Fuzzy Supervisor Research Articles

Intelligent modified internal model control for speed control of nonlinear uncertain heavy duty vehicles

The objective of this paper is to control the speed of heavy duty vehicle (HDV) through angular position of throttle valve. Modified internal model control (IMC) schemes with fuzzy supervisor as an adaptive tuning are proposed to control the speed of HDV. Internal model (IM) plays a key role in design of various IMC structures with robust and adaptive features. The motivation to design an IM is to produce nearly stable performance as of the system itself. Clustering algorithm and Hankel approximation based model order reduction techniques are used for the design of suitable IM. The time domain performance specifications such as overshoot, settling time, rise time and integral error performance indices such as the integral of the absolute error and the integral of the square of error are taken into consideration for performance analysis of HDV for various uncertainties.

Comparative Study of PI, RST, Sliding Mode and Fuzzy Supervisory Controllers for DFIG based Wind Energy Conversion System

This work aims to present a comparative study of four controllers for Double Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS). The DFIG is directly connected to the grid and driven by the rotor through an AC/DC/AC converter. A model was developed for each component (Turbine, DFIG and Rectifier-Filter-Inverter) of the wind system. The PWM control method is applied to the inverter to drive the DFIG from the rotor circuit. To ensure high performance and better enforcement of DFIG, a direct vector control strategy of active and reactive power of the stator has been developed. The synthesis of conventional PI controller and advanced RST, Sliding Mode (SM) and Fuzzy Supervisory (FS) controllers is performed. The system’s performance has been tested and compared according to reference tracking, robustness, and disturbance rejection. A set of simulation studies are carried-out on a WECS model to prove the effectiveness of the proposed controllers design.

Design of Multiregional Supervisory Fuzzy PID Control of pH Reactors

This work concerns designing multiregional supervisory fuzzy PID (Proportional-Integral-Derivative) control for pH reactors. The proposed work focuses, mainly, on two themes. The first one is to propose a multiregional supervisory fuzzy-based cascade control structure. It would enable modifying dynamics and enhance system’s stability. The fuzzy system (master loop) has been chosen as a tuner for PID controller (slave loop). It takes into consideration parameters uncertainties and reference tracking. The second theme concerns designing a hybrid neural network-based pH estimator. The proposed estimator would overcome the industrial drawbacks, that is, cost and size, found with conventional methods for pH measurement. The final end-user-interface (EUI) front panel and the results that evaluate the performance of the supervisory fuzzy PID-based control system and hybrid NN-based estimator have been presented using the compatibility found between LabView and MatLab. They lead to conclude that the proposed algorithms are appropriate to systems nonlinearities encountered with pH reactors.

Availability Principle Applied to a Fuzzy Supervisory Control of a Pilot Flotation Column

This work presents a study of fuzzy expert control system, based on the availability principle, of a pilot flotation column at the Hybrid Process Control Laboratory in Santa Maria University. Sensors and actuators are communicated to a programmable Logic Controller (PLC) where calibrations and local control are implemented, and the data is sent to a PC network, where a Distributed Control System (DCS) is running. Process model predictions are obtained by running a simulator fed with actual operating variables and virtual variables associated to feed characteristics. All data is displayed at PC stations and historical data files are also built for further analysis. Operating variables (real and virtual) can also be analyzed off-line by advanced software tools. Supervisory control running on PC network writes DCS set points on PLC.The supervisory control performance was experimentally evaluated by keeping the mineralogical objectives of concentrate grade and recovery in narrow bands. The operation of the supervisor was compared to a base case where there is no supervisory control, and each experiment was subjected to stochastic auto-correlated disturbances in the feed conditions. The fuzzy supervisory control, unlike conventional control, is more capable of maintaining the metallurgical results within control limits during almost 90% of the operating time. An availability principle for manipulated variables was successfully tested acting on top of the fuzzy logic decision system.

Vision Robot Moving Control by Supervisory Fuzzy Neural Network

Supervisory fuzzy neural network has been developed for the vision robot control. This fuzzy neural network (FNN) is constructed by method of adaptive learning algorithm. The FNN control is combined with supervisory controller (SC) to be a full controller called supervisory fuzzy neural network controller (SFNN). The SFNN is tuned by off‐line method and designed by considering image processing such as to be nearly identical to the perfect controller. The error of system is come from captured image data, so the controlled results should be drive into a suitable switching plane such that the stability can be achieved. This SFNN can achieve good control performance of vision robot. Simulation results show the proposed method possesses satisfied performances for the vision robot.

