unit 1.2 Overview of Control Engineering

制作人：中国石油大学（华东） xueyue Reading material Overview of Control Engineering The goal of control engineering is to improve, or in some cases enable , the performance of a system by the addition of sensors, control processors, and actuators. The sensors measure or sense various signals in the system and operator commands; the control processors process the sensed signals and drive the actuators, which affect the behavior of the system. A schematic diagram of a general control system is shown in figure 1. 1. 2. Figure 1. 1. 2 A schematic diagram of a general control system. This general dia8ram can represent a wide variety of control systems. The system to be controlled might be an aircraft, a large electric power generation and distribution system, an industrial process, a head positioner for a computer disk drive, a data network, or an economic system. The signals might be transmitted via analog or digitally encoded electrical signals, mechanical linkages. or pneumatic or hydraulic lines. Similarly the control processor o, processors could be mechanical, pneumatic. hydraulic. analog electrical, general-purpose or custom digital computers. Because the sensor signals can affect the system to be controlled (via the control processor and the actuators) ,the control system shown in figure 1. 1. 2 is called a feedback or locked-loop control system, which refers to the signal "loop" that circulates clockwise in this figure. In contrast, a control system that has no sensors. And therefore generates the actuator signals from the command signals alone, is sometimes called an open-loop control system"' Similarly. a control system that has no actuators, and produces only operator display signals by processing the sensor signals, is sometimes called a monitoring system. in industrial settings, it is often the case that the sensor, actuator, and processor signals are Boolean, i. e. assume only two values. Boolean sensors include mechanical and thermal limit switches, proximity switches. thermostats, and pushbutton switches for operator commands. Actuators that are often configured as Boolean devices include heaters, motors. pumps, valves. solenoids, alarms. and indicator lamps. Boolean control processors, referred to as logic controllers, include industrial relay systems. 制作人：中国石油大学（华东） xueyue general-purpose micoprocessors, and commercial programmable logic controllers. In this book, we consider control systems in which the sensor, actuator, and processor signals assume real values ,or at least digital representations of real values. Many control systems include both types of signals: the real-valued signals that we will consider, and Boolean signals, such as fault or limit alarms and manual override switches, that we will not consider. 1.System Design and Control Configuration Control configuration is the selection and placement of the actuators and sensors on the system to be controlled ,and is an aspect of system design that is very important to the control engineer. Ideally, a control engineer should be involved in the design of the system itself, even before the control configuration. Usually, however, this is not the case, the control engineer is provided with an already designed system and starts with the control configuration. Many aircraft, for example. are designed to operate without a control system ; the control system is intended to improve the performance (indeed, such control systems are sometimes .called stability augmentation systems. emphasizing the secondary role of the control system). Actuator Selection and Placement The control engineer must decide the type and placement of the actuators. In an industrial process system , for example , the engineer must decide where to put actuator such as pumps, heaters, and valves. The specific actuator hardware (or at least, its relevant characteristics) must also be chosen. Relevant characteristics include cost, power limit or authority, speed of response, and accuracy of response. One such choice might he between a crude, powerful pump that is slow to respond. and a more accurate but less powerful pump Sensor Selection and Placement The control engineer must also decide which signals in the system will be measured or sensed, and with what sensor hardware. in 8n industrial process, for example. the control engineer might decide which temperatures, flow rates, pressures. and concentrations to sense. For a mechanical system, it may be possible to choose where a sensor should be placed, e.g. where an accelerometer is to be positioned on an aircraft, or where a strain gauge is placed along a beam. The control engineer may decide the particular type or relevant characteristics of the sensors to he used, including type of transducer, and the signal conditioning and data acquisition hardware. For example, to measure the angle of a shaft, sensor choices include a potentiometer. a rotary variable differential transformer. or an 8-bit or 12-bit absolute or differential shaft encoder. In many cases, sensors are smaller than actuators. so a change of sensor hardware is a less dramatic revision of the system design than a change of actuator hardware. There is not yet a well-developed theory of actuator and sensor selection and placement possibly because it is difficult lo precisely formulate the problems. and possibly because the problems are so dependent on available technology. Engineers use experience simulation, and trial and error to guide actuator and sensor selection and placement. 2.Modeling The engineer develops mathematical models of . the system to be controlled, . noises or disturbances that may act on the system . the commands the operator may issue, . desirable or required qualities of the linear system. 