Adaptive Fuzzy PI Controller Research Articles

Design and implementation of a robust fuzzy adaptive PI controller for inherently unstable systems

Design and implementation of a robust fuzzy adaptive PI controller for inherently unstable systems

Adaptive fuzzy PI control for the dynamic operation of the transcritical CO2 thermal system in electric vehicles

Process control is essential for ensuring the stable and efficient operation of EVs’ transcritical CO2 thermal management systems. Traditional PI control generally shows an insufficient dynamic response for the variable and complex operating conditions and then causes fluctuant process control. This study proposed an adaptive fuzzy PI control strategy to provide real-time varying PI gains for fast-tracking response and fluctuation suppression, considering the compressor discharge pressure, coolant, and air outlet temperature. A high-fidelity transcritical CO2 thermal model in EVs was first built in the Amesim platform to simulate real-time operation, and the adaptive fuzzy PI control algorithm was then developed to optimize the process control. The co-simulation is conducted to validate the effectiveness and adaptability of the proposed control methodology by comparing it with a benchmark traditional PI control strategy. The results indicated that the proposed adaptive fuzzy PI could provide a 9.22 % overshoot suppression for discharge pressure, a maximal 22.29 % overshoot inhibition and 86 s settling time reduction for the air outlet temperature, and a 25 % settling time reduction for the coolant temperature control. In addition, the fuzzy PI improved the start-up characteristics by avoiding excessive response. Such findings demonstrate that the proposed fuzzy PI control algorithm can effectively optimize the dynamic operation of the transcritical CO2 thermal system in EVs.

Intelligent Control of Solar LED Street Lamp Based on Adaptive Fuzzy PI Control

As road traffic develops, energy-saving and efficient street lights have become a key research field for relevant professionals. To reduce street lights energy consumption, a fireworks algorithm is used to optimize the membership function parameters of fuzzy control and the initial parameters of PI control. A fireworks algorithm improved adaptive fuzzy PI solar LED street light control system is designed. The results showed that in the calculation of Root-mean-square deviation and Mean absolute error, the Root-mean-square deviation of the adaptive fuzzy PI control system improved by the fireworks algorithm was 0.213, 0.258, 0.243, 0.220, and the Mean absolute error was 0.143, 0.152, 0.154, 0.139, respectively, which proved that the prediction accuracy was high and the stability was good. In the calculation of the 1-day power consumption of the solar LED intelligent control system, the average power consumption of the designed solar LED intelligent control system was about 2000W, which was 25.9%, 47.4%, and 42.9% lower than the other three control methods, respectively. This proves that its energy consumption is low, and its heat generation is low, and the battery service life is long. The research and design of an adaptive fuzzy PI control solar LED street light intelligent control system has good performance, which can effectively achieve intelligent management and energy conservation and emission reduction in smart cities.

Design of integral sliding mode control and fuzzy adaptive PI control for voltage stability in DC microgrid

This paper introduces a novel control strategy that merges integral sliding mode control with fuzzy adaptive PI control. This hybrid approach maximizes the benefits of both techniques to ensure voltage stability in DC microgrid. Firstly, a mathematical model characterizes the DC–DC boost converter. Subsequently, a sliding surface, incorporating an integral term, is employed to regulate the converter’s output voltage and current errors. To address uncertainties stemming from factors like input inductance and output capacitance, a dynamic sliding mode controller is formulated. The proposed sliding mode control scheme significantly reduces the time required for voltage stability, curbs system oscillations, and showcases robustness. Furthermore, the inclusion of fuzzy adaptive PI control aids in refining the voltage deviation signal and droop resistance. This enhancement improves the precision of the error tracking system. Finally, the effectiveness of this strategy is demonstrated through MATLAB simulations, supported by experimental validation and analysis. The findings reveal that this control strategy efficiently accelerates the convergence of DC microgrid voltage to a stable state.