Design of an Adaptive Fuzzy Sliding Mode Control Using Supervisory Fuzzy Control for Micro-Electro-Mechanical Systems (MEMS) Z-Axis Gyroscope

This paper presents a novel robust adaptive control strategy for MEMS gyroscope, based on the coupling of the fuzzy logic control with sliding mode control (SMC) approach and adaptive laws. The drawbacks of the conventional SMC include chattering phenomenon and requirement of a priori knowledge of the bounds of uncertainties. In this paper, these problems are suitably attenuated by adopting an adaptive fuzzy sliding mode control (AFSMC) approach which uses two additional fuzzy self-tuning controllers as supervisory fuzzy systems to adaptively tune the output control gain, sliding gain of the AFSMC in order to speed up the convergence rates of the tracking errors and parameter estimations with a desired level of attenuation. Control system stability is proved by Lyapunov method. Numerical simulations using the MEMS gyroscope model with uncertainties demonstrate the effectiveness of this approach in high speed trajectory tracking problems and robustness in estimating the gyroscope parameters and the angular velocity. Numerical simulations show that the chattering is effectively attenuated by this method. Convergence rate of tracking errors and the gyroscope parameters is better than convergence rates in the literature.

Fuzzy predictive control of three-tank system based on a modeling framework of hybrid systems

In this article, a fuzzy predictive control scheme is proposed for controlling liquid level in a three-tank system in the presence of disturbances and uncertainties. The three-tank system is considered as a hybrid system, and a hybrid model of the system is obtained using the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized based on a piecewise affine linear method. Then, a model predictive control is designed based on the hybrid model and applied to the three-tank system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from the problem of bias in output in the presence of disturbance and uncertainty. In this article, a fuzzy supervisor is utilized to adjust the main predictive controller such that the effects of disturbance and uncertainties are degraded by using zero-offset tracking. The proposed fuzzy predictive control scheme has advantages of simplicity and efficiency in normal operation, and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method.

Fuzzy Predictive Control of a Boiler–Turbine System Based on a Hybrid Model System

This paper proposes a fuzzy predictive control scheme for controlling power output of a boiler–turbine system in the presence of disturbances and uncertainties. A new model of the boiler–turbine system is introduced based on the modeling approaches of hybrid systems, namely, the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized using the piecewise affine approach. To overcome the deficiency of the model predictive control in presence of disturbance and uncertainty, a fuzzy predictive control scheme is proposed in which a fuzzy supervisor is utilized to adjust the main predictive controller. The proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in nominal conditions and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method.

Fuzzy Predictive Control of Step-Down DC-DC Converter Based on Hybrid System Approach

In this paper, a fuzzy predictive control scheme is proposed for controlling output voltage of a step-down DC-DC converter in presence of disturbance and uncertainty. The DC-DC converter is considered as a hybrid system and modeled by Mixed Logical Dynamical modeling approach. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. A model predictive control is designed based on the hybrid model and applied to the system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from lack of robustness in presence of disturbance and uncertainty. So, to succeed in facing up to the problem a fuzzy supervisor is utilized to adjust the main predictive controller based on the measured states of the system. In this paper it is shown that the proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in normal operation and robustness in presence of disturbance and uncertainty. Through simulations effectiveness of the proposed method is shown.

Supervisory Fuzzy Predictive Control for a Concentrated Solar Power Plant

This paper deals with the design and evaluation of the performance of fuzzy predictive controller in supervisory mode for a solar-concentration-based power plant, with cylindrical-parabolic solar collectors. For the design of the controller, the inner control loops present in this solar platform are included in the model of the plant. That is, a control loop consisting of a PI plus a feed-forward controller is considered for modeling the closed-loop behavior of the plant. With this purpose, an ARIX and a Takagi-Sugeno model are compared. As expected, the Takagi-Sugeno model performs better than the ARIX model. Therefore, Takagi-Sugeno model is used for designing the supervisory control strategy. The proposed control scheme is tested in the ACUREX simulator (the simulator of the Almeria Solar Platform). The tests included in this paper are: set-point changes, variations in the solar radiation, and modifications in the parameters of the inner control loop.

