Research on PMSM Speed Control System Based on Adaptive Fuzzy Control

With the vigorous development of photovoltaic industry, the research on three-phase photovoltaic grid-connected inverter is deepening. For the problem, in this article, a combination of fuzzy control methods and traditional PI control is proposed to improve the adaptive capability of the system by constructing a composite fuzzy PI control pattern. At the same time, The LCL-type three-phase grid-connected inverter is simultaneously studied and the fuzzy PI control is validated through demonstration experiments. It is shown that the fuzzy PI control method is somewhat feasible in this system.

Focusing on the passive network, this paper analyzes the discrete mathematical model and its control strategy of the passive terminal of the VSC-HVDC system on the basis of the α-β frame of axes. The method of the functioning-fuzzy-subset inference is introduced into automatic regulation of the proportional integral (PI) parameters. Based on the digital signal processor (DSP), the fuzzy PI control method is adopted and the VSC-HVDC experiment device is constructed. The experiment of constant AC voltage control based on fuzzy adaptive PI controller in transmitting power to passive network via VSC-HVDC system is developed. The experimental results show that the fuzzy controller with adaptive PI parameters for AC voltage control of VSC-HVDC system is effective.

<div class="section abstract"><div class="htmlview paragraph">This study analyzes feedback and control methods for road feel simulation in automotive steer-by-wire front steering systems based on bidirectional control. Unlike traditional road feel design methods, this research employs a force-direct feedback-position type bidirectional control structure for the SBW system. It explores the mechanism of road feel generation in Electric Power Steering systems and designs a road feel simulation algorithm based on bidirectional control. Compared to conventional methods, the force direct feedback-position type bidirectional control method enables faster and more stable simulation of road feel torque. In low-speed driving, this approach provides higher steering ease, while at high speeds, the driving stability is enhanced, and both scenarios achieve an improved road feel.</div><div class="htmlview paragraph">In the research, a complete vehicle model is established in Simulink at first, followed by a co-simulation with CarSim. A magic formula tire model and a nonlinear two-degree-of-freedom vehicle model are used to determine the wheel aligning torque. This aligning torque is converted into the desired SBW steering wheel torque through the EPS assistance characteristic curve. Subsequently, torque compensation is applied to achieve the desired road feel feedback torque. In the force-direct feedback control, sliding mode control is chosen as an alternative to PI control, while fuzzy PI control is selected over traditional PI control in position control. Simulations under various operating conditions demonstrate that this control strategy effectively tracks both torque and angle, enhancing the authenticity of road feel, as well as the ease and stability of driving. In summary, the road feels simulation method designed under the force direct feedback-position type bidirectional control structure adequately meets the driver's road feel requirements across different driving conditions.</div></div>

The development of power grid has put forward higher requirements on the performance of current sensors. Traditional current sensors already can not meet these needs of grid metering and protection. This paper proposes a fiber-optic current sensor (FOCS) based on fuzzy PI control strategy which combines fuzzy control with PI control. These parameters of fuzzy PI controller can be adjusted online at different stages of the system response. Simulation results show that the FOCS based on fuzzy PI control has a fast dynamic response and no overshoot, compared with FOCS based on traditional PI control. These advantages of accuracy in metering and rapidity in protection of power grid, can be satisfied simultaneously.

This paper presents a fuzzy PI controller as a power system stabilizer. The fuzzy PI controller has been designed in order to prepare an auxiliary signal for the excitation system of the synchronous generator. The proposed controller utilizes a combination of a fuzzy logic controller (FLC) and a PI controller, which are the most recent researches in the problem at hand. In comparison with the conventional fuzzy controllers, the proposed fuzzy PI controller takes advantage of both FLC and conventional PI controller to overcome all kind of disturbances. The damping of oscillations local modes in a power system will substantially be controlled by employing this controller as a replacement for conventional PSS. Furthermore, the presented paper considers the conventional PSS and compares its performance with respect to three proposed types of fuzzy PI controller. Correspondingly, the effects of the supplementary signals in damping oscillation have been shown. Finally, to scrutinize the effectiveness of the proposed controller in damping oscillations, a three-phase short circuit condition is studied in order to clarify the application of the developed methodology. The achieved outcomes show that the proposed controller for stabilizing power system can provide very good damping characteristic.