Adaptive fuzzy-based stability control and series impedance correction for the grid-tied inverter

The regenerative braking in the tram allows the energy to be returned to the power grid through a power inverter. Since the inverter location between the tram and the power grid is not fixed, resulting in a wide variety of impedance networks at grid coupling points, posing a severe threat to the stable operation of the grid-tied inverter (GTI). By independently changing the loop characteristics of the GTI, the adaptive fuzzy PI controller (AFPIC) can adjust according to different impedance network parameters. It is challenging to fulfill the stability margin requirements of GTI under high network impedance since the PI controller has phase lag characteristics. A correction method of series virtual impedance is proposed, which connects the inductive link in a series configuration with the inverter output impedance, correcting the inverter equivalent output impedance from resistance-capacitance to resistance-inductance and improving the system stability margin. Feedforward control is adopted to improve the system's gain in the low-frequency band. Finally, the specific series impedance parameters are obtained by determining the maximum network impedance and setting the minimum phase margin of 45°. The realization of virtual impedance is simulated by conversion to an equivalent control block diagram, and the effectiveness and feasibility of the proposed method are verified by simulation and a 1 kW experimental prototype.

Improving robotic hand control via adaptive Fuzzy-PI controller using classification of EMG signals

Robotic or prosthetic organs are designed to have the maximum similarity to human organs. This paper aims to improve robotic hand control via an adaptive Fuzzy-PI controller using EMG signals. The data is collected from the FDS and FPL muscles of the forearm of five individuals who performed eight movements. Then, appropriate filters are used to eliminate the noise of the signals, and MAV, VAR, and SE features are extracted. Based on MAV and VAR, classification is carried out using DA, KNN, and SVM. With an average accuracy, specificity, and sensitivity of 90.69%, 94.64%, and 62.10%, SVM is a better choice for movement detection. Following the movement detection by SVM, an appropriate reference signal is sent to the controller. The reference signal is the angle change of the fingers during the movement. All the eight gestures are modeled in a new way through these angles. The adaptive fuzzy-PI controller is used to control a robotic hand model with fifteen degrees of freedom. It has the advantages of learning from human experiences and adapting to environmental changes. The performance of the controller is evaluated in two ways. One is the comparison of the fuzzy-PI with the PI by RMSE. The average RMSE for eight movements using the fuzzy-PI is 1.6067, and for the PI, 5.0082. These results show that the fuzzy-PI controller performs better than the PI. Another new evaluation way presented in this paper is comparing the EMG signal features with the robotic hand movement signal features in terms of RMSE. The small RMSE values indicate that the EMG signal and robotic hand movement data features are significantly similar. Therefore, it can be concluded that the robotic hand controlled by the proposed controller is notably identical to the human hand.

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.

Adaptive Fuzzy Logic Controller for Harmonics Mitigation Using Particle Swarm Optimization

An excessive use of non-linear devices in industry results in current harmonics that degrades the power quality with an unfavorable effect on power system performance. In this research, a novel control technique-based Hybrid-Active Power-Filter (HAPF) is implemented for reactive power compensation and harmonic current component for balanced load by improving the Power-Factor (PF) and Total–Hormonic Distortion (THD) and the performance of a system. This work proposed a soft-computing technique based on Particle Swarm-Optimization (PSO) and Adaptive Fuzzy technique to avoid the phase delays caused by conventional control methods. Moreover, the control algorithms are implemented for an instantaneous reactive and active current (I_d-I_q) and power theory (Pq0) in SIMULINK. To prevent the degradation effect of disturbances on the system's performance, PS0-PI is applied in the inner loop which generate a required dc link-voltage. Additionally, a comparative analysis of both techniques has been presented to evaluate and validate the performance under balanced load conditions. The presented result concludes that the Adaptive Fuzzy PI controller performs better due to the non-linearity and robustness of the system. Therefore, the gains taken from a tuning of the PSO based PI controller optimized with Fuzzy Logic Controller (FLC) are optimal that will detect reactive power and harmonics much faster and accurately. The proposed hybrid technique minimizes distortion by selecting appropriate switching pulses for VSI (Voltage Source Inverter), and thus the simulation has been taken in SIMULINK/MATLAB. The proposed technique gives better tracking performance and robustness for reactive power compensation and harmonics mitigation. As a result of the comparison, it can be concluded that the PSO-based Adaptive Fuzzy PI system produces accurate results with the lower THD and a power factor closer to unity than other techniques.

