Hybrid Fuzzy Controller Research Articles

Control synthesis of switched discrete‐time fuzzy systems via hybrid approach

SUMMARYBecause switched system with nonlinear subsystems shows great difficulties in controller design by conventional methodologies, the fuzzy logic theory, which has been proven to be a practical and effective way to deal with the analysis and synthesis problems for complex nonlinear systems, is applied to design a class of hybrid fuzzy controller for switched discrete‐time fuzzy system in this paper. On a basis of one step prediction of switching signal, a hybrid parallel distributed compensation (PDC) control strategy is proposed, which includes two necessary parts: the state feedback controllers for subsystems and the update feedback controllers for switching instants. In both arbitrary and dwell‐time switching cases investigated, it is proved that the hybrid PDC controller contains less conservativeness than conventional switched PDC controller. Numerical examples are given to illustrate the effectiveness and advantages of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.

Road-friendliness of Fuzzy Hybrid Control Strategy Based on Hardware-in-the-Loop Simulations

Purpose: In order to improve road-friendliness of heavy vehicles, a fuzzy hybrid control strategy consisting of a hybrid control strategy and a fuzzy logic control module is proposed. The performance of the proposed strategy should be effectively evaluated using a hardware-in-the-loop (HIL) simulation model of a semi-active suspension system based on the fuzzy hybrid control strategy prior to real vehicle implementations. Methods: A hardware-in-the-loop (HIL) simulation system was synthesized by utilizing a self-developed electronic control unit (ECU), a PCI-1711 multi-functional data acquisition board as well as the previously developed quarter-car simulation model. Road-friendliness of a semi-active suspension system controlled by the proposed control strategy was simulated via the HIL system using Dynamic Load Coefficient (DLC) and Dynamic Load Stress Factor (DLSF) criteria. Results: Compared to a passive suspension, a semi-active suspension system based on the fuzzy hybrid control strategy reduced the DLC and DLSF values. Conclusions: The proposed control strategy of semi-active suspension systems can be employed to improve road-friendliness of road vehicles.

Hybrid fuzzy control of wind turbine generator by pitch control using RNN

This article presents the design of a hybrid fuzzy sliding mode loss-minimisation control for the speed of a permanent magnet synchronous generator (PMSG) and a high-performance on-line training recurrent neural network (RNN) for the turbine pitch angle control. The back-propagation learning algorithm is used to regulate the RNN controller. The PMSG speed uses maximum power point tracking below the rated speed, which corresponds to low- and high-wind speeds, and the maximum energy can be captured from the wind. The sliding mode controller with an integral-operation switching surface is designed, in which a fuzzy inference mechanism is utilised to estimate the upper bound of uncertainties.

A Random Spatial lbest PSO-Based Hybrid Strategy for Designing Adaptive Fuzzy Controllers for a Class of Nonlinear Systems

In this paper, a new variant of particle swarm optimization (PSO), called random spatial lbest PSO model, is proposed and implemented for designing a newly devised stable adaptive hybrid fuzzy controller. The newly developed concurrent hybrid strategy for designing fuzzy controllers utilizes the conventional Lyapunov theory and the proposed PSO-based stochastic approach. The objective is to design a self-adaptive fuzzy controller online, optimizing both its structures and free parameters such that the designed controller can guarantee the desired stability and simultaneously provide satisfactory transients performance. The global version and two different lbest variants of PSO schemes and the proposed random spatial lbest model of PSO are employed for three popular, challenging, and nonlinear processes, and the proposed controller emerges as the superior algorithm in terms of tracking performance overall. These results aptly demonstrate the usefulness of the proposed approach.

Hybrid Fuzzy Load Frequency Controller for a Two Area Interconnected Power System

This work addresses the special requirements of Automatic Generation Control in Modern interconnected Power system. In order to track the system frequency and handling the power system stability issues many control strategies has been suggested by the researchers .A new Hybrid fuzzy approach is introduced here .Fuzzy Logic controller with Mamdani interface having five member ship functions is tested with the Thermal Thermal and hydro thermal system Further hybrid Fuzzy controller is also tested with the same system and results are compared for the both The system Which is having Hybrid Fuzzy concept and thereby the response of frequency and tie line power can be improved substantially following a load change in any area. Further dynamic responses for small perturbation have been observed, considering HFLC and integral controller and the results of both have been compared.

Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle

Helicopters have generated considerable interest in both the control community due to their complex dynamics, and in military community because of their advantages over regular aerial vehicles. In this paper, we present the modeling and control of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as quadrotor aircraft. This model has been generated using Newton-Euler equations. In order to control the helicopter, classical PD (proportional derivative) and Hybrid Fuzzy PD controllers have been designed. Although fuzzy control of various dynamical systems has been presented in literature, application of this technology to quadrotor helicopter control is quite new. A quadrotor helicopter has nonlinear characteristics where classical control methods are not adequate especially when there are time delays, disturbances and nonlinear vehicle dynamics. On the other hand, Fuzzy control is nonlinear and it is thus suitable for nonlinear system control. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations. For the evaluation of the autonomous flight controllers, some experiments were also performed. For this purpose, an experimental test stand has been designed and manufactured. This study showed that although, both of the classical PD and the Fuzzy PD controllers can control the system properly, the Fuzzy PD controllers performed slightly better than the classical PD controllers, and have benefits such as better disturbance rejection, ease of building the controllers.

Application of hybrid fuzzy PID controller for three-area power system with generation rate constraint

This paper presents a new approach for load frequency control of three-area interconnected reheat thermal power system with the consideration of generation rate constraint (GRC). The PID-type fuzzy controller is combined with a conventional PID controller to enhance the performance and robustness of the controller. The proposed controller called hybrid fuzzy PID (HFLPID) controller has been designed for a three-area connected power system. Two performance criteria were utilised for the comparison. First, settling times and overshoots of the frequency deviation were compared. Later, integral of time multiplied absolute error (ITAE) analysis was carried out to compare all the controllers. All the models were simulated by MATLAB 7.0/Simulink software. The simulation results show that the proposed controller developed in this study performs better than the other controllers. Robustness of proposed controller is checked by analysing the system response with varying system parameters.

Adaptive Hybrid Intelligent Tracking Control for Uncertain Fractional Order Chaotic Systems

This paper presents an adaptive hybrid fuzzy controller to achieve prescribed tracking performance of fractional order chaotic systems. Depending on plant knowledge and control knowledge, a weighting factor can be adjusted by combining the indirect adaptive fuzzy control effort and the direct fuzzy adaptive control effort. Nonlinear fractional order chaotic response system is fully demonstrated to track the trajectory generated from fractional order chaotic drive system. The numerical results show that tracking error and control effort can be made smaller and the proposed hybrid intelligent control scheme is more flexible during the design process.

Uncertain Fractional Order Chaotic Systems Tracking Design via Adaptive Hybrid Fuzzy Sliding Mode Control

In this paper, in order to achieve tracking performance of uncertain fractional order chaotic systems an adaptive hybrid fuzzy controller is proposed. During the design procedure, a hybrid learning algorithm combining sliding mode control and Lyapunov stability criterion is adopted to tune the free parameters on line by output feedback control law and adaptive law. A weighting factor, which can be adjusted by the trade-off between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. To confirm effectiveness of the proposed control scheme, the fractional order chaotic response system is fully illustrated to track the trajectory generated from the fractional order chaotic drive system. The numerical results show that tracking error and control effort can be made smaller and the proposed hybrid intelligent control structure is more flexible during the design process.

Soft Computing Techniques for Process Control Applications

Technological innovations in soft computing techniques have brought automation capabilities to new levels of applications. Process control is an important application of any industry for controlling the complex system parameters, which can greatly benefit from such advancements. Conventional control theory is based on mathematical models that describe the dynamic behaviour of process control systems. Due to lack in comprehensibility, conventional controllers are often inferior to the intelligent controllers. Soft computing techniques provide an ability to make decisions and learning from the reliable data or expert’s experience. Moreover, soft computing techniques can cope up with a variety of environmental and stability related uncertainties. This paper explores the different areas of soft computing techniques viz. Fuzzy logic, genetic algorithms and hybridization of two and abridged the results of different process control case studies. It is inferred from the results that the soft computing controllers provide better control on errors than conventional controllers. Further, hybrid fuzzy genetic algorithm controllers have successfully optimized the errors than standalone soft computing and conventional techniques.

유도전동기 드라이브의 DTC를 위한 하이브리드 퍼지제어기

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjection with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid fuzzy controller for direct torque control(DTC) of induction motor drives. The speed controller is based on adaptive fuzzy learning controller(AFLC), which provide high dynamics performances both in transient and steady state response. Flux position, error in flux magnitude and error in torque are used as FLC state variables. The speed is estimated with model reference adaptive system(MRAS) based on artificial neural network(ANN) trained on-line by a back-propagation algorithm. This paper is controlled speed using hybrid fuzzy controller(HFC) and estimation of speed using ANN. The performance of the proposed induction motor drive with HFC controller and ANN is verified by analysis results at various operation conditions.

