Fuzzy PI Controller Research Articles

Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network

Deflection switched reluctance motors (DSRM) are prone to chattering at low speeds, which always affects the output efficiency of the DSRM and the mechanical loss of the motor. Combining the characteristics of a traditional reluctance motor with the strong nonlinear and high coupling of the DSRM, a control system for a DSRM based on a flexible neural network (FNN) is proposed in this paper. Based on the better robustness and fault tolerance of fuzzy PI control, the given speed signal is adjusted and converted into a torque control signal. As a result, the FNN control module possesses the strong self-learning ability and adaptive adjustment ability necessary to obtain the control voltage signal. Through simulations and experiments, it was verified that the control system can run stably on DSRM and shows good dynamic performance and anti-interference ability.

PI parameter optimization of Asynchronous motor vector control based on MIGA

In motor control, the stable control of motor speed and the stability in the face of sudden torque are two important indicators to evaluate the quality of the control system. This paper proposes a multi-island genetic algorithm (MIGA) and fuzzy PI vector control algorithm. Combined with the asynchronous motor control method, the simulation test and bench test are carried out. Taking the Integral Time Multiplied by the Absolute Value of Error (ITAE) minimum as the optimization goal, the MIGA optimization algorithm is used to obtain the optimal solution for the quantization factors ke0, kec0 , proportional factors kup, kui of the fuzzy PI controller. The optimal parameters are imported into the asynchronous motor control model for simulation verification and bench test verification. The simulation test shows that the fuzzy PI control method optimized by MIGA can effectively reduce the speed rise time, overshoot and steady-state error. When the load is suddenly applied, the speed drop is 5.16 r/min, and the adjustment time is 0.015s. The bench test shows that: under the changing load, the change trend of the speed curve of the simulation and the test is basically the same, which proves the correctness and effectiveness of the algorithm proposed in this paper.

Power quality improvement of grid-connected photovoltaic systems using PI-fuzzy controller

To ensure enhanced reliability and availability of electricity to consumers, grid-connected photovoltaic systems need to improve their power quality, this paper uses a three-phase five levels cascaded H-bridge inverter in grid-connected mode to improve the flexibility and efficiency of the photovoltaic system. Each photovoltaic (PV) array in the proposed system has a maximum power point tracker (MPPT) to extract the PV array maximum power point for a particular irradiance and temperature and reduce the mismatch that causes an imbalance in the power sent from the inverter to the main grid. The fuzzy logic controller is used to tune the proportional-integral (PI) controller to regulate the current and voltage of the grid-connected inverter by changing the gain of the PI controller (Kp, Ki) to obtain a fast response and improve the power quality of the system despite different load disturbances and inputs. The system was simulated in MATLAB/Simulink, and the results show the superiority of the proposed control unit, in which a pure and stable sine output voltage and current waveforms. Finally, the total harmonic distortion (THD) is improved to reach 3.81% based on the fuzzy PI controller, while 7.77% is based on the PI controller.

Study on Decoupling Control Strategy of Redundant Parallel Hybrid Vibration Isolation Platform

Background: The vibration isolation platform (VIP) can greatly reduce the risk of damage to dynamic equipment caused by external vibration interference and provide the most stable and reliable working environment for dynamic equipment. Objective: Aiming at the problems of small vibration isolation dimension, low bearing capacity, serious motion coupling, and narrow vibration isolation bandwidth in the field of vibration isolation of dynamic load equipment, a hydraulic redundant six degrees of freedom parallel vibration isolation platform (PVIP) with active and passive composite vibration isolation support is proposed. Methods: The dynamic coupling model of active and passive hybrid vibration isolation (APHVI) of redundant PVIP is established, and the matrix diagonal decoupling control strategy and fuzzy PI control theory are combined. Analysis of passive vibration isolation performance and APHVI performance of VIP is done. Results: The simulation results show that the effect of APHVI based on fuzzy PI decoupling control is better than that of passive vibration isolation and matrix diagonal decoupling control. Conclusion: Fuzzy PI control can effectively attenuate more than 98 % of the disturbance, significantly improve the vibration isolation performance and decoupling effect of the platform, and expand the effective vibration isolation bandwidth.

