The Voltage Stabilizing Control Strategy of Off-Grid Microgrid Cluster Bus Based on Adaptive Genetic Fuzzy Double Closed-Loop Control

The nuclear steam generator is a nonminimum-phase system, which is caused by the swell and shrink effects. Since its inverse system has unstable dynamics, it is difficult to train the fuzzy controller via the conventional backpropagation of the system output errors. In this paper, a genetic algorithm is applied for the simultaneous design of membership functions and rule sets for a fuzzy control method for a steam generator water level. The genetic fuzzy controller for the steam generator is a fuzzy logic controller which is tuned offline by the genetic algorithm using the water level, feedwater flowrate, and steam flowrate signals of the steam generator. The symmetric Gaussian membership functions based on the flowrate and water level errors are applied. The proposed genetic fuzzy controller has a generalized and simplified rule base. The same genetic algorithm that is used to optimize the genetic fuzzy controller tunes a conventional proportional-integral (P-I) controller, and the performance of two controllers is compared. The genetic fuzzy controller shows good response that its swell and shrink phenomena are smaller and its response is faster than those of a well-tuned P-I controller are.

As the classical PID can not adapt to the dynamic characteristics of the brushless DC motor, integral separation fuzzy control combined with classical PI is put forward to build up brushless DC motor speed current double closed-loop control system. Among them, the speed outer loop uses integral separation fuzzy PI control; the current inner loop uses integral separation PI control. With the core of LPC1769 microprocessor, the hardware system is designed, in which the integral separation double closed-loop fuzzy control software is achieved. Simulation and practical test show that integral separation fuzzy control strategy has fast response, small overshoot, and strong ability of anti-disturbance, which is more suitable for dynamic control of brushless DC motor.

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.

This paper presents a systematic design method of a controller using the discrete fuzzy PI control and the particle swarm optimization (PSO). Unlike a conventional PI controller, the discrete fuzzy PI controller has variable gains according to its input variables. Generally, it is complicated to tune the parameters of a fuzzy controller because there are too many parameters which are strongly coupled. In the discrete-time domain, the discrete fuzzy PI controller is a superset of the conventional PI controller. And the initial parameters of the fuzzy PI controller are selected by using the inclusion relationship. And, for the sake of simplicity, only four rules are used to construct a nonlinear fuzzy control surface. The tuning parameters of the discrete fuzzy PI controller are optimized by using the PSO. To verify the effectiveness of the controller designed by using the discrete fuzzy PI control and the PSO, we applied it to a wind turbine pitch controller. As a result, the proposed controller has variable gains, unlike the PI controller, and make the pitch controller operate in boarder operating regions.

PMSM has the characteristics of nonlinearity, strong coupling, and time-varying parameters. The parameters of PI controller cannot make self-tuning with the change of system state. This paper proposes a new fuzzy control rule. The proportion factor can be adjusted online. The simulation results show that fuzzy PI controller speed regulation system with improved fuzzy rules has faster response speed, small overshoot and strong anti-interference ability. The control system with improved rules has better control performance.

As a kind of mobile robot, the intelligent trolley is also called a wheeled robot, and this field is the fastest growing in the current technological development. Smart trolleys have good automatic guidance functions, often used in modern logistics conveying systems. A fuzzy control algorithm has a good control effect for nonlinear systems and has a strong anti-interference ability. In order to optimize the multi-objective longitudinal following system of smart carts, therefore, this paper develops the study of fuzzy PID control algorithm for multi-objective longitudinal following of smart carts. Based on the incremental PID control, an improved PID algorithm is constructed by introducing incomplete differentiation, differential prior, and “optimal curvature” algorithm. Based on the actual control experience, the fuzzy control table is designed, and the adaptive control algorithm is obtained. Finally, the simulation results of the fuzzy PID control algorithm are tested by MATLAB/Simulink, and the most suitable fuzzy PID control algorithm for multi-objective optimal longitudinal following of intelligent carts is derived.

The study of Vehicle Health monitoring plays very important role in deciding the possibility of completing journey successfully. Already work has been done on Pre Trip plan assistance with vehicle Health Monitoring with simple and Hybrid Fuzzy controllers. The conventional Fuzzy logic controllers are knowledge-based systems, incorporating human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions. They are limited in application, as its logic rules and membership functions have to be preset with expert knowledge and have a great influence over the performance of the fuzzy logic controller. In the present work, a Genetic Fuzzy system is proposed to promote the learning performance of logic rules. The resultant hybrid system seems to be highly adaptive and trained through a proper performance, hence is much more sophisticated and has a higher degree of adaptive parameters. The work proposes a benefit of Methodology by comparison of calculating safest distance by evaluating Vehicle Health Monitoring using Hybrid Fuzzy, and genetic fuzzy controller.

