Performance Improvement of the Nonlinear Fuzzy PID Controller

The photovoltaic (PV) system’s nonlinear nature, uncertainty in temperature, and light intensity in the environment are responsible for the fluctuation of the maximum power point (MPP). Many maximum power point tracking (MPPT) algorithms are proven effective in the literature, including use of artificial intelligence. Proportional integral derivative controller and fuzzy controllers found effective to track MPP. Using the combination of Fuzzy-PID controller, swiftly adapt to changes in the atmosphere to confirm the solar array is continually operating at its highest efficiency with MPP. Easy and effective oscillation elimination is possible with PID control. To regulate the maximum power of the PV system combination of Fuzzy-PID self-tuning controllers is found effective.The goal is to enhance a PV system’s dynamic response to in presence of irradiation and temperature fluctuations. For tuning Fuzzy logic controller (FLC), gain scheduler values of the proportional, integral, and derivative constants are fundamental. The design processes for non-linear conventional PID controllers, FLC, and series combination of Fuzzy-PID controller are discussed. It compares the traditional PID controller, FLC, and Fuzzy-PID controller. The comparison of outputs produced for PID, FLC, and series Fuzzy-PID controller confirms the effectiveness of the algorithm. Use of Fuzzy controller helps in self-tuning of PID controller constant value. MATLAB/Simulink based environment used to perform the simulations.

The nonlinear fuzzy PID control strategy, which combines nonlinear control, fuzzy control and PID control, is put forward in this paper to deal with the control of a class of nonlinear and large inertia plants under complicated and large variation conditions. The proposed control strategy can adjust control parameters timely and appropriately according to the dynamic characteristics of the systems, so as to achieve fast transient response and good steady-state accuracy. Moreover, the control strategy has good robustness and self-adaptive ability to the changes of model parameters. Take the case of the three-tank water system, the control strategy is investigated by simulation and experimentation. Experimental results, which agree with simulation results, show the control strategy suggested in the paper is correct and feasible. The design and implementation of it is simple and convenient, and it may have important reference value for the control of a class of continuous nonlinear and large inertia plants such as heating furnace and multi-capacity level plant in complicated conditions.

In CNC lathe servo feed system, many parameters are uncertain, so it is difficult to establish accurate mathematical model of the feed system, and parameters of conventional PID controller can not change the parameters to adjust with the controlled object. Fuzzy PID controller, which has the advantages of conventional PID control and fuzzy control, can solve these problems very well. So we can make it applied in CNC lathe servo feed system. This paper has made a simulation of fuzzy self-turning PID controller in the MATLAB environment. The simulation shows that the fuzzy self-turning PID controller has a better control capability and a higher stability, and it improved the servo feed system dynamic performance.

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

Aiming at the limitation of traditional PID algorithm in the field of control, the fuzzy control theory and traditional PID control algorithm are combined to realize parameter self-tuning function, a fuzzy self-tuning PID controller, and this algorithm is analyzed in-depth study. It provides a new solution of modern industrial control problem that combining with fuzzy control technology and PID control technology. The FPGA has high integration, short development cycle, the characteristics of strong reusability; fuzzy PID controller has a good practical value and optimistic application prospect based on FPGA.

This paper presents an auto-tuned, fuzzy logic controller. It starts with a simple fuzzy PID controller definition which is a useful nonlinear PID controller. Then an analysis of the fuzzy nonlinear relationship is given and some features of the fuzzy controller are discussed. The scaling factors of the fuzzy PID controller are associated with PID controller parameters. Thirdly, a practical approach to auto-tuning the fuzzy PID controller is proposed by using Astrom and Hagglund's (1984) auto-tuning technique for PID tuning. Last, experiments on the auto-tuned fuzzy PID controller are conducted and compared to regular PID controllers.

The paper is concerned with two types of fuzzy PID controllers, the rule-based fuzzy PID controller and the learning fuzzy PID controller. Initially, the development of the rule-based fuzzy PID controller is studied, and subsequently, the development of the learning fuzzy PID controller is outlined. For the rule-based fuzzy PID controller, the PID gains are firstly tuned using a conventional tuning method. The rule buffer of the rule-based fuzzy then readjusts the PID gains during the system operation. The learning fuzzy PID controller not only readjusts the PID gains, it also creates and updates the rules in the rule-buffer automatically during the application of the learning fuzzy PID controller to the process. The rule-based fuzzy PID controller and the learning fuzzy PID controller are both applied to a non-linear revolute-joint robot-arm for path tracking experiments. The results of the experiments for the rule-based fuzzy PID controller and the learning fuzzy controller are compared with a conventional PID controller in order to evaluate the results.

