Robust Fuzzy Logic Controller Research Articles

A simple and easy-to-implement battery equalization strategy for photovoltaic-battery energy storage systems

Abstract For renewable energy sources such as photovoltaic (PV), energy storage systems should be prioritized as they smooth the output well. Although lithium-ion battery energy storage systems are favored for their excellent performance, the large number of batteries connected in series and parallel may lead to inconsistent battery packs, which can cause system problems. Therefore, battery equalization techniques should be employed. This article proposes a battery equalization technique suitable for PV battery energy storage systems. In terms of the equalization circuit, we propose an equalization circuit consisting of a switch-selective circuit and a Cuk circuit, which is simple and easy to expand; in terms of the equalization strategy, we adopt a highly robust fuzzy logic control to better adapt to PV battery energy storage systems, we developed a set of control rules suitable for this equalization strategy. We have investigated the principle of the proposed battery equalization technique and verified it experimentally during the battery pack's resting, charging, and discharging. The consistency of the battery pack is significantly improved as a result.

Multiobjective Reliability-Based Design Optimization of the Fuzzy Logic Controller for MR Damper-Based Structures

To devise an optimum and robust fuzzy logic controller for MR damper-based structures subjected to earthquake ground motions, the multiobjective reliability-based design optimization (RBDO) using the adaptive Kriging model is performed to determine the parameters of the fuzzy logic controller. The optimization problem is formulated with two objective functions, namely, the minimization of interstory drift and average control force of the concerned structure, and subjected to a probability constraint on structural dynamic responses under the effects of random structural stiffness and stochastic earthquake loadings. To reduce the computational cost of reliability assessment, a global Kriging model is constructed in an augmented space as a surrogate for computational evaluations. Subsequently, the trained metamodel combined with the nondominated sorting genetic algorithm (NSGA-II) is integrated into the framework of RBDO for solving the fuzzy logic control (FLC) optimization problem. The feasibility and effectiveness of the multiobjective RBDO in the FLC design are finally validated by conducting numerical simulations on both linear and nonlinear structures. As demonstrated in the linear case, the fuzzy logic controllers obtained from the multiobjective RBDO show more robustness than those derived from the multiobjective deterministic design optimization (DDO). In the nonlinear case, using the multiobjective DDO to prelocate a coarse safety domain can significantly reduce the number of samples for training the metamodel and facilitate the implementation of the multiobjective RBDO; in addition, the controlled structural performance with a specified fuzzy logic controller can be further improved by considering MR damper distribution optimization.

Design of Robust Fuzzy Logic Controller Based on Gradient Descent Algorithm with Parallel-Resonance Type Fault Current Limiter for Grid-Tied PV System

This article demonstrates a new topology for optimization of the electrical variables and enhancement of low-voltage-ride-through (LVRT) capacity of a grid-tied photovoltaic system (PS) during asymmetrical faults. The proposed topology comprises a fuzzy-logic controller (FLC) based on gradient descent (GD) optimization in association with parallel-resonance-type fault current limiter (PRFCL) as an LVRT circuitry. Gradient descent is an iterative process to minimize the objective function by updating the variable in the opposite direction of the gradient of the objective function. The PRFCL optimizes the fault variables, specifically preventing voltage sag without any transitional spikes. A 100-kW detailed model of grid-tied PS is used in MATLAB/Simulink to analyze the response of the proposed topology at the point of common coupling (PCC) and PV side variables. A keen comparative study of the conventionally adopted proportional-integral (PI) controller in association with crowbar circuitry is carried out for the justification of the proposed topology. The simulation findings of the proposed topology authenticate the optimal response of variables in terms of stability, robustness, smoothness, and fault tolerance at both the grid side and the PV side.

Vibration control of laminated composite cantilever beam operating in elevated thermal environments using fuzzy logic controller

In the present study, vibration control of laminated composite cantilever beam operating in the elevated thermal environment is achieved using combined experimental and numerical techniques. The impact hammer test is performed on the glass-epoxy cantilever beam at different temperatures. Experimentally recorded impact hammer force signals and piezoelectric accelerometer time-domain signals are processed through a system identification toolbox in MATLAB to obtain transfer functions of the plant models. A robust fuzzy logic controller is developed to accomplish the effective vibration control of a cantilever composite beam operating at different temperatures. The fuzzy logic controller with two inputs and one output is designed using the 20 if-then rules. The results are presented in both frequency and time domain, keeping the vibration attenuation of the fundamental frequency as the point of interest. The results indicate the proposed fuzzy logic control strategy can attenuate the vibrations of a cantilever composite beam for a wide temperature range.