Fuzzy hybrid control of a wind-excited tall building

A fuzzy hybrid control technique using a semi-active tuned mass damper (STMD) has been proposed in this study for mitigation of wind induced motion of a tall building. For numerical simulation, a third generation benchmark is employed for a wind-excited 76-story building. A magnetorheological (MR) damper is used to compose an STMD. The proposed control technique employs a hierarchical structure consisting of two lower-level semi-active controllers (sub-controllers) and a higher-level fuzzy hybrid controller. Skyhook and groundhook control algorithms are used as sub-controllers. When a wind load is applied to the benchmark building, each sub-controller provides different control commands for the STMD. These control commands are appropriately combined by the fuzzy hybrid controller during realtime control. Results from numerical simulations demonstrate that the proposed fuzzy hybrid control technique can effectively reduce the STMD motion as well as building responses compared to the conventional hybrid controller. In addition, it is shown that the control performance of the STMD is superior to that of the sample TMD and comparable to an active TMD, but with a significant reduction in power consumption.

Fuzzy Self-Tuning Precompensation PD Control with Gravity Compensation of 3 DOF Planar Robot Manipulators

Industrial robot control covers nonlinearity, uncertainty and external perturbation considered in control laws design. Proportional and Derivative (PD) with gravity compensation control is well-known control used in manipulators to ensure global asymptotic stability for fixed symmetrical positive definite gain matrices. To enhance PD with gravity compensation controller performance, in this paper, we propose hybrid fuzzy PD control precompensation with gravity compensation, consisting of a fuzzy logic-based precompensator followed by hybrid fuzzy PD with gravity compensation controller. Hybrid fuzzy control is done by a Supervisory Hierarchical Fuzzy Controller (SHFC) for tuning conventional controller Proportional and Derivative gains based on actual tracking location and velocity error. Hierarchical hybrid fuzzy control consists of an intelligent upper supervisory fuzzy controller and a lower direct conventional PD controller. Numerical simulations using the dynamic model of a three DOF planar rigid robot manipulator with uncertainty show the effectiveness of the approach in trajectory tracking problems. Our results show that the proposal controller has performance superior to a conventional controller.

Modelling and motion control of a mechatronic system using BGM with intelligent controllers

In this research paper, a mechatronics system such as a pan tilt platform (PTP) has been considered for motion control under intelligent controllers. A proportional-derivative (PD) controller is considered for comparison of results obtained from fuzzy and hybrid controllers. The trajectory following performance of the mechatronics system is found against these controllers. The results of simulations show that hybrid fuzzy controller reduce the tracking error effectively in lesser settling time. The intelligent controllers require knowledge base of error and derivative of error to compensate the PTP dynamics. The intelligent controllers have similar trends as the PD controllers and compensated both electrical and mechanical dynamics. The PD controller requires position measurement. The intelligent controllers have knowledge base consisting of position and velocity data. Thus intelligent controllers have position measurement along with knowledge base for position control system. The best results were achieved with hybrid fuzzy controllers. They meet the desired specifications.

A Hybrid of Fuzzy and Proportional-Integral-Derivative Controller for Electro-Hydraulic Position Servo System

Problem statement: While classical PID controllers are sensitive to variations in the system parameters, Fuzzy controllers do not need precise information about the system variables in order to be effective. However, PID controllers are better able to control and minimize the steady state error of the system. To enhance the controller performance, hybridization of these two controller structures comes to one mind immediately to exploit the beneficial sides of both categories. Approach: A hybrid fuzzy PID controller for the Electro-Hydraulic Position Servo System (EHPSS) was proposed in this study. The proposed control scheme was separated into two parts, fuzzy controller and PID controller. Fuzzy controller was used to control the piston when the piston locates far away from the target position. PID controller is applied when the piston is near the desired position. Results: We demonstrated the performance of control scheme via experiments performed on the EHPSS. Conclusion: The results from the experiments showed that the proposed hybrid fuzzy PID controller has superior performance compared to individual PID controller and fuzzy controller.