PID and fuzzy PID controller are applied into the wind turbine with nonlinear mathematical model. PID control has simple principle and its parameters setting are rather easy. Fuzzy control need not to establish the mathematical of the control system and has strong robustness. Fuzzy PID control is combined with PID control and fuzzy control. The advantages of fuzzy PID control are simple, easy in setting parameters and strong robustness. Fuzzy PID controller combines with closed-loop optimal fuzzy reasoning (COFR), which can effectively improve the robustness, when the robustness is special requirement. MATLAB software is used for simulations, results display that fuzzy PID controller which combines with COFR has better performances than PID controller when errors exist, especially robustness performance.

In the process of frozen food production, its refrigeration system is a typical large inertia, nonlinear, strongly coupled system, which is prone to large overshooting during operation, resulting in cost increase and quality control problems. This paper solves the problem of large overshoot in the digital PID control process by designing a fuzzy control, and designs a practical fuzzy controller so that the design method of the fuzzy PID controller can be realized in the simulation and actual control of the freezing system. A genetic algorithm optimization scheme is also designed for the fuzzy PID control system to optimize the fuzzy controller’s affiliation function, control rules, as well as proportionality and quantization factors, to enhance the model’s dynamic characteristics and robustness. Design of food quick-freezing simulation experiments and use the model in the refrigeration system control in practice, compare the performance of this model with the PID, fuzzy PID model, and found that the temperature difference set value of 4 ℃ and 3 ℃, respectively, the parameters of the traditional PID controller in the commissioning stage of the system after the end of the rectification to remain fixed, which makes the actual quality of the cooling water temperature difference control degradation. And the fuzzy genetic PID controller still has good control quality. In this paper, the model curve converges rapidly, the overshoot is only 3.9%, and the regulation time is only 450M seconds. The designed method of the study is feasible and has high industrial application value.

Because of the existing hybrid fuzzy PID controller does not perform, using electric hydraulic servo system application (SEHS). Therefore, when the system parameters change will require a new adjustment of PID controller variable. Therefore, a hybrid fuzzy and fuzzy self-tuning PID control was put forward. With this control scheme was divided into two parts, and the fuzzy controller and fuzzy self-tuning PID controller. Fuzzy controller is used to control the output of the system of the values of the system away from target value. We proved that the performance of the control scheme through the experiment of the motor speed control SEHS. The experimental results show that the proposed a hybrid fuzzy PID controller and fuzzy self-tuning effect is better than that of a hybrid fuzzy and PID controller.

To improve tracking precision and increase the utilization of solar energy, the solar automatic tracking fuzzy PID control system based on ARM is designed. Embedded microcontroller 32-bit ARM processor is the core. A kind of mixed tracking mode is adopted, and it combines photoelectric tracking mode and solar trajectory tracking mode. In the solar trajectory tracking mode, the fuzzy PID segmented tracking control method is put forward. The fuzzy control and PID control are combined to control the system. If the system's angle deviation is large, PID control is adopted, else fuzzy control is adopted. And the controller can be automatically switched control mode according to the size of angle deviation. Matlab simulation results indicate that the solar automatic tracking system with fuzzy PID control has a good dynamic characteristics and high tracking accuracy, effectively improves the photoelectric conversion efficiency.

Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains

Structural analysis of fuzzy controllers with nonlinear input fuzzy sets in relation to nonlinear PID control with variable gains*1

Cascade fuzzy control of a tubular chemical reactor

The research on active control of the pantograph has been intensively studied, however, the control results in practice are not satisfactory as expected, for the existence of time delays coming from the input and output measurement. This paper focuses on the problem of time delays in the designing of the control strategy, including the sensor signal input and the action of the actuator. A multibody dynamic pantograph model is established based on the relative coordinate method, combined with the rigid catenary established by the modal superposition method. Firstly, the performance of the traditional PID controller with signal time delay is studied, including the separation or combination of sensor signal input delay and actuator signal output delay. Then, a novel fuzzy PID controller with an improved rule library is proposed, which further improves the control performance of the PID controller. Finally, to deal with the time delay of sensor and actuator well, a dynamic predictive fuzzy adaptive PID (DMC-Fuzzy-PID) controller is proposed by combining dynamic matrix control (DMC) with the improved fuzzy PID controller, which may support the engineering application of active control of pantograph.KeywordsActive controlTime delayRigid catenaryMultibody dynamicsFuzzy PID controller