Applications of Fuzzy Supervisory PID Controller to a Power System

This paper presents a way that fu zzy logic can be used in high level control functions. Specifically, we examine the use of fuzzy logic in supervisory control, for selecting discrete control actions, identifying the operating environment and evaluating controller performance. Proportional integral derivative (PID) controllers are widely used in excitation control of power systems, they exh ibit poor performance when the controlled systems contain unknown linearities. The main objective of this paper is to simu late the use of fuzzy logic to provide new control functions that are outside the domain of the PID control-where fuzzy control is likely to provide the greatest payoff. Simply, a supervisory correction term is added to the input of the PID controller. The supervisory correction term is the output of fuzzy supervisory controller. A performance demonstration of the proposed scheme via the excitation control of a single-machine infinite-bus system subjected to a wide variety of transient disturbances is presented in this paper. Ou r results show that the fuzzy supervisory PID controllers have high performance compared to PID controllers with significant reduction in overshoot and undershoot. The scheme can be easily imp lemented in pract ice by adding a fuzzy supervisory controller to the existing PID controller

Reliable Decentralized Control of Fuzzy Discrete-Event Systems and a Test Algorithm

A framework for decentralized control of fuzzy discrete-event systems (FDESs) has been recently presented to guarantee the achievement of a given specification under the joint control of all local fuzzy supervisors. As a continuation, this paper addresses the reliable decentralized control of FDESs in face of possible failures of some local fuzzy supervisors. Roughly speaking, for an FDES equipped with n local fuzzy supervisors, a decentralized supervisor is called k-reliable (1 ≤ k ≤ n) provided that the control performance will not be degraded even when n - k local fuzzy supervisors fail. A necessary and sufficient condition for the existence of k-reliable decentralized supervisors of FDESs is proposed by introducing the notions of M̃uc-controllability and k-reliable coobservability of fuzzy language. In particular, a polynomial-time algorithm to test the k-reliable coobservability is developed by a constructive methodology, which indicates that the existence of k-reliable decentralized supervisors of FDESs can be checked with a polynomial complexity.

Methodologies for supervision of Hybrid Energy Sources based on Storage Systems – A survey

Hybrid Energy Sources based on Storage Systems (HES) are increasingly used to improve the grid integration of renewable energy generators, or to improve the energy efficiency and the reliability of transport systems. This paper proposes a survey on the methodologies to design fuzzy logic based supervision strategies of this new kind of energy generating systems. Different ways to manage energy storage system are particularly discussed.A graphical modeling tool is used to facilitate the analysis and the determination of fuzzy control algorithms adapted to complex hybrid systems. The methodology is divided in different steps covering the design of a supervisor from the system work specifications to an optimized implementation of the control.An Experimental Design Methodology (EDM) combined with optimization algorithms is used to tune a set of parameters suitable for a fuzzy supervisor in order to optimize power, energy, efficiency, voltage quality, economic or environmental indicators.The application of this methodology to the supervision of different topologies of HES, based on renewable energy in a grid or in building applications or dedicated to transport systems, illustrates the performance and the systematic dimension of the approach.

Robust Performance of Fuzzy Supervisory Control for Seismically Excited Cable-Stayed Bridge

This paper investigates the robust performance of fuzzy supervisory control (FSC) technique for seismically excited cable-stayed bridge. The FSC technique utilizes fuzzy logic-based decision-making process to incorporate a set of pre-designed static control gains into a time-varying optimal control gain. To demonstrate the excellent robustness of the FSC technique on response control of earthquake-excited cable-stayed bridge, a conventional linear quadratic Gaussian (LQG) controller with single static gain and two fuzzy supervisory controllers are designed for the benchmark cable-stayed bridge, and their robust performances are examined under uncertainty in bridge model and against failures in actuators and sensors. Under the presence of uncertainty in the bridge model, the FSC system successfully reduces the seismic responses of the bridge without significant increase in the total amount of power and stroke required by the control system, while the LQG system exhibits a substantial increase in the seismic responses. Under conditions of sensor or actuator failures, the FSC system guarantees a more enhanced robust performance than the LQG system as well.

Wind Turbine Control with Fuzzy Supervisory in the Partial Load Region

Improvement of the performance of wind turbines as a source of clean energy production depends on the ideal control. In this paper a controller design based on fuzzy logic and for partial load region of wind turbine has been discussed. Here, fuzzy logic is used as a gain scheduler for classical controller. In the structure of the Controller, feedback signal of angular velocity of generator rotor is used to adjust the torque on the generator, while the pitch angle was kept constant on the optimal value. Fuzzy rule-base of supervisory system was derived based on responses of the turbine system to the controller with various gains which lead to track the ideal power curve. Simulation results confirmed the improvement of system response in comparison to the controlling without fuzzy supervisory.

