Smith Fuzzy PID Research Articles

Research on Post-Rolling Cooling System Temperature Control Based on Smith-Fuzzy PID

Research on Post-Rolling Cooling System Temperature Control Based on Smith-Fuzzy PID

Residual Error Integral Predictor-Based Smith Fuzzy PID Temperature Controller for Thermal Vacuum Test

Residual Error Integral Predictor-Based Smith Fuzzy PID Temperature Controller for Thermal Vacuum Test

Exploring the optimal design of computer control system for heating boilers in power plants

Objective: For stable and efficient control of the heating boilers in power plants, an improved Smith-fuzzy PID algorithm is used to optimize the computer control system for heating boilers. Methods: Under the computer control system, the pressure, exhaust gas temperature, water temperature, safety, and energy consumption of heating boilers are explored, thereby determining the optimization effect of the computer control system. Results: The improved Smith-fuzzy PID algorithm has the optimal control effect on water temperature and pressure of the heating boilers, with the highest balance and stability. In comparison, the fluctuations in temperature control curves under Smith-PID and PID algorithms are large. Compared with the exhaust gas temperature of the other two algorithm systems, the exhaust gas temperature of the improved Smith-fuzzy PID algorithm-based computer system is reduced by 40 ?C, which decreases the consumption of coal resources. Conclusion: The improved Smith-fuzzy PID algorithm-based heating boiler computer control system has the most prominent effects on water temperature, pressure, and exhaust gas temperature. The designed system is accurate and reliable, satisfying the actual design requirements of computer control systems for heating boilers.

Modified Smith fuzzy PID temperature control in an oil-replenishing device for deep-sea hydraulic system

With the characteristics of large inertia, large time delay and time-varying parameters, it is difficult to control accurately the oil heating process in an oil-replenishing device for deep-sea hydraulic system. Four temperature control methods are presented and compared in this paper, i.e. proportional–integral–derivative controller(PID controller), fuzzy PID controller, Smith fuzzy PID controller and modified Smith fuzzy PID controller which adds a first order filter in the Smith predictor. They are simulated in MATLAB/SIMULINK based on the cases of transfer function matching and mismatching between the theoretical and the actual model. The results show that, in both cases, the step response curve of the modified Smith fuzzy PID controller has neither the significant oscillation nor the overshoot and requires the minimum time to achieve stabilization as well. Furthermore, this paper applies those four control methods in actual oil heating temperature control process through PIC16F877A micro controller unit (MCU). The experimental data demonstrate that the oil heating system controlled by the modified Smith fuzzy PID controller could stabilize at the expected temperature with nearly no overshoot, which further proves that the modified Smith fuzzy PID controller is able to sufficiently meet the challenges of industrial applications containing varying system parameters.

Simulation of Smith fuzzy PID temperature control in enzymatic detection of pesticide residues.

Enzyme activity is easily influenced by temperature, resulting in accuracy decline of enzymatic detection of pesticide residues. In this study, a controller which controls the internal temperature of the pesticide residues detector was simulated and analyzed. The mathematical model of temperature control was established by the application of the heat transfer theory. Against models with characteristics of large inertia and large hysteresis, a Smith fuzzy PID controller was proposed by combining the Smith predictor with the fuzzy PID controller. The PID controller, the fuzzy PID controller, and the Smith fuzzy PID controller were simulated in MATLAB, respectively in the same step signal given with amplitude of 1. The performance indexes (percent overshoot, settling time, and steady-state error) of various controllers were presented as follows: the PID controller (19%, 250 s, 0.0001), the fuzzy PID controller (11%, 450 s, 0.0001), and the Smith fuzzy PID controller (0%, 140 s, 0). From 1 180 s to 1 230 s, an interference signal with amplitude of 5 was added to test interference immunity. The PID controller and the fuzzy PID controller had greater fluctuations, but the Smith fuzzy PID controller had no fluctuations. The recovery time of each controller was described below: the PID controller (200 s), the fuzzy PID controller (300 s), and the Smith fuzzy PID controller (120 s). Robustness of the controller was tested by adjusting the time constant and the delay time. The performance indexes of the controllers were shown as follows: the PID controller (38%, 450 s, 0.0001), the fuzzy PID controller (23%, 880 s, 0.00025), and the Smith fuzzy PID controller (1%, 150 s, 0). The results of simulation showed that the performance indexes of the Smith fuzzy PID controller were better than that of the other controllers. Besides, the robustness and interference immunity are stronger than other controllers as well. The Smith fuzzy PID controller can accurately control the internal temperature of the pesticide residues detector to provide the best temperature for enzymatic detection. DOI: 10.3965/j.ijabe.20150801.007 Citation: Sun J, Zhang M X, Li Z M, Wu X H. Simulation of Smith fuzzy PID temperature control in enzymatic detection of pesticide residues. Int J Agric & Biol Eng, 2015; 8(1): 50－56.

Application of a New Smith Fuzzy PID in Electric Boiler Temperature Control System

Since the object model is time-varying and time-delay in the electric boiler temperature control system, Smith Fuzzy PID controller is applied in it. Simulation results in the paper show that the overshoot is reduced by 31.3% compared with the system under Smith PID control and the adjusting time reached a 30% reduction compared with those under Fuzzy PID control with T=380 and τ=110. In order to eliminate the oscillation problem in system when model parameters change, a new Smith Fuzzy PID controller with two degrees of freedom is applied, whose control performance is much better than that of Smith Fuzzy PID.