制作人：中国石油大学（华东） xueyue These models might be deterministic (e g. . ordinary differential equations (ODE's), partial differential equations (PDE's) . or transfer functions) . or stochastic or probabilistic (e.g., power spectral densities). Models are developed in several ways. Physical modeling consists of applying various laws of physics (e .g . Newton's equations, energy conservation, or flow balance) to derive ODE or PDE models. Empirical modeling or identification consists of development: models from observed or collected data. The a priori assumptions used in empirical modeling can vary from weak to strong: in a "black box" approach, only a few basic assumptions are made,for example . linearity and time-invariance of the system . whereas in a physical model identification approach. a physical model structure is assumed, and the observed or collected data is used to determine good values for these parameters. Mathematical models of a system are often build up from models of subsystems, which may have been developed using different types of modeling. Often , several models are developed , varying in complexity and fidelity. A simple model might capture some r)f the basic features and characteristics of the system, noises, or commands ; a simple model can simplify the design, simulation, or analysis of the control system, at the risk of inaccuracy. A complex model could be very detailed and describe the system accurately, hut a complex model can greatly complicate the design, simulation, or analysis of the system. 3. Controller Design The controller or control law describes the algorithm or signal processing used by the control processor to generate the actuator signals from the sensor and command signals it receives. Controllers vary widely in complexity and effectiveness. Simple controllers include the proportional (P). the proportional plus derivative (PD) . the proportional plus integral (PI), and the proportional plus integral plus derivative (PID) controllers, which are widely and effectively used in many industries. More sophisticated controllers include the linear quadratic regulator {I.QR), the estimated-state feedback controller, and the linear quadratic Gau3sian (LQG) controller. These sophisticated controllers were first used in state-of-the-art aerospace systems, but are only recently being introduced in significant numbers. Controllers are designed by many methods. Simple P o, PI controllers have only a few parameters to specify, and these parameters might be adjusted empirical/y, while the control system is operating. using "tuning rules’. A controller design method developed in the 1930's through the 1950's, o{ten called classical controller design, is based on the 1930's work on the design of vacuum tube feedback amplifiers. With these heuristic (but very often successful) techniques, the designer attempts to synthesize a compensation network or controller with which the closed-loop system performs well (the terms 'synthesize’ ,"compensation’. and "network ‘ where borrowed from amplifier circuit design). In the 1960's through the present time. State-space or "modern" controller design methods have been developed. These methods are based on the fact that the solutions to some optimal control problems can be expressed in the form of a feedback law or controller, and the development of efficient computer methods to solve these optimal control problems. Over the same time period. researchers and control engineers have developed methods 0f controller design that are based on extensive computing, for example, numerical optimization. 4.Controller Implementation The signal processing algorithm specified by the controller is implemented on the control processor .Commercially available control processors are generally restricted to logic control and 制作人：中国石油大学（华东） xueyue specific types of control laws such as PID .Custom control Processors built from general-purpose microprocessors or analog circuitry can implement a very wide variety of control laws . general purpose digital signal processing (DSP)chips are often used in control processors that implement complex control laws .Special-purpose chips designed specifically for control processors are also now available. 5.Control System Testing ,Validation and Tuning Control system testing may involve ·extensive computer simulations with a complex ,detailed mathematical model ·real-time simulation of the system with the actual control processor operating (‘hardware in the loop’) ·real-time simulation of the control processor, connected to the actual system to be controlled. ·field tests of the control system Often the controller is modified after installation to optimize the actual performance , a process known as tuning. Selected from: Stephen P.boyd,Craig H.Barratt,”Linear Controller Design ”,Prentice-Hall ,Inc,1991 Word and Expression Sensor 传感器 Actuator 执行器 Schematic 示意性的 Aircraft 航空器 飞行器 Positioner 定位器 Encode 编码 Mechanical linkage 机械连接 Pneumatic 气动的 Hydraulic 水力的 液动的 Monitor 监视器 Boolean 布尔的 Switch 开关 Proximity 近似的 接近的 Thermostat 自动调温器 温度调节装置 Pushbutton 按钮 Solenoid（电）螺线管 Motor 电机 马达 Pump 泵 Valve 阀 Relay（电工）继电器 Override switch 过载开关 Control configuration 控制组态 Stability augmentation 稳定性增益 制作人：中国石油大学（华东） xueyue Concentration 浓度 Accelerometer 加速度表 Strain gauge 应变仪 拉力计 Transducer 传感器 acquisition 获取，采集 potentiometer 电位器 stochastic 随机的 probabilistic ．概率的 physical modeling 物理模型 empirical modeling 经验模型 identification 辩识 parameter 参数 fidelity 保真性 proportional plus derivative 比例加微分 proportional plus integral 比例加积分 sophisticated 复杂的，高级的 非常有经验的 linear quadratic regulator 线性二次型调节器 linear quadratic Gaussian 线性二次型高斯 state-of-the-art 技术水平，科学发展动态，现代化的 tuning rules 整定规则 classical 经典的 heuristic 启发式的 optimal 最优的，最佳的 algorithm 算法 validation 有效，证实 field test 现场测试，现场试验 e.g. exampli gratia (拉丁语) 例如