Discrete Controller Design

Slides:



Advertisements
Similar presentations
PID Control Loops Guy Zebrick.
Advertisements

Chapter 2-4. Equilibrium carrier concentrations
1 Chapter 40 - Physiology and Pathophysiology of Diuretic Action Copyright © 2013 Elsevier Inc. All rights reserved.
FIGURE 12.1 Two variable process-control loops that interact.
FIGURE 9.1 Control of temperature by process control.
0 - 0.
Addition Facts
PID Control Professor Walter W. Olson
Pole Placement.
President UniversityErwin SitompulModern Control 7/1 Dr.-Ing. Erwin Sitompul President University Lecture 7 Modern Control
Controller Design Based on Transient Response Criteria
Tuning PID Controller Institute of Industrial Control,
10.1 Introduction Chapter 10 PID Controls
ERT 210 Process Control & dynamics
PID Controllers and PID tuning
Modern Control Systems (MCS)
1 ChE / MET Apr 12. Feedback Controller Tuning: (General Approaches) 1)Simple criteria; i.e QAD via ZN I, t r, etc easy, simple, do on existing.
INDUSTRIAL AUTOMATION (Getting Started week -1). Contents PID Controller. Implementation of PID Controller. Response under actuator Saturation. PID with.
Jan Jantzen Fuzzy PID Control Jan Jantzen
CHE 185 – PROCESS CONTROL AND DYNAMICS
Solving Absolute Value Equations Solving Absolute Value Equations
Addition 1’s to 20.
Week 1.
Dynamic Behavior of Closed-Loop Control Systems
Chapter 4 Systems of Linear Equations; Matrices
Enhanced Single-Loop Control Strategies
Chapter 4: Basic Properties of Feedback
Nyquist Stability Criterion
CHE 185 – PROCESS CONTROL AND DYNAMICS
CHE 185 – PROCESS CONTROL AND DYNAMICS
CHE 185 – PROCESS CONTROL AND DYNAMICS
Controller Tuning: A Motivational Example
Process Control Instrumentation II
Proportional/Integral/Derivative Control
Book Adaptive control -astrom and witten mark
DYNAMIC BEHAVIOR AND STABILITY OF CLOSED-LOOP CONTROL SYSTEMS
Controller Design (to determine controller settings for P, PI or PID controllers) Based on Transient Response Criteria Chapter 12.
Basic Concepts  Block diagram representation of control systems  Transfer functions  Analysis of block diagrams  P, PI and PID controllers ( Continuous.
Control Systems Design Part: Optimisation Slovak University of Technology Faculty of Material Science and Technology in Trnava 2007.
Model Reference Adaptive Control (MRAC). MRAS The Model-Reference Adaptive system (MRAS) was originally proposed to solve a problem in which the performance.
PID Controller Design and
PID. The proportional term produces an output value that is proportional to the current error value. Kp, called the proportional gain constant.
Final Exam M.Eng. Amjad Elshenawy RTECS Gc(s)Gc(s) Gc(s)Gc(s) Controller   n sensor noise  w load disturbance  Gp(s)Gp(s) Gp(s)Gp(s) Plant.
Automatic Control System V. Compensation. If the satisfactory design of the process dynamics can’t be obtained by a gain adjustment alone, then some methods.
PID CONTROLLERS By Harshal Inamdar.
Lecture 25: Implementation Complicating factors Control design without a model Implementation of control algorithms ME 431, Lecture 25.
Control Systems With Embedded Implementation (CSEI)
Features of PID Controllers
Chapter 4 A First Analysis of Feedback Feedback Control A Feedback Control seeks to bring the measured quantity to its desired value or set-point (also.
System Time Response Characteristics
Lecture 2: Linear Discrete Systems 1. Introduction The primary new component of discrete or digital systems is the notion of time discretization. No longer.
Lecture 9: PID Controller.
Advanced control strategies. CONTROL SYSTEMS The process parameters which are measured using probes described in the previous sections may be controlled.
Óbudai Egyetem Dr. Neszveda József Open and Closed loop Control II. Block diagram model.
SKEE 3143 Control Systems Design Chapter 2 – PID Controllers Design
BIRLA VISHWAKARMA MAHAVIDHYALAYA ELECTRONICS & TELECOMUNICATION DEPARTMENT o – ANKUR BUSA o – KHUSHBOO DESAI UNDER THE GUIDENCE.
Lecture 5\6 Analysis in the time domain (I) —First-order system North China Electric Power University Sun Hairong.
ABE425 Engineering Measurement Systems ABE425 Engineering Measurement Systems PID Control Dr. Tony E. Grift Dept. of Agricultural & Biological Engineering.
Exercise 1 Suppose we have a simple mass, spring, and damper problem. Find The modeling equation of this system (F input, x output). The transfer function.
EEN-E1040 Measurement and Control of Energy Systems Control I: Control, processes, PID controllers and PID tuning Nov 3rd 2016 If not marked otherwise,
Salman Bin Abdulaziz University
Chapter 7 The Root Locus Method The root-locus method is a powerful tool for designing and analyzing feedback control systems The Root Locus Concept The.
Automatic control systems II
Lec 14. PID Controller Design
PID controller for improving Max power point tracking system
Basic Design of PID Controller
Chapter 9 Design via Root Locus <<<4.1>>>
Digital Control Systems (DCS)
UNIT-II TIME RESPONSE ANALYSIS
PID Controller Design and
Presentation transcript:

Discrete Controller Design Chapter 9: Discrete Controller Design (PID Controller)

9.2 PID CONTROLLER The proportional–integral–derivative (PID) controller is often referred to as a ‘three-term’ controller. It is one of the most frequently used controllers in the process industry. In a PID controller the control variable is generated from the sum of a term proportional to the error, a term which is the integral of the error, and a term which is the derivative of the error.