Research on the Intelligent Control Strategy of the Fuel Cell Phase-Shifting Full-Bridge Power Electronics DC-DC Converter

With the aggravation of energy problems, the development and utilization of new energy has become the focus of all countries. As an effective new energy, the fuel cell has attracted the attention of scholars. However, due to the particularity of proton exchange membrane fuel cell (PEMFC), the performance of traditional PI controlled phase-shifted full-bridge power electronics DC-DC converter cannot meet the needs of practical application. In order to further improve the dynamic performance of the converter, this paper first introduces several main topologies of the current mainstream front-end DC-DC converter, and analyzes their performance in the fuel cell system. Then, the operation process of the phase-shifted full-bridge power electronics DC-DC converter is introduced, and the shortcomings of the traditional PI control are analyzed. Finally, a double closed-loop adaptive fuzzy PI controller is proposed, which is characterized by dynamically adjusting PI parameters according to different working states to complete the intelligent control of phase-shifted full-bridge DC-DC converter. The simulation results in MATLAB/Simulink show that the proposed algorithm has good a control effect. Compared with the traditional algorithm, the overshoot and stabilization time of the system are shorter. The algorithm can effectively suppress the fluctuation of the output current of the fuel cell converter, and is a very practical control method.

Fuzzy Logic Decoupling Control of Real and Reactive Power in Grid-Connected Photovoltaic Power System based a Seven Levels Inverter Linked to a Three-Stage Boost Circuit

ABSTRACT The paper presents a detailed modelling, simulation, and control of the real and reactive power flows in the Grid-Connected Photovoltaic Power system (GCPV) based on seven-phase voltage source inverter linked to a three-stage boost circuit interfacing a photovoltaic (PV) generation system to the power grid. Synchronisation of the inverter and grid AC waveforms is achieved using a phase-locked-loop (PLL) circuit. An effective decoupling strategy based on fuzzy logic controllers (DFLC) is designed to eliminate the interaction between the two current components. The DC-link voltage is controlled using a closed loop scheme based on a clamping bridge and an adaptive fuzzy logic PI controller (AFLC-PI). The overall model is implemented in Matlab and Simulink/SimPowerSystems toolboxes. Simulations results with the PV system operating with real irradiance data are presented to demonstrate the performance of the proposed decoupling and control strategies under different conditions of the power grid.

Adaptive fuzzy PI controller for permanent magnet synchronous motor drive based on predictive functional control

The high-performance speed control is hard to achieve for the permanent magnet synchronous motor (PMSM) drive system under uncertain disturbances, input saturation, and system delay. To address this challenge, a novel adaptive fuzzy proportional-integral (AFPI) control scheme is investigated in this paper. An anti-saturation PI (ASPI) controller is proposed serving as the main control unit that drives PMSM to track the desired profiles, which is optimized online by a designed adaptive fuzzy tuner. Specifically, together with the parameter update constraints, an improved anti-integral saturation mechanism is suggested for the ASPI controller. Meanwhile, the adaptive fuzzy tuner with the self-tuning input domain enhances the optimization ability to effectively tackle the system uncertainties. Then, a predictive functional control method is incorporated into the tuner to mitigate the control lag caused by error-driven characteristics, which ensures the dynamic tracing control performance in the presence of system delay. The stability and the H∞ robustness of the AFPI controller are guaranteed based on d-decomposition theory. Simulations and real-time experiments are performed, and the results illustrate that the designed scheme has superior performance in terms of dynamic tracking control and anti-disturbance capabilities as compared to conventional methods.

Torque ripple and noise control of switched reluctance motor using an adaptive fuzzy PI control with the aid of AR algorithm

In recent days switched reluctance motor is widely used for numerous industrial applications due to its simple structure, minimum cost and maximum efficiency. Regardless of numerous exclusive benefits of the switched reluctance motor (SRM), acoustic noise of this motor is high and it is important to accomplish more analysis on the noise lessening, which is the primary goal of this paper. The major causes of acoustic noise in a SRM are torque ripple and radial magnetic force. Since radial magnetic force is highly influential by the design of motor, torque ripple control is analysed in this article for acoustic noise control. Torque ripple control of SRM is proposed using optimization in direct torque control (DTC) method. Nowadays, optimisation plays a crucial role in motor drives for enhanced control. In this paper, artificial raindrop algorithm is proposed in DTC of SRM to minimise torque ripple. Performance of proposed ARA based DTC of four-phase 8/6 SRM is analysed using Matlab and compared with the performance of fuzzy gain scheduling PI controller based DTC.