Photovoltaic emulator using error adjustment fuzzy logic proportional-integral controller

The photovoltaic (PV) technology has been increasingly used in our energy generation. Therefore, it is essential to have a good PV testing facility during the development process. The PV emulator (PVE) is a voltage or current source that mimic the current-voltage characteristic as a PV module that requires proper control strategy to work. The resistance feedback method (RFM) control strategy has many good attributes, except the transient response, which is caused by the proportional-integral (PI) controller. This paper proposed a new fuzzy logic PI (FLPI) controller to improve the transient performance of the RFM PVE. It is based on the error adjustment method that founded on the transient state and load of the PVE. The performance of the proposed PVE is compared with the original PVE that used RFM with the PI controller. The finding of the research shows that the transient performance of the proposed PVE has improved 2.3 times compared to the original PVE without affecting its accuracy.

Adaptive Fuzzy Proportional Integral Sliding Mode Control for Two-Tank Interacting System

This paper presents an adaptive fuzzy proportional integral sliding mode control (AFPISMC) for two-tank interacting system (TTIS). In order to maintain the desired liquid level of the TTIS and meet the reference values for attenuated chattering problems, this paper proposes a combination of a sliding mode control (SMC) with a proportional integral (PI) sliding surface and a fuzzy inference system. Fuzzy logic and the universal approximation theorem of fuzzy systems are used to approximate the uncertain function in the PISMC. The stability of the control system is proved by the Lyapunov theory. The simulation results of the proposed method in MATLAB/Simulink were compared to a fuzzy control, a sliding mode control with conditional integrals, a fuzzy-PID control, and a conventional PID control. The comparison results showed that the proposed controller was most effective when the rising time reached 0.2375 s, the percent of overshoot was 0%, the steady state error converged to zero, the settling time was 0.4612 s, and chattering was reduced.

Fuzzy based MPPT Control System in Grid Connected Solar Plant

The increase in demand for the electricity system is increasing. Thus, enviromentally friendly technologies such as new and renewable energy are needed, one of which is a power plant with Photovoltaic as the main component. Solar power plant has enviromentally friendly properties. The design of this solar power plant uses Fuzzy Logic Controller and PI Controller methods, where both methods are used to find the maximum power peak point used for fast charges and MPPT, which is simulted using MATLAB/Simulink. The things presented in developing the generating system using these two methods are solar radiation intensity, temperature and Photovoltaic module. The model developed will make it possible to examine the characteristics of solar power plant and predict the amount of energy generated by solar panels in a particular location and compare the effectiveness of Fuzzy Logic and PI methods. The results shows that Fuzzy method produces a boost converter output voltage in the range of 600 V with result that are still not good and less stable, but the irradiation of 100 W/m2 voltage is stable, while the boost converter output voltage controller by the PI method is very good and stable in all conditions. Radiation and voltage are also in the range of 600 V.

Design of Solar and Battery Hybrid Electric Vehicle Charging Station

Microgrids have emerged as a new way to integrate and use large-scale distributed power to provide electricity to isolated locations, such as islands and villages. A utility engineering microgrid structure is designed to provide fast response and efficient operation by analyzing hybrid power systems and fuzzy-PI-based control techniques. This project aims to design a solar photovoltaic (PV)-battery-diesel generator-based hybrid power system employing a Sepic converter. The Perturb and Observe algorithm is used for maximum power extraction from solar panel. A lithium-ion battery is utilized as a storage battery in this system, which may be charged by the PV source. When the storage battery is depleted and PV generation is unavailable, the charging station uses the grid and a diesel generator. The generated voltage is synchronised at the Point of Common Coupling (PCC) to achieve continuous charging. The simulation is done in MATLAB/Simulink. The simulation output of using the PI control strategy is compared with the fuzzy-PI control strategy. This demonstrates the efficacy of the fuzzy-PI control method.

Hybrid dragonfly algorithm and pattern search optimized adaptive fuzzy PID controller for frequency regulation of multi microgrid system using Henkel matrix based reduced‐order model

AbstractThis paper proposes frequency regulation of multi microgrid systems. The system under study consists of wind energy, solar energy and energy storage system operating in dissimilar combinations to establish equilibrium among the production and consumer demand. In this proposed work, stability analysis of the concerned model is studied using the Henkel matrix approach of the reduced‐order model. Again, an attempt is made to apply the adaptive fuzzy proportional integral and derivative (AFPID) controller for frequency control of the system. The optimal gains of the controllers are tuned by reducing the integral time absolute error using a hybrid dragonfly algorithm supported by the pattern search (hDAPS) algorithm. The efficacy of the proposed adaptive fuzzy controller is examined with fuzzy PID and PID for different cases of the concern system. The efficacy of the hDAPS algorithm is justified over the genetic algorithm, dragonfly algorithm and particle swarm optimization. The robustness study also reveals that the systems are offering reduced overshoot and undershoot under different scenarios of load and source environment. To demonstrate expediency of the proposed controller sensitivity analysis was carried out under different loading conditions. In addition, to show the efficiency of the hDAPS tuned AFPID controller, the outcomes were associated with other artificial intelligence methods reported in the literature.