Design an adaptive sliding mode controller for an advanced hybrid energy storage system in a wind dominated RAPS system based on PMSG

Abstract Controlling of a standalone wind dominated system under variable load conditions and fluctuating wind is a challenging task. Energy storage systems (ESSs) can play a significant role in stabilizing voltage of such systems. In this paper, a nonlinear controller is designed for a remote area power supply (RAPS) system consisting of the wind turbine based on a permanent magnet synchronous generator (PMSG), a battery supercapacitor hybrid energy storage system (HESS) and variable local load connected to the DC bus. An adaptive sliding mode controller (SMC) as a nonlinear control method is designed to improve the stability and the performance of such standalone systems under high perturbations. Moreover, the battery and supercapacitor performances are modified by embedding a filter in the adaptive SMC and proportional–integral (PI) controller. Also, a robust fuzzy logic controller (FLC) is implemented to the power generation system to track the maximum power point from the wind. The simulation results of the system with adaptive SMC reveal the superiority of the proposed controller compared with PI in the three different ESS schemes.

Tie-line Power Flow Control Method for Grid-connected Microgrids with SMES Based on Optimization and Fuzzy Logic

In an active distribution grid, renewable energy sources (RESs) such as photovoltaic (PV) and energy storage systems (e.g., superconducting magnetic energy storage (SMES)) can be combined with consumers to compose a microgrid (MG). The high penetration of PV causes high fluctuations of tie-line power flow and highly affects power system operations. This can lead to several technical problems such as voltage fluctuations and excessive power losses. In this paper, a fuzzy logic control based SMES method (FSM) and an optimized fuzzy logic control based SMES method (OFSM) are proposed for minimizing the tie-line power flow. Consequently, the fluctuations and transmission power losses are decreased. In FSM, SMES is used with a robust fuzzy logic controller (FLC) for controlling the tie-line power flow. An optimization model is employed in OFSM to simultaneously optimize the input parameters of the FLC and the reactive power of the voltage source converter (VSC) of SMES. The objective function of minimizing the tie-line power flow is incorporated into the optimization model. Particle swarm optimization (PSO) algorithm is utilized to solve the optimization problem while the constraints of the utility power grid, VSC, and SMES are considered. The simulation results demonstrate the effectiveness and robustness of the proposed methods.

Fuzzy Logic Control for Doppler Search in DSSS Systems

The study on the Doppler frequency search strategy in direct sequence spread spectrum (DSSS) systems has been addressed in this article. To reduce the estimation error and alleviate the possible false detection, a novel Doppler search algorithm that combines the serial-based search and the fuzzy-logic (FL)-based search is presented. At first, the serial-based search is used until the decision variable exceeds a preset threshold; the FL-based search is then performed for a second search and the final decision is made. In the second search stage, Doppler search step size is adaptively adjusted by an FL controller. To design a robust FL controller, the characteristics of the spectrum distribution and several possible abnormal conditions are analyzed, input and output fuzzy membership functions are derived, and a set of fuzzy rules is formulated. Finally, the effectiveness of the proposed approach is evaluated through Monte Carlo simulations. The obtained results show that a smaller estimation error is achieved when the proposed algorithm is used; further, reasonable mean acquisition times are attained compared with other existing search techniques. Furthermore, the proposed algorithm can alleviate possible false detection in the abnormal conditions.

Design of Robust Fuzzy Logic Controller Based on the Levenberg Marquardt Algorithm and Fault Ride Trough Strategies for a Grid-Connected PV System

This paper emphasizes the design and investigation of a new optimization scheme for a grid-connected photovoltaic system (PVS) under unbalance faults. The proposed scheme includes fuzzy logic controller (FLC) based on the Levenberg–Marquardt (LM) optimization technique in coordination with bridge-type-fault-current limiter (BFCL) as the fault ride through (FRT) Strategy. The LM optimization-based control is an iterative process with a fast and robust response and is always convergent. The BFCL reduces the fault currents to rated values without compromising at ripples. A keen and critical comparison of the designed strategy is carried out with a conventionally tuned proportional-integral (PI) controller in coordination with the crowbar FRT strategy. A 100kW MATLAB/Simulink model of a photovoltaic system is used for simulation and analysis of unbalance faults at the point of common-coupling (PCC) and at 5 km away from PCC. It is found that grid-connected PVS is highly influenced by the fault type and less effected by the distribution line length. The simulation results authenticated smooth, stable, ripples with free, robust, and fault-tolerant behavior of the proposed scheme.