In order to meet the requirements of high precision and fast response of permanent magnet direct drive (PMDD) wind turbines, this paper proposes a fuzzy proportional integral (PI) controller associated with a new control strategy for wind turbine converters. The purpose of the control strategy is to achieve the global optimization for the quantization factors, ke and kec, and scale factors, kup and kui, of the fuzzy PI controller by an improved particle swarm optimization (PSO) method. Thus the advantages of the rapidity of the improved PSO and the robustness of the fuzzy controller can be fully applied in the control process. By conducting simulations for 2 MW PMDD wind turbines with Matlab/Simulink, the performance of the fuzzy PI controller based on the improved PSO is demonstrated to be obviously better than that of the PI controller or the fuzzy PI controller without using the improved PSO under the situation when the wind speed changes suddenly.

Power conditioning system (PCS) is an interface between distributed generation and utility grid. It regulates voltage, current and power transmitted from the hybrid direct fuel-cell/turbine (DFC/T) power plant to utility grid. This paper presents a self-adaptive fuzzy PI controller of three phase inverter in PCS for the DFC/T power plant. One of the main tasks of PCS for distributed generations is power flow control. Traditionally, the control scheme for grid connected inverters is PI controller. However, PI control scheme would lead to large overshoot and long response time when the load increases sharply. Thus, a hybrid fuzzy PI control scheme is proposed in this paper in order to improve the power flow control. The fuzzy controllers can adjust the PI parameters according to the voltage error and the derivative of voltage error. The overall self-adaptive tuning fuzzy PI controller can update the PI parameters in real time, which makes the power flow control much more accurate than conventional PI controllers. Analysis and design methodologies of hybrid fuzzy PI controllers are presented in this paper. The model was developed in Matlab/Simulink environment. Simulation results show that the proposed control scheme can effectively reduce the overshoot and response time compared to the conventional PI controllers.

\nAiming at the control problem of dual-motor driving servo system, in order to avoid the complicated numerical calculation of complicated control algorithm, we presented a simple fuzzy PI controller for the first time. Firstly, we explained system control principle. In view of the shortcomings of current-error negative feedback, we used speed-error negative feedback synchronous driving control strategy. Then we theoretically analyzed dual-motor synchronous driving system in detail. We proposed the conditions of dual-motor driving servo system synchronous operation for step signal and ramp signal, respectively. Finally, we presented a simple fuzzy PI controller, by combining fuzzy controller with PI controller. We analyzed the fuzzy PI control system and found it was stable. We considered two cases in simulation tests comparing fuzzy PI control with PI control. Simulation results show that fuzzy PI control has better adaptability than conventional PI control, validating the effectiveness and efficiency of the proposed control strategy.\n

Switched reluctance motors have complex problems such as strong nonlinear characteristics, variable given speed, and large load disturbances. Aiming at the problem that the switched reluctance motor cannot adapt to the changing speed and large disturbance under the traditional PI control, this paper combines the fuzzy control and the PI control to form the fuzzy-PI controller, which constitutes the fuzzy adaptive speed regulation system of the SRM. Using fuzzy control to correct system parameters in real time can improve the situation that the traditional PI direct torque control system cannot effectively control the strong nonlinearity of SRM. By accurately establishing the nonlinear model of the SRM, and using the finite element simulation software to calculate the static characteristics of the motor's flux linkage, inductance and torque, the mathematical model of the SRM speed control system is established. Then it is shown by MATLAB/Simulink simulation, compared with the traditional PI control algorithm, the fuzzy-PI control algorithm can make the SRM system achieve better control effect in the case of variable speed and large load disturbance, improve the dynamic response of the system, reduce the overshoot, and enhance the robustness.