Comparative application of the self-adaptive fuzzy-PI controller for a wind energy conversion system connected to the power grid and based on DFIG

Comparative application of the self-adaptive fuzzy-PI controller for a wind energy conversion system connected to the power grid and based on DFIG

Vibration Suppression Method Based on PI Fuzzy Controller Containing Disturbance Observe for Dual-flexible Manipulator with an Axially Translating Arm

Vibration Suppression Method Based on PI Fuzzy Controller Containing Disturbance Observe for Dual-flexible Manipulator with an Axially Translating Arm

Fuzzy-PI Control for Buck Converter Output Voltage Stabilizer

This paper presents a hybrid control between Fuzzy and PI to solve the problem of stability of the output voltage on the Buck Converter, especially in small power applications. This paper makes two main contributions. The first is the design of a simple Fuzzy-PI output voltage control design of a buck converter in Matlab. The second is the result of comparing the response of the traditional PI control system with the designed Fuzzy-PI hybrid control. The method used is a simulation with 3 stages. This process starts from the design of the Fuzzy-PI controller, simulation test with 3 scenarios, and verification with a comparison, namely the PI controller. The purpose of the simulation is to see the response of the system to changes in input voltage, reference voltage, and load resistance. Based on the test results, it is known that the Fuzzy-PI control is better than the PI control in maintaining the stability of the buck converter output voltage at every parameter change.

A driving-style-oriented adaptive control strategy based PSO-fuzzy expert algorithm for a plug-in hybrid electric vehicle

The propelling torque of a plug-in hybrid electric vehicle (PHEV) is provided by the engine and driving motor. However, the driver style has a great influence on the fuel economy performance of the PHEV. To address this issue, this study proposes a novel driving-style-oriented adaptive control strategy by using Particle Swarm Optimization (PSO) combined with Fuzzy expert algorithm. The first is development of a driving style recognition method by taking advantage of the Fuzzy expert algorithm. The four input parameters of Fuzzy expert system are derived by Principal Component Analysis based the collected data of driving behavior real tests. Second, a novel driving-style-oriented adaptive control strategy is developed based the Equivalent Consumption Minimization Strategy (ECMS) integrated with adaptive Equivalent Factor (EF). The EF is updating according to the corresponding driving style by using the PI-Fuzzy controller, and the PSO algorithm is applied to further optimize the fuzzy rules for tuning the PI factors. Finally, in combination with the above efforts, a novel driving-style-oriented adaptive control strategy based PSO-Fuzzy Expert Algorithm has been further established accordingly. A normal ECMS with constant EF and an adaptive ECMS for neural network to recognize the driving style is applied to compare with the proposed strategy. The validation results indicate that the strategy significantly decreasing the fuel consumption with the equivalent fuel consumption is reduced by 19.3%, and the proposed strategy has the best performance.

A LOW-COST APPROACH TO DATA-DRIVEN FUZZY CONTROL OF SERVO SYSTEMS

Servo systems become more and more important in control systems applications in various fields as both separate control systems and actuators. Ensuring very good control system performance using few information on the servo system model (viewed as a controlled process) is a challenging task. Starting with authors’ results on data-driven model-free control, fuzzy control and the indirect model-free tuning of fuzzy controllers, this paper suggests a low-cost approach to the data-driven fuzzy control of servo systems. The data-driven fuzzy control approach consists of six steps: (i) open-loop data-driven system identification to produce the process model from input-output data expressed as the system step response, (ii) Proportional-Integral (PI) controller tuning using the Extended Symmetrical Optimum (ESO) method, (iii) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller in terms of the modal equivalence principle, (iv) closed-loop data-driven system identification, (v) PI controller tuning using the ESO method, (vi) PI controller parameters mapping onto parameters of Takagi-Sugeno PI-fuzzy controller. The steps (iv), (v) and (vi) are optional. The approach is applied to the position control of a nonlinear servo system. The experimental results obtained on laboratory equipment validate the approach.