Task Space Control of an Autonomous Underwater Vehicle Manipulator System by Robust Single-Input Fuzzy Logic Control Scheme

In this paper, a robust single-input fuzzy logic control Robust Single Input Fuzzy Logic Controller (RSIFLC) scheme is proposed and applied for task-space trajectory control of an autonomous underwater vehicle manipulator system (AUVMS) employed for underwater manipulation tasks. The effectiveness of the proposed control scheme is numerically demonstrated on a planar underwater vehicle manipulator system [consisting of an underwater vehicle and a two link rotary (2R) serial planar manipulator]. The actuator and sensor dynamics of the system are also incorporated in the dynamical model of an AUVMS. The proposed control law consists of a feedforward term to exaggerate the control activity with immoderation from the known desired acceleration vector and an estimated perturbed term to compensate for the unknown effects namely external disturbances and unmodeled dynamics as a first part and a single-input fuzzy logic control as a feedback portion to enhance the overall closed-loop stability of the system as a second part. The primary objective of the proposed control scheme is to track the given end-effector task space trajectory despite of external disturbances, system uncertainties, and internal noises associated with the AUVMS. To show the efficacy of the proposed control scheme, comparison is made with conventional fuzzy logic control (CFLC), sliding mode control (SMC), and proportional–integral–derivative (PID) controllers. Simulation results confirmed that with the proposed control scheme, the AUVMS can successfully track the given desired spatial trajectory and gives better and robust control performance.

Improving Asynchronous Motor Speed and Flux Loop Control by Using Hybrid Fuzzy-SMC Controllers

This paper presents a new method combining sliding mode control (SMC) and fuzzy logic control (FLC) to enhance the robustness and performance for a class of non-linear control systems. This fuzzy sliding mode control (FSMC) is developed for application in the area for controlling the speed and flux loops of asynchronous motors. The proposed control law can solve those problems associated with the conventional control by sliding mode control, such as high current, flux and torque chattering, variable switching frequency and variation of parameters, in which a robust fuzzy logic controller replaces the discontinuous part of the classical sliding mode control law. Simulation results of the proposed FSMC technique on the speed and flux rotor controllers present good dynamic and steady-state performances compared to the classical SMC in terms of reduction of the torque chattering, quick dynamic torque response and robustness to disturbance and variation of parameters.

Robust fuzzy logic stabilization with disturbance elimination.

A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.

Design of PSO‐based fuzzy logic controller for single axis magnetic levitation system

AbstractIn this paper, a particle swarm optimization (PSO) method is proposed to design an optimal robust fuzzy logic controller (FLC). The objective of this paper is to design a nonlinear optimal robust controller for the single axis magnetic levitation system with high accuracy. PSO algorithm is applied to search globally optimal parameters of FLCs. Three different FLCs are designed. First, proportional derivative (PD)‐like FLC. Second, the FLC is based on the PSO algorithm to find the optimal range of the eight linguistic membership functions (FLC1 with PSO algorithm). Finally, the FLC is based on the PSO algorithm to find the optimal range and shape of the four linguistic membership functions (FLC2 with PSO algorithm). The performances of three different FLCs are compared. Simulation results show that PSO‐based optimal FLCs find the optimal range and shape of the four linguistic membership functions and achieved better performance than the other proposed controllers, minimizing 48 fuzzy rules. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Wave Prediction and Robust Control of Heaving Wave Energy Devices for Irregular Waves

This paper presents a comparison of different existing and proposed wave prediction models applicable to control wave energy converters (WECs) in irregular waves. The objective of the control is to increase the energy conversion. The power absorbed by a WEC is depending on the implemented control strategy. Uncertainties in the physical description of the system as well as in the input from irregular waves provide challenges to the control algorithm. We present a hybrid robust fuzzy logic (FL) controller that addresses the uncertainty in the model and the short-term tuning of the converter. The proposed robust controller uses the energy from the power take off as input, while the FL controller portion is used for short-term tuning. The FL controller action is based on a prediction of the incoming wave. Simulation results indicate that the proposed hybrid control yields improved energy production, while being robust to modeling errors.

Design and implementation of robust multivariable PI-like fuzzy logic controller for aerodynamic plant

Most modern plants are multivariable, non-linear and time-variant. Therefore, they are difficult to model and control especially when measurements are insufficient or imprecise due to variables interactions, disturbances and noise impact. A perspective model-free approach is based on fuzzy logic, which has proved efficient in ensuring both robust and economic control, employing linguistic expert knowledge in production of simple and feasible non-linear controllers that outperform their linear counterparts. The aim of this investigation and the main contributions are related to the development of a method for the design of multivariable robust fuzzy logic controllers and its implementation for the control of a laboratory-scale aerodynamic plant in real time using MATLAB.