9.2 PID CONTROLLER Proportional: the error is multiplied by a gain Kp. A very high gain may cause instability, and a very low gain may cause the system to be very sluggish (slow). Integral: the integral of the error is found and multiplied by a gain. The gain can be adjusted to drive the error to zero in the required time. Derivative: The derivative of the error is multiplied by a gain. The derivative control is used to improve the transient response by reducing overshoot.

The input–output relationship of a PID controller can be expressed as where u(t) is the output from the controller and e(t) = r (t) − y(t), in which r (t) is the desired set-point (reference input) and y(t) is the plant output. Ti and Td are known as the integral and derivative action time, respectively. By taking the Laplace transform of this equation, we can write the transfer function of a continuous-time PID as

Discrete PID Controller To implement the PID controller using a digital computer we have to convert the equation: from a continuous to a discrete representation. There are several methods for doing this and the simplest is to use the trapezoidal approximation for the integral and the backward difference approximation for the derivative: 0 𝑡 𝑒 𝑡 𝑑𝑡 ≈ 𝑘=1 𝑛 𝑇𝑒 𝑘𝑇 , 𝑑𝑒(𝑡) 𝑑𝑡 ≈ 𝑒 𝑛𝑇 −𝑒(𝑛𝑇−𝑇) 𝑇

Using these approximations, we can write: 𝑢 𝑛𝑇 = 𝐾 𝑝 [𝑒 𝑛𝑇 + 1 𝑇 𝑖 𝑘=1 𝑛 𝑇𝑒 𝑘𝑇 + 𝑇 𝑑 𝑒 𝑛𝑇 −𝑒(𝑛𝑇−𝑇) 𝑇 ], 𝑢 𝑛𝑇−𝑇 = 𝐾 𝑝 [𝑒 𝑛𝑇−𝑇 + 1 𝑇 𝑖 𝑘=1 𝑛−1 𝑇𝑒 𝑘𝑇 + 𝑇 𝑑 𝑒 𝑛𝑇−𝑇 −𝑒(𝑛𝑇−2𝑇) 𝑇 ]. Subtracting these two equations, we obtain: 𝑢 𝑛 = 𝑢 𝑛−1 +𝐾 𝑝 𝑒 𝑛 − 𝑒 𝑛−1 + 𝐾 𝑝 𝑇 𝑇 𝑖 𝑒 𝑛 + 𝐾 𝑝 𝑇 𝑑 𝑇 [ 𝑒 𝑛 − 2𝑒 𝑛−1 + 𝑒 𝑛−2 ] where 𝑢 𝑛 :=𝑢(𝑛𝑇) and 𝑢 𝑛−1 :=𝑢 𝑛𝑇−𝑇 . The PID is now in a suitable form which can be implemented on a digital computer. Here the current control action uses the previous control value as a reference.

9.2.3 PID Tuning Tuning the controller involves adjusting the parameters Kp, Td and Ti in order to obtain a satisfactory response. There are many techniques for tuning a controller, ranging from the first techniques described by J.G. Ziegler and N.B. Nichols (known as the Ziegler– Nichols tuning algorithm), to recent auto-tuning controllers. In this section we shall look at the tuning of PID controllers using the Ziegler–Nichols tuning algorithm.

Ziegler and Nichols suggested values for the PID parameters of a plant based on open-loop or closed- loop tests of the plant. According to Ziegler and Nichols, the open-loop transfer function of a system can be approximated with a time delay and a single-order system, i.e. where TD is the system time delay (i.e. transportation delay), and T1 is the time constant of the system.

For open-loop tuning, we first find the plant parameters by applying a step input to the open loop system. The plant parameters K, TD and T1 are then found from the result of the step test as shown.

Ziegler and Nichols then suggest using the PID controller settings given in the Table below when the loop is closed. These parameters are based on the concept of minimizing the integral of the absolute error after applying a step change to the set- point.

Example The open-loop unit step response of a thermal system is shown. Obtain the transfer function of this system and use the Ziegler–Nichols tuning algorithm to design: a proportional controller, a proportional plus integral (PI) controller, and a PID controller. Draw the block diagram of the system in each case.

Solution From the previous Figure, the system parameters are obtained as K = 40◦C, TD = 5 s and T1 = 20 s, and, hence, the transfer function of the plant is

(a) Proportional controller According to the Table of ZN settings for a proportional controller are: Thus,

The transfer function of the controller is then and the block diagram of the closed-loop system with the controller is shown below.