Research on Speed Regulation performance of switched reluctance Motor based on nonlinear active disturbance rejection

Abstract The state has vigorously advocated new energy electric vehicles in recent years. Compared with other motors, switched reluctance motor has the advantages of superior speed regulation performance, low production cost and strong controllability. However, due to its double salient structure, it is easy to produce magnetic circuit saturation, and its control is non-linear, which is easy to lead to large torque ripple and strong noise. To solve this problem, a control strategy combining active disturbance rejection control and direct torque is proposed to suppress torque ripple. A simulation model is built and calculated with simulation software. At the same time, it is compared with PI control and fuzzy PID control under direct torque control. Finally, the simulation experiment is carried out on a 6 / 4 motor. The simulation results also show that the direct torque control strategy based on active disturbance rejection control technology can effectively control the torque and speed of switched reluctance motor.

Design of an Electronic Load Controller for Micro Hydro Power Plant Using Fuzzy-PI Controller

Design of an Electronic Load Controller for Micro Hydro Power Plant Using Fuzzy-PI Controller

An adaptive robust fuzzy PI controller for maximum power point tracking of photovoltaic system

The transduction of solar energy to electrical energy requires Solar Photovoltaic (SPV) systems, which must be operated at Maximum Power Point (MPP) to extract maximum possible power. Being dependent on environmental factors such as irradiation and temperature, the MPP and, therefore, the SPV system’s performance is nonlinear. A Maximum Power Point Tracking (MPPT) controller is usually employed, which guides the SPV systems to work at MPP. For this task, in this paper, an Adaptive Robust Fuzzy Proportional-Integral (ARFPI) controller for MPPT of an SPV system is proposed. The proposed ARFPI controller parameters have been tuned using Particle Swarm Optimization by minimizing an equal-weighted combination of Integral of the Time-Weighted Absolute Error (ITAE) and Integral of the Absolute Error (IAE). In this combination, ITAE penalized long-term errors offering a faster response, while IAE penalized aggregate errors offering lower ripples. The MPPT performance of the proposed controller has been assessed using undershoot and steady-state ripples for several varying irradiance and temperature profiles with real-world data. Further, to assess its relative performance, it has also been benchmarked against traditional MPPT techniques, i.e., perturb and observe, incremental conductance, and PID controller. The presented investigations revealed clear superiority (reduced ripples and undershoot) of ARFPI controller, and therefore it is concluded to be a potential MPPT controller for the SPV system.

Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control

The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users.

Self-tuning Fuzzy-PI controller for load frequency control analysis with the integration of wind energy

ABSTRACT Optimal novel Self-tuning Fuzzy-PI (STFPI) controller has been introduced to analyze the LFC (Load Frequency Control) in a multi-unit unified system. The finest constraints of this suggested STFPI controller are found using quasi-opposition-based Jaya (QOB Jaya) algorithm. The investigation is performed over a two area multi-generation unified system. The examined model considers both renewable-type (wind) and conventional-type (thermal, diesel, & hydro) generating sources. The efficacy of the proposed controller is confirmed by (i) varying the size of loading, (ii) changing the constraints of designed system, and (iii) injecting arbitrary loading into the power system. It has been derived that the value of the objective function (Integral Time Absolute Error) with QOB Jaya tuned STFPI controller is improved by 95.44%, 85.17%, and 50% as compared to PSO-tuned PID Controller, QOB Jaya-tuned FPI Controller, and Jaya-tuned STFPI Controller, respectively. The supremacy of the projected controller is proved by doing comparisons among the responses of fuzzy-PI controller and a pre-published outcome such as PSO (particle swarm optimization) tuned PID (proportional-integral-derivative) controller.

Hybrid Fuzzy-PI and ANFIS Controller Design for Rotor Current Control of DFIG Based Wind Turbine

Wind energy generation provides one of the best and economical solution to the growing power demand. Doubly Fed induction generator is one of the most important variable speed wind generators. Integrating advanced controllers improves its performance and efficiency. The purpose of this research work is to optimize the wind energy conversion and efficient wind power extraction by controlling rotor current. Four controllers that are PI, Fuzzy, Hybrid Fuzzy-PI and Neural network based Adaptive Neuro Fuzzy controller are designed and implemented on DFIG. Controllers’ performance is assessed in terms of transients in rotor current i.e., percentage overshoot, settling time and steady state error under varying wind speed operation. Comparison of the results demonstrates that Adaptive Neuro Fuzzy controller outperforms as compared to that of Hybrid Fuzzy-PI, Fuzzy and conventional PI regarding transient response and maximum power extraction efficiency.