Fuzzy logic for structural system control

This paper provides some information and numerical tests that aims to investigate the use of a Fuzzy Controller applied to control systems. Some advantages are reported regarding the use of this controller, such as the characteristic ease of implementation due to its semantic feature in the statement of the control rules. On the other hand, it is also hypothesized that these systems have a lower performance loss when the system to be controlled is nonlinear or has time varying parameters. Numerical tests are performed using modal LQR optimal control and Fuzzy control of non-collocated systems with full state feedback in a two-dimensional structure. The paper proposes a way of designing a controller that may be a supervisory Fuzzy controller for a traditional controller or even a fuzzy controller independent from the traditional control, consisting on individual mode controllers. Some comments are drawn regarding the performance of these proposals in a number of arrangements.

Design of Switching Multiobjective Controller: A New Approach

Design of switching H2/H∞ output‐feedback controller for discrete‐time LTI systems with state‐multiplicative noise is considered. The closed loop system achieves a minimum bound on the stochastic H2 performance level, while satisfying the H∞ performance. The proposed controller is based on a fuzzy supervisor which manages the combination of two separate H2 and H∞ controllers. A convex formulation of the two controllers leads to a structure which benefits from the advantages of both controllers to ensure a good performance in both the transient phase (H2 controller) and the steady phase (H∞ controller). The stability analysis uses the Lyapunov technique, inspired from switching system theory, to prove that the closed loop system with the proposed controller structure remains globally stable despite the configuration changing.

FUZZY SUPERVISED NONLINEAR PID CONTROL OF A CLASS OF UNKNOWN NONLINEAR SYSTEMS

Fuzzy control has been successfully applied in many cases to which conventional control algorithms are difficult to be applied. Recently, it was proven that fuzzy systems are capable of approximating any real continuous function to arbitrary accuracy. This result motivates us to use the fuzzy identifiers for nonlinear dynamic systems and then design the fuzzy supervised nonlinear PID controller based on the fuzzy system. There are two main objectives in this paper: (1) We use the Takagi and Sugeno's fuzzy models as an identifier for nonlinear dynamic systems, and derive the identification algorithm, (2) the fuzzy supervisor design method for tracking control is proposed based on this fuzzy system. In order to improve the dynamic response of the closed loop fuzzy model, the optimization of the performance of the fuzzy supervisor will be considered. To prove the potential applications of the proposed strategy, simulation was carried out for the speed control of a DC motor with serial excitation and a first order nonlinear process.

A Geese PSO tuned fuzzy supervisor for EKF based solutions of simultaneous localization and mapping (SLAM) problems in mobile robots

The present paper shows how a recently proposed modified Particle Swarm Optimization (PSO) algorithm, called Geese PSO algorithm, can be utilized to tune a fuzzy supervisor for an adaptive Extended Kalman filter (EKF) based approach to solve simultaneous localization and mapping (SLAM) problems for mobile robots or vehicles. This type of fuzzy based adaptive EKF approach for SLAM problems has recently been shown to be an effective approach to improve performance in those situations where correct a priori knowledge of process and/or sensor/measurement uncertainty statistics i.e. Q and/or R respectively, is not available. The newly proposed system in this work is demonstrated to provide better estimation and map-building performance in comparison with those fuzzy supervisors for the adaptive EKF algorithm, where the free parameters of the fuzzy systems are tuned using basic PSO based algorithm. The utility of the proposed approach is aptly demonstrated by employing it for several benchmark environment situations with various numbers of waypoints and landmarks, where the Geese PSO algorithm could tune the fuzzy supervisor better than the basic PSO based algorithm.

Fuzzy supervisor approach on logic-based switching H2/H∞

In this paper the synthesis of a logic-based switching H2/ H∞ state-feedback controller for continuous-time linear time-invariant (LTI) singular perturbation systems is considered which achieves a minimum limit on the H2 performance level, while satisfying the H∞ performance. The proposed hybrid control scheme is based on a fuzzy supervisor, which manages the combination of controllers. A convex linear matrix inequality (LMI)-based formulation of the fast and slow subsystem controllers leads to a ε-independent structure which ensures a good performance in both the transient phase and the steady-state phase. The stability analysis uses the Lyapunov technique, inspired from switching system theory, to prove that the system with the proposed controller remains globally stable, despite the configuration (controller) changing. Simulations show that the proposed non-linear scheme composed of two low-order controllers improves the performance in comparison with the linear optimal scheme with higher order designed for a full-order system.