In this work, a new adaptive extreme learning machine (ELM) neural network-based fuzzy controller is designed and simulated for implementing speed regulation in a permanent magnet synchronous motor. ELM is a neural model wherein the number of hidden neurons to be placed in the hidden layer is tuned during the process of neural network training itself. A new adaptive ELM model is developed for placing the number of hidden neurons in the hidden layer, and this new adaptive ELM is tuned with artificial bee colony (ABC) algorithm for optimizing its weight parameters and also the number of hidden neurons. Fuzzy proportional–integral (PI) controller is developed in this work in order to eliminate the steady-state error. The new adaptive ELM neural model optimized with ABC algorithm is applied to tune the input parameters of the fuzzy PI controller and also on optimizing the rules and fuzzy membership functions. The optimized adaptive ELM neural network-based fuzzy PI controller is utilized to investigate the speed regulation of permanent magnet synchronous motor (PMSM) in this work. The developed new PI controller with the PMSM is tested for its performance characteristics and to prove its validity is compared with the traditional controller and other heuristic controllers proposed in earlier literature works.

As the near space atmospheric environment is complicated and variational, aircraft electric propulsion system parameters and load characteristics of the propeller will be changed, if the control method of electric propulsion system shall not be treated, the system stability and reliability will be affected. A novel double close loops control method with current and speed was presented in this paper, PI control combined with fuzzy adaptive scheme was developed for high-performance robust speed controller, which has the advantages of fuzzy adaptive control and PI control, PI control was developed for the current controller. And the system simulation experiments were accomplished in Matlab/Simulink. The simulation results are illustrated to show that the system works well, has very good static and dynamic performance, strong robustness and self-adaptive ability than PI control.

Proportional Integral (PI) controller has attracted researchers in industrial control processes because of its simplicity and robust performance in a wide range of operating conditions. It has been used to provide proportional delay differentiation on web servers in previous work. However, PI controller cannot achieve satisfactory results due to (1) the web server's non-linearity properties, and (2) the difficulty of building an accurate model for the web server. To address these issues, a nonlinear fuzzy PI controller is proposed in this paper, which has the advantage of fuzzy controller while maintaining the simplicity and robustness of PI controller. The proposed controller are self-tuned according to the periodical online performance measurement. The experimental results demonstrate that our fuzzy PI controller outperforms the PI controller in several aspects.

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.

In this paper, a robust speed controller for an induction motor is proposed. The speed controller consists of a fuzzy sliding adaptive controller (FSAC) and a sliding mode torque observer (SMTO). The FSAC removes the problem of oscillations caused by discontinuous inputs of the sliding mode controller. The controller also provides robust characteristics against parameter and sampling time variations. Although, however, the performance of FSAC is better than the PI controller and fuzzy controller in robustness, it generates the problem of slow response time. To alleviate this problem, a compensator, which performs feedforward control using torque signals produced by the SMTO, is added. The simulation and hardware implementation results show that the proposed system is robust to the load disturbance, parameter variations, and measurement noises.

Permanent magnet synchronous motor (PMSM) is a multivariable, nonlinear and strongly coupled system. It is difficult to achieve the ideal control effect by using the basic proportional integral (PI) control method in the process of speed change and load disturbance. In this paper, an adaptive PI parameter control strategy for speed and torque is proposed. First, the optimal control parameters are obtained by the iterative learning. Then, the Adaptive Neuro-Fuzzy Inference System(ANFIS) learns from iterative learning results to achieve the system membership function parameters and control rules adjustment. Finally, the simulation results show that the system is characterized by high efficiency, high speed, high accuracy, small static response error, less response time and small overshoot. It is suitable for each torque transition and different operating speeds, which is superior to the basic PI control and fuzzy PI control with high control precision, good dynamic performance and strong anti-interference ability.