Aiming at the characteristics of automobile braking and the performance demands of ABS, the mathematic model is founded. ABS control strategy based on robust and fuzzy logic hybrid control is advanced. The fuzzy and robust control simulation model is founded with Matlab/Simulink for automobile ABS. The model has better robustness, good steady-state characteristics, strong anti-interference ability and high control accuracy. The simulation results compare with a single robust control, fuzzy control and the ideal ABS control. The results show the fuzzy robust control strategy can effectively improve the response of automobile ABS, strengthen the vehicle's ability to adapt to various roads. It also can get the best braking slip ratio and the braking distance.

To obtain perfect performances of the main steam temperature control system, a new scheme is proposed. The scheme substitutes fuzzy self-tuning PID controller for the main regulator of cascade control system. The controller is composed of fuzzy controller and PID controller. According to the error and error rate of the control system and fuzzy control rules, the fuzzy controller can online adjust the three parameters of the PID controller, then alter the output of the fuzzy self-tuning PID controller to maintain the main steam temperature. The results show that the proposed control scheme has smaller overshoot, shorter settling time, better capacity of anti-disturbance and stronger robustness than the conventional PID cascade control.

The objectives of the project are to simulate linear Mamdami type fuzzy temperature controller and non-linear Takegi-Sugeno type fuzzy temperature controllers using MATLAB and Simulink, and to compare the performance between the two controllers. A case study has been created to test the controllers involved a water boiler, where the system is modeled using Joules Law and Law of Thermodynamics. A Proportional-Integral-Derivative (PID) controller was tuned and the PID parameters were then used to obtain the gain of the fuzzy controllers. Simulation results confirmed that non-linear fuzzy controller has smaller overshoot and faster settling time compared to the linear fuzzy controller and PID controller, although an extra derivative gain may be needed for the non-linear fuzzy controller if the integral term is huge enough to affect the stability of the system.

This paper developed a fuzzy logic controller with a genetic algorithm control system for sensor based pipeline robot navigation in pipe environment. The fuzzy controller creates initial membership functions and fuzzy logic rules. the genetic algorithm, which automatically designs a fuzzy logic controller by modifying the position of controller membership functions and the commands given to the pipeline robot, optimizes the fuzzy controller. This GA-fuzzy system is used to control the mobile pipeline robot, which is equipped with an array of ultrasonic sensors to acquire the distance between the pipeline robot and obstacle. On the basis of this system, a strategy for combining local behavior control with global behavior control planning was proposed in this paper. Finally a pipeline robot runs in simulation environment. The experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved well enough to smoothly drive the pipeline robot in different pipe environment.

Magnetic suspension system is a typical non-linear hysteretic system. A kind of technology, which combines the permanent magnet and the electromagnetism, is presented for its characteristics and can obviously reduce power consumption. In order to solve the impact of the change of current in magnet coils on the overall rigidity, a double closed loop control system, which introduces the current loop into the position loop for forming a position and current double closed loop control system, is adopted. A fuzzy PID controller is designed to meet the requirement of suspended beams on the machine tool according to the process characteristics of the machine tool. Compared with the results of the simulation, it can be shown that fuzzy PID controller improves the stiffness of the suspension system which can meet the precision requirement of the magnetic suspension beams system on the machine tool.

In order to solve the poor dynamic characteristics and adjustment effect of traditional voltage filter control system of welding machine. In this paper, the mathematical model of the voltage filter control system of welding machine is established, and the fuzzy control rule is applied to the Voltage Control System, and two improved fuzzy control schemes including fuzzy-PID compound control and fuzzy adaptive control are proposed. Finally, the MATLAB software is used to compare the simulation of the three controllers. Simulation results show that fuzzy-PID compound control and Fuzzy adaptive control have better dynamic performance than traditional PID control; Compared with fuzzy-PID compound control, Fuzzy adaptive control has better steady-state performance and stronger anti-interference ability.

This paper proposes a nonlinear approach for designing traffic-responsive ramp controls using a genetic fuzzy approach. The problem is formulated as a nonlinear feedback control problem. To overcome the conventional problems of the calibration process of fuzzy controllers and improve the overall performance of ramp metering, an adaptive genetic-based algorithm is integrated into the system to periodically tune the fuzzy sets parameters. The approach thus adapts the control system automatically to changing traffic patterns. The objective of the ramp control is to minimize the total time spent in the freeway system while maintaining acceptable ramp service levels. Traffic data from a ramp study site in the Munich Autobahn (A9 motorway) was used to assess the genetic fuzzy controller using a hydrodynamic traffic model to estimate the genetic fitness. The paper concludes that adaptive fuzzy control based on genetic algorithms is expected to enhance the performance of ramp metering without compromising the cost-effectiveness associated with fuzzy controllers.

in this paper, the fuzzy control and the PID control are combined, and the fuzzy inference method is utilized to realize the on-line auto-tuning of PID parameters. By applying the fuzzy control toolbox and the Simulink in Matlab , it takes the electric steering gear as the object to simulate. By setting the fuzzy auto-tuning controller, the paper draws a conclusion that this method surpasses the conventional PID with higher control accuracy, smaller overshoot, and better dynamic performance.