Ripple Attenuation for Induction Motor Finite Control Set Model Predictive Torque Control Using Novel Fuzzy Adaptive Techniques

Finite control set model predictive torque control (FCS-MPTC) strategy has been widely used in induction motor (IM) control due to its fast response characteristic. Although the dynamics of the FCS-MPTC method are highly commended, its steady-state performance—ripple deserves attention in the meantime. To improve the steady-state performance of the IM drives, this paper proposes an improved FCS-MPTC strategy, based on a novel fuzzy adaptive speed controller and an adaptive weighting factor, tuning strategy to reduce the speed, torque and flux ripples caused by different factors. Firstly, a discrete predicting plant model (PPM) with a new flux observer is established, laying the ground for achieving an FCS-MPTC algorithm accurately. Secondly, after analyzing the essential factors in establishing a fuzzy adaptive PI controller, with high ripple suppression capacity, an improved three-dimensional controller is designed. Simultaneously, the implementation procedures of the fuzzy adaptive PI controller-based FCS-MPTC are presented. Considering that a weighting factor must be employed in the cost function of an FCS-MPTC method, system ripples increase if the value of the weighting factor is inappropriate. Then, on that basis, a novel fuzzy adaptive theory-based weighting factor tuning strategy is proposed, with the real-time torque and flux performance balanced. Finally, both simulation and hardware-in-loop (HIL) test are conducted on a 1.1 kW IM drive to verify the proposed ripple reduction algorithms.

10 kW Optimization of grid-connected wind power system and its application in Xiamen

Coastal areas not only have large annual average wind speed, but also have more days of strong winds in a year. Xiamen >8 gale days, 22.4 days a year, so it is very suitable for wind power generation. This design is 10 kW wind grid-connected power generation system, each branch is composed of permanent magnet synchronous motor, rectifier, boost module, inverter and filter, 20 groups are used in parallel to form 10 kW system. Choose a single permanent magnet synchronous wind turbine power of 500 W, need 20. System DC voltage :12 V, when the power is fixed, the proper lifting of DC voltage is helpful to reduce the current, thus preventing the IGBT from causing device damage due to overcurrent. A two-dimensional fuzzy controller is used to select the outer loop error and error differential signal as input variables. In order to verify the correctness of fuzzy adaptive PI control strategy, a system simulation model is built.

The black-start capability improvement of VSC-based HVDC transmission system using fuzzy-adaptive PI controller

ABSTRACT The High Voltage DC Transmission (HVDC) transmission system commissioned in 1954 was planned for meeting the objectives like economy, faster recovery rate, frequency conversion, bulk power transmission, rapid controllability from sending and receiving end and so on. These objectives are difficult to achieve easily with the HVAC system. Among these, quicker reccovery rate, which is also called black start capability during fault and maintenance, is considered in this paper. This is achieved effectively when real power is controlled from sending end, while the dc link voltage is controlled at the other end. For this, better control schemes from both sending and receiving end, fast acting phase locked loop (PLL) and faster and intelligent adaptable controllers play a vital role. Hence, new PLL, fuzzy-adaptive PI with faster and effective control schemes are proposed to improve the black start rate of HVDC links. The performance of the proposed system is verified with traditional PI with conventional technique and with fuzzy-adaptive PI controller. The results are verified using MATLAB/SIMULINK software.

Design of Fuzzy Adaptive Pi Controller for Inherently Unstable System

Desired performance of any process depends on the control action taken by the controller. Choice of control structure and the design approach is mainly concerned with a priori knowledge of the process dynamics governed by any system, its limitations to achieve desire response and calculation of system state at different instant of time. Many practitioners and researchers in the field of control agree that controller design can be quite laborious in cases where the plant is unstable. The controller synthesis becomes a challenge for such systems because there are certain design and performance limitations that narrow the range of feasible solutions. These constraints reflect imperfect controller design which may work satisfactorily in one situation and may fail completely in other. Adaptive control where the controller parameters adjust them self as per the system requirements, is a candidate solution for such problem. This paper mainly focuses on design of model reference adaptive control based PI (Proportional-Integral) controller for unstable system. Adaptation mechanism is developed by fuzzy logic which makes the controller more versatile and reduces the design requirements. Results are compared with the conventional controller tuned with Ziegler-Nichols method. Detailed simulation Study and experimental validation shows the effectiveness of proposed Fuzzy Adaptive PI controller to control the unstable system for obtaining the desired performance out of it.