Fuzzy Hybrid Control of Vibration Attitude of Full Car via Magneto-rheological Suspensions

A magneto-rheological(MR) semi-active suspension system with the controllable damping forces has received more attention in reducing the vibration of a vehicle. However, many control strategies only discussed one or two vibration states of the vehicle based on a quarter-car model or a half vehicle model via MR suspensions. They cannot provide a satisfying whole-vehicle performance on a road test. Hence, a full car vibration model via an MR suspension system is proposed. To reduce the heave, pitch and roll motion of the vehicle body and the vertical vibration of four wheels, a fuzzy hybrid controller for vibration attitude of full car via MR suspensions is proposed. First, a skyhook-fuzzy control scheme is designed to reduce the heave, roll and pitch motion of the vehicle body. Second, a revised ground hook control strategy is adopted to decrease the vertical vibration of the wheels. Finally, a hybrid control scheme based on a fuzzy reasoning method is proposed to tune the hybrid damping parameter, which is suitable for coordination the attitude of the vehicle body and the wheels. A test and control system for the vibration attitude of full car is set up. It is implemented on a car equipped with four MR suspensions. The results on random highway and rough road indicate that the fuzzy hybrid controller can decrease the vibration accelerations of the vehicle body and the wheels to 65%–80% and 80%–90%, respectively. It reduces the automotive vibrations of heave, roll and pitch more effectively than a passive suspension and an MR suspension with a traditional hybrid control scheme so that it achieves better ride comfort and road holding concurrently. This paper proposes a new fuzzy hybrid control(FHC) method for reducing vibration attitude of full car via MR suspensions and develops a road test to evaluate the FHC.

Hybrid fuzzy and sliding-mode control for motorised space tether spin-up when coupled with axial oscillation

A hybrid fuzzy sliding mode controller is applied to the control of motorised tether spin-up coupled with an axial oscillation phenomenon. A six degree of freedom dynamic model of a motorised momentum exchange tether is used as a basis for interplanetary payload exchange. The tether comprises a symmetrical double payload configuration, with an outrigger counter inertia and massive central facility. It is shown that including axial elasticity permits an enhanced level of performance prediction accuracy and a useful departure from the usual rigid body representations, particularly for accurate payload positioning at strategic points. A special simulation program has been devised in MATLAB and MATHEMATICA for a given initial condition data case.

Tilt control design for high-speed trains: a study on multi-objective tuning approaches

This paper presents work on a hybrid fuzzy control scheme to improve the performance of tilting trains using a nulling-based tilt strategy. Two multi-objective genetic algorithm tuning methods (MOGA and NSGAII) were employed to optimise both the fuzzy output membership functions and the controller parameters. The objective functions incorporated the tilt response and roll gyroscope signals for the deterministic (curved track) profile, and lateral acceleration for the stochastic (straight track) profile. Simulation results discuss the effectiveness of using the presented techniques for tuning the fuzzy control scheme via multiple objectives. The proposed scheme is compared with the conventional nulling-tilt approach and a manually tuned fuzzy controller.

Modeling and control of a curved pneumatic muscle actuator for wearable elbow exoskeleton

A novel curved pneumatic muscle based rotary actuator for the wearable elbow exoskeleton with joint torque control is proposed. Compared to the general utilization of the pneumatic muscle actuator (PMA) in a rotary joint, this novel structure weakens coupling relationship between the output torque/force and contacting displacement of the PMA so that it can be easily utilized in the tele-robotics with torque/force-feedback or the exciting application in rehabilitation for a wide range. By referred to two physical models, namely beam model and membrane model, the mechanics properties of this mechanical structure is analyzed. In addition a hybrid fuzzy controller composed of bang–bang controller and fuzzy controller is employed for output torque control with high accuracy as well as fast response. In a series of experiments, the actuator exhibits both good static and dynamic performances that well validated the models and control strategy.

Soft computing methods applied to the control of a flexible robot manipulator

The paper describes use of soft computing methods (fuzzy logic and neural network techniques) in the development of a hybrid fuzzy neural control (HFNC) scheme for a multi-link flexible manipulator. A manipulator with multiple flexible links is a multivariable system of considerable complexity due to the inter-link coupling effects that are present in both rigid and flexible motions. Modelling and controlling the dynamics of such manipulators is therefore difficult. The proposed HFNC scheme generates control actions combining contributions form both a fuzzy controller and a neural controller. The primary loop of the proposed HFNC contains a fuzzy controller and a neural network controller in the secondary loop to compensate for the coupling effects due to the rigid and flexible motion along with the inter-link coupling. It has been ascertained from the present investigation that the proposed soft-computing-based controller works effectively in the tracking control of such a multi-link flexible manipulator. The results are extendable to other multivariable systems of similar complexity.