New control approach for a PV-diesel autonomous power system

A new control scheme for the hybrid photovoltaic-diesel single-phase autonomous power system is proposed. The main advantage of this scheme is that the voltage control is accomplished by the interface inverter without need to the automatic voltage regulator of the diesel-driven generator. Unlike three-phase systems, frequency and voltage control in single-phase autonomous power systems imposes additional complexity. This is due to the pulsating nature of the single-phase loads instantaneous power at twice the rated frequency that may degrade the control efficacy. This obstacle is addressed in this paper and a new scheme is presented. The approach includes three control loops for maximum power tracking, voltage control and frequency control. The generator field current is held constant at its nominal value avoiding the saturation in the field circuit. A robust fuzzy logic controller is adopted for the speed control loop of the diesel engine. The dynamic performance of the system is investigated under different operating conditions.

A ROBUST FUZZY LOGIC PATH TRACKER FOR NON-HOLONOMIC MOBILE ROBOTS

In this paper the path tracking problem for non-holonomic mobile robots is dealt with. The model used is known as the Dubins Car model. A robust fuzzy logic controller that steers the car to the appropriate direction is proposed. A technique called "spatial window technique" which greatly increases the overall performance of the control scheme is also introduced. The simulations of this scheme show that the path following problem is treated very adequately and the noise tolerance of the overall system is high.

An adaptive fuzzy control to antilock non-straight line braking using three dimensional (finite element) vehicle model

The objective in this paper is to design a stable and robust adaptive fuzzy logic controller (AFLC) to antilock braking by the consideration of the total range of tyre-road friction. A favourable property of the fuzzy logic controllers (FLC) is that the state equations of the controlled system (plant) are not directly required, nothing but the selected input output signals (variables). Consequently, it is possible to apply well-elaborated and accurate finite element or multibody vehicle models with large number of degrees of freedom to the simulation of controlled vehicle motions. In this paper, a truck finite element model of 2148 degrees of freedom (DOF) is applied as the controlled plant. The braking moments acting on the wheels are controlled with (four) fuzzy controllers in the function of the current slips and change of slips (time rate of slips). Numerical examples, by the consideration of variable road friction, demonstrate the excellent performance of the developed AFLC.

Robust fuzzy logic control of mechanical systems

An approach for the design of fuzzy control laws for tracking control of a large class of mechanical systems is proposed. The approach employs the framework of Lyapunov's stability theory to formulate a class of control laws that guarantee convergence of the tracking errors to within specification limits in presence of bounded parameter uncertainties and input disturbances. The proposed control laws possess a large number of parameters and functional relationships to be chosen by the designer according to a methodology developed in the paper. The large number of design degrees of freedom makes the approach suitable for fuzzy logic implementation. A number of fuzzy implementations of the proposed control methodology are provided. All implementations guarantee tracking error convergence to within prespecified performance limits. An extensive simulation study using a model of a two-degree-of-freedom robot manipulator was conducted. Fuzzy and non-fuzzy implementations of the proposed methodology were compared to control laws designed using other design methods. Simulation study results indicate a superiority of the proposed control methodology compared to other approaches. The study also demonstrates better performance of the fuzzy control implementation compared to its non-fuzzy counterpart.

Self-tuning multivariable fuzzy and neural control using genetic algorithms

In this paper genetic algorithms (GAs) are applied to improve the performance of fuzzy and neural multivariable controllers. In the case of fuzzy multivariable controllers the GA task is to determine an appropriate compensation element which will per–form partial decoupling between system inputs and outputs thus permitting the application of a decentralized control scheme. A novel robust fuzzy logic controller is then applied to each one of the SISO subsystems. The neural multivariable controller is based on the Gawthrop-type MIMO controller but now a neural network is used for the prediction of the system outputs. A genetic algorithm decides how much the control signal should contribute in the cost function thus succeeding the optimal compromise between system stability and zero steady-state error.

A robust fuzzy logic controller for robot manipulators with uncertainties

Owing to load variation and unmodeled dynamics, a robot manipulator can be classified as a nonlinear dynamic system with structured and unstructured uncertainties. In this paper, the stability and robustness of a class of the fuzzy logic control (FLC) is investigated and a robust FLC is proposed for a robot manipulator with uncertainties. In order to show the performance of the proposed control algorithm, computer simulations are carried out on a simple two-link robot manipulator.