基于pid算法的小型恒温水浴锅控制系统的设计外文翻译(编辑修改稿)

基于pid算法的小型恒温水浴锅控制系统的设计外文翻译(编辑修改稿)内容摘要：

e, chemical position, speed and practically every other variable for which a measurement exists. Automobile cruise control is an example of a process which utilizes automated control. Due to their long history, simplicity, well grounded theory and simple setup and maintenance requirements, PID controllers are the controllers of choice for many of these applications. controller theory Note: This section describes the ideal parallel or noninteracting form of the PID controller. For other forms please see the Section Alternative notation and PID forms. The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). Hence: where Pout, Iout, and Dout are the contributions to the output from the PID controller from each of the three terms, as defined below. . Proportional term The proportional term makes a change to the output that is proportional to the current error value. The proportional response can be adjusted by multiplying the error by a constant Kp, called the proportional gain. The proportional term is given by: Where Pout: Proportional output Kp: Proportional Gain, a tuning parameter e: Error = SP − PV t: Time or instantaneous time (the present) Change of response for varying KpA high proportional gain results in a large change in the output for a given change in the error. If the proportional gain is too high, the system can bee unstable (See the section on Loop Tuning). In contrast, a small gain results in a small output response to a large input error, and a less responsive (or sensitive) controller. If the proportional gain is too low, the control action may be too small when responding to system disturbances. In the absence of disturbances, pure proportional control will not settle at its target value, but will retain a steady state error that is a function of the proportional gain and the process gain. Despite the steadystate offset, both tuning theory and industrial practice indicate that it is the proportional term that should contribute the bulk of the output change. term The contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. Summing the instantaneous error over time (integrating the error) gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain and added to the controller output. The magnitude of the contribution of the integral term to the overall control action is determined by the integral gain, Ki. The integral term is given by: Iout: Integral output Ki: Integral Gain, a tuning parameter e: Error = SP − PV τ: Time in the past contributing to the integral response The integral term (when added to the proportional term) accelerates the movement of the process towards setpoint and eliminates the residual steadystate error that occurs with a proportional only controller. However, since the integral term is responding to accumulated errors from the past, it can cause the present value to overshoot the setpoint value (cross over the setpoint and then create a deviation in the other direction). For further notes regarding integral gain tuning and controller stability, see the section on loop tuning. Derivative term The rate of change of the process error is calculated by determining the slope of the error over time (. its first derivative with respect to time) and multiplying this rate of change by the derivative gain Kd. The magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd. The derivative term is given by: Dout: Derivative output Kd: Derivative Gain, a tuning parameter e: Error = SP − PV t: Time or instantaneous time (the present) The derivative term slows the rate of change of the controller output and this effect is most noticeable close to the controller setpoint. Hence, derivative control is used to reduce the magnitude of the overshoot produced by the integral ponent and improve the bined controllerprocess stability. However, differentiation of a signal amplifies noise and thus this term in the controller is highly sensitive to noise in the error term, and can cause a process to bee unstable if the noise and the derivative gain are sufficiently large. Summary The output from the three terms, the proportional, the integral and the derivative terms are summed to calculate the output of the PID controller. Defining u(t) as the controller output, the final form of the PID algorithm is: and the tuning parameters are Kp: Proportional Gain Larger Kp typically means faster response since the larger the error, the larger the Proportional term pensation. An excessively large proportional gain will lead to process instability and oscillation. Ki: Integral Gain Larger Ki implies steady state errors are eliminated quicker. The tradeoff is larger overshoot: any negative error integrated during transient response must be integrated away by positive error before we reach steady state. Kd: Derivative Gain Larger Kd decreases overshoot, but slows down transient response and may lead to instability due to signal noise amplification in the differentiation of the error. 3. Loop tuning If the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, . its output diverges, with or without oscillation, and is limited only by saturation or mechanic。