Improvement of adaptive fuzzy control to adjust speed for a doubly fed induction motor drive (DFIM)

This paper presents the doubly fed induction motor (DFIM) speed control using adaptive fuzzy logic PI (AFLPI) controller to give better dynamic performances. Before the advent of modern technology, integral proportional based current controller is usually used due to its simplicity. But, the performance of closed-loop control is largely influenced by this type of speed and torque controllers used, as long as the PI controllers suffer from tuning problem. To overcome the problem, a new technique AFLPI based speed controller for direct field oriented control fed DFIM to get fast speed response and to minimize the torque ripple. The application of this type of control is very satisfactory to replace the conventional PI controller and, even the fuzzy logic PI (FLPI) controller. The performance of the field oriented controlled DFIM drive has simulated at different operating conditions using the AFLPI controller and the obtained results are compared with FLPI controller and conventional PI controller. Accordingly, an improvement in dynamic and robustness is clearly appears in AFLPI controller simulation results compared to the others aforementioned controllers. Simulation Results are presented for the three techniques using Matlab/Simulink to prove the dynamic performances and robustness.

Integrated Avoid Collision Control of Autonomous Vehicle Based on Trajectory Re-Planning and V2V Information Interaction.

An integrated longitudinal-lateral control method is proposed for autonomous vehicle trajectory tracking and dynamic collision avoidance. A method of obstacle trajectory prediction is proposed, in which the trajectory of the obstacle is predicted and the dynamic solution of the reference trajectory is realized. Aiming at the lane changing scene of autonomous vehicles driving in the same direction and adjacent lanes, a trajectory re-planning motion controller with the penalty function is designed. The reference trajectory parameterized output of local reprogramming is realized by using the method of curve fitting. In the framework of integrated control, Fuzzy adaptive (proportional-integral) PI controller is proposed for longitudinal velocity tracking. The selection and control of controller and velocity are realized by logical threshold method; A model predictive control (MPC) with vehicle-to-vehicle (V2V) information interaction modular and the driver characteristics is proposed for direction control. According to the control target, the objective function and constraints of the controller are designed. The proposed method’s performance in different scenarios is verified by simulation. The results show that the autonomous vehicles can avoid collision and have good stability.

Adaptive and Fuzzy PI Controllers Design for Frequency Regulation of Isolated Microgrid Integrated With Electric Vehicles

The number of electric vehicles and renewable energy resources integrated into the power system is increasing day by day. The objective behind the development of electric vehicles and renewable energy sources is to build a sustainable and green power system. The renewables either don't possess system inertia or have less system inertia, therefore, they don't effectively respond to the load variations. The battery storage system of electric vehicles is used as the first line of defense to counter the effect of load/frequency variations and make the system stable. As active power is inversely proportional to the system frequency, for this purpose electric vehicles are included in the microgrid environment. In this paper, an isolated microgrid having a reheat turbine system, wind turbine system, photovoltaic system, and electric vehicles is studied. The output of the renewables is not controlled to utilize its maximum output power. Therefore, adaptive droop control and fuzzy PI control mechanisms are implemented to cater to the frequency variations of the isolated microgrid; the former regulates the power of electric vehicles while maintaining the energy needs of each EV and the later controls the output power of reheat turbine system according to the frequency variation. Furthermore, the genetic algorithm optimization toolbox is utilized to optimize the parameters of the adaptive and fuzzy PI controllers. The proposed model is developed in MATLAB/Simulink which shows that these control techniques effectively sustained the system frequency of isolated microgrid in the desired limits.

A Novel Double Closed-loop Control Method for Single-phase PWM Rectifier

Abstract The research object is the single-phase PWM rectifier in this paper. The goal of DC voltage dynamic response speed improvement and unit power factor realization is the rectifier oriented. Based on current inner loop DQ decoupling control and voltage outer loop adaptive fuzzy PI control, a double closed-loop control method for single-phase PWM rectifier is proposed. Firstly, based on the second-order generalized integrator algorithm, the DQ current decoupling control mathematical model is established by analyzing the rectifier decoupling control. Secondly, fuzzy PI control algorithm is designed for voltage outer loop control. Finally, simulation verification is carried out. Simulation results prove that the proposed control strategy has good dynamic performance.