error and good disturbance rejection.


PID controllers are widely used in embedded control systems because they are simple, effective and easy to implement. However, PID controllers also have some limitations and challenges, such as choosing appropriate gains, dealing with noise and nonlinearities, and implementing anti-windup mechanisms.


Digital Filters




A digital filter is a device or algorithm that processes a discrete-time signal to modify its characteristics or extract useful information from it. A digital filter can be represented by a difference equation that relates its input and output sequences:


where y[n] is the output sequence, x[n] is the input sequence, M and N are nonnegative integers that determine the order of the filter, and bk and ak are filter coefficients.


A digital filter can also be represented by its transfer function in terms of z transform:


where H(z) is the transfer function, Y(z) is the output z transform, X(z) is the input z transform, and bk and ak are filter coefficients.


A digital filter can be classified into two main types: finite impulse response (FIR) and infinite impulse response (IIR). An FIR filter has only feedforward terms (ak=0 for k>0), and its impulse response (the output when the input is a unit impulse) has a finite duration. An IIR filter has both feedforward and feedback terms (ak≠0 for some k>0), and its impulse response has an infinite duration.


A digital filter can also be classified into different categories based on its frequency response characteristics, such as low-pass, high-pass, band-pass, band-stop, all-pass or notch filters. A digital filter can be designed using various methods, such as windowing, frequency sampling, bilinear transformation or pole-zero placement.


Digital filters are useful for embedded control systems because they can perform various tasks such as noise reduction, signal smoothing, signal detection, signal extraction, signal enhancement or signal modulation. However, digital filters also have some challenges and trade-offs, such as choosing appropriate filter type, order and coefficients, dealing with finite word length effects, and implementing efficient and robust algorithms.


State-Space Methods




State-space methods are a powerful and general approach for modeling, analyzing and designing control systems. A state-space model represents a system by a set of state variables that capture the essential features of the system dynamics, and a set of equations that relate the state variables to the inputs and outputs of the system.


A state-space model has the following form:


where x(t) is the state vector, u(t) is the input vector, y(t) is the output vector, A is the state matrix, B is the input matrix, C is the output matrix, and D is the feedthrough matrix.


A state-space model can also be represented in discrete-time form:


where x[n] is the state vector at time step n, u[n] is the input vector at time step n, y[n] is the output vector at time step n, A is the state transition matrix, B is the input matrix, C is the output matrix, and D is the feedthrough matrix.


State-space methods can be used to perform various tasks such as system identification, state estimation, stability analysis, controllability and observability analysis, pole placement, optimal control and model predictive control.


State-space methods are advantageous for embedded control systems because they can handle multiple inputs and outputs, nonlinearities and uncertainties, time-varying and time-delayed systems, and complex system interactions. However, state-space methods also have some drawbacks such as high computational complexity, difficulty in physical interpretation and parameter estimation.


Discrete-Time Systems




A discrete-time system is a system that operates on discrete-time signals, which are signals that are defined only at discrete instants of time. A discrete-time system can be represented by a difference equation that relates its input and output sequences:


where y[n] is the output sequence, x[n] is the input sequence, M and N are nonnegative integers that determine the order of the system, and bk and ak are system coefficients.


A discrete-time system can also be represented by its transfer function in terms of z transform:


where H(z) is the transfer function, Y(z) is the output z transform, X(z) is the input z transform, and bk and ak are system coefficients.


A discrete-time system can be classified into two main types: finite impulse response (FIR) and infinite impulse response (IIR). An FIR system has only feedforward terms (ak=0 for k>0), and its impulse response (the output when the input is a unit impulse) has a finite duration. An IIR system has both feedforward and feedback terms (ak≠0 for some k>0), and its impulse response has an infinite duration.


A discrete-time system can also be classified into different categories based on its frequency response characteristics, such as low-pass, high-pass, band-pass, band-stop, all-pass or notch systems. A discrete-time system can be designed using various methods, such as windowing, frequency sampling, bilinear transformation or pole-zero placement.


Discrete-time systems are essential for embedded control systems because they can model the behavior of digital devices and algorithms that operate on sampled data. However, discrete-time systems also have some challenges and trade-offs, such as choosing appropriate sampling rate, dealing with aliasing and quantization effects, and implementing efficient and robust algorithms.


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