Based On Fuzzy Pid Smith Predictor Controllers And Applications

In the industrial process control, many processes exhibit dead times in their dynamic behavior (the effect of the control action takes some time to felt in the controlled variable). Therefore, it is difficult to obtain good control performance using standard feedback controllers for processes with significant dead times. The Smith predictor is an effective controller for time-delay systems, but there are some shortcomings, such as an accurate model needed and poor disturbance rejection and robustness. Therefore, how to improve the robustness of Smith predictor has important theoretical and practical value. Meanwhile, it is very important to extend the application of Smith predictor.After analysising the basic characteristics of fuzzy PID (Proportion Integral Derivative) controller and Smith predictor, an analytical fuzzy PID controller is introduced to the Smith predictor in order to improve the robustness. Based on Lyapunov theory and sliding mode control, it can be proofed that the robustness of fuzzy PID-based Smith predictor is better than the PID-based Smith predictor in time domain. In simulation examples, we consider many examples, such as first-order plus dead-time (FOPDT) system, second-order plus dead-time (SOPDT) system, second order integral plus dead-time system, high-order system. The simulation results further demonstrate the robustness of the fuzzy PID-based Smith predictor.Parameter tuning is also very for the application of Smith predictor in industry. By using the internal model control (IMC) based tuning method for traditional PID controllers and regarding the fuzzy PID controller as an internal model controller,we can get the parameters of the fuzzy PID controller-the main controller of the fuzzy PID-based Smith predictor.In terms of examples, use TIF2812DSP (Digital Signal Process) as a core to establish a fuzzy PID-based Smith predictor control experimental platform. The process is founded with operational amplifiers. Then, experiments carry out with the combine of MATLAB and CCS, the experimental data and results further show that the robustness of the fuzzy PID-based Smith predictor and the effectiveness of the tuning method.