Showing papers on "Fuzzy control system published in 2013"

Adaptive Sliding-Mode Control for Nonlinear Active Suspension Vehicle Systems Using T–S Fuzzy Approach

Abstract: This paper deals with the adaptive sliding-mode control problem for nonlinear active suspension systems via the Takagi-Sugeno (T-S) fuzzy approach. The varying sprung and unsprung masses, the unknown actuator nonlinearity, and the suspension performances are taken into account simultaneously, and the corresponding mathematical model is established. The T-S fuzzy system is used to describe the original nonlinear system for the control-design aim via the sector nonlinearity approach. A sufficient condition is proposed for the asymptotical stability of the designing sliding motion. An adaptive sliding-mode controller is designed to guarantee the reachability of the specified switching surface. The condition can be converted to the convex optimization problems. Simulation results for a half-vehicle active suspension model are provided to demonstrate the effectiveness of the proposed control schemes.

Robust Adaptive Fuzzy Tracking Control for Pure-Feedback Stochastic Nonlinear Systems With Input Constraints

Abstract: This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

Robust Observer Design for Unknown Inputs Takagi–Sugeno Models

Abstract: This paper deals with the observer design for Takagi-Sugeno (T-S) fuzzy models subject to unknown inputs and disturbance affecting both states and outputs of the system. Sufficient conditions to design an unknown input T-S observer are given in linear matrix inequality (LMI) terms. Both continuous-time and discrete-time cases are studied. Relaxations are introduced by using intermediate variables. Extension to the case of unmeasured decision variables is also given. A numerical example is given to illustrate the effectiveness of the given results.

A Novel Control Design on Discrete-Time Takagi–Sugeno Fuzzy Systems With Time-Varying Delays

Abstract: This paper focuses on analyzing a new model transformation of discrete-time Takagi-Sugeno (T-S) fuzzy systems with time-varying delays and applying it to dynamic output feedback (DOF) controller design. A new comparison model is proposed by employing a new approximation for time-varying delay state, and then, a delay partitioning method is used to analyze the scaled small gain of this comparison model. A sufficient condition on discrete-time T-S fuzzy systems with time-varying delays, which guarantees the corresponding closed-loop system to be asymptotically stable and has an induced l2 disturbance attenuation performance, is derived by employing the scaled small-gain theorem. Then, the solvability condition for the induced l2 DOF control is also established, by which the DOF controller can be solved as linear matrix inequality optimization problems. Finally, examples are provided to illustrate the effectiveness of the proposed approaches.

To Transmit or Not to Transmit: A Discrete Event-Triggered Communication Scheme for Networked Takagi–Sugeno Fuzzy Systems

Abstract: This paper first proposes a discrete event-triggered communication scheme for a class of networked Takagi-Sugeno (T-S) fuzzy systems. This scheme has two main features: 1) Whether or not the sampled state should be transmitted is determined by the current-sampled state and the error between the current-sampled state and the latest transmitted state. Compared with those in a periodic time-triggered communication scheme, the communication bandwidth utilization is considerably reduced while preserving the desired control performance; and 2) it is a discrete event-triggered communication scheme due to the fact that the triggered conditions are only measured and checked at a constant sampling period. Compared with a continuous event-triggered communication scheme, the special hardware for continuous measurement and computation is no longer needed. Second, a networked T-S fuzzy model is delicately constructed, which not only considers nonuniform time scales in the networked T-S fuzzy model and the parallel distributed compensation fuzzy control rules but includes the aforementioned state error as well. Third, a stability criterion and a stabilization criterion about the networked T-S fuzzy system are derived, respectively. The stability criterion and stabilization criterion can provide a tradeoff to balance the required communication resource and the desired performance: Lowering the desired performance allows the network to allocate more limited bandwidth to other nodes in need. Finally, a numerical example is given to show the effectiveness of the proposed method.

Adaptive Fuzzy Output Feedback Control of MIMO Nonlinear Systems With Unknown Dead-Zone Inputs

Abstract: This paper is concerned with the problem of adaptive fuzzy tracking control for a class of multi-input and multi-output (MIMO) strict-feedback nonlinear systems with both unknown nonsymmetric dead-zone inputs and immeasurable states. In this research, fuzzy logic systems are utilized to evaluate the unknown nonlinear functions, and a fuzzy adaptive state observer is established to estimate the unmeasured states. Based on the information of the bounds of the dead-zone slopes as well as treating the time-varying inputs coefficients as a system uncertainty, a new adaptive fuzzy output feedback control approach is developed via the backstepping recursive design technique. It is shown that the proposed control approach can assure that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded. It is also shown that the observer and tracking errors converge to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems

Abstract: This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.

Observer-Based Adaptive Neural Network Control for Nonlinear Stochastic Systems With Time Delay

Abstract: This paper considers the problem of observer-based adaptive neural network (NN) control for a class of single-input single-output strict-feedback nonlinear stochastic systems with unknown time delays. Dynamic surface control is used to avoid the so-called explosion of complexity in the backstepping design process. Radial basis function NNs are directly utilized to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. The proposed adaptive NN output feedback controller can guarantee all the signals in the closed-loop system to be mean square semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the proposed methods.

Adaptive Fuzzy Control via Observer Design for Uncertain Nonlinear Systems With Unmodeled Dynamics

Abstract: In this paper, the problems of stability and tracking control for a class of large-scale nonlinear systems with unmodeled dynamics are addressed by designing the decentralized adaptive fuzzy output feedback approach. Because the dynamic surface control technique is introduced, the designed controllers can avoid the issue of “explosion of complexity,” which comes from the traditional backstepping design procedure that deals with large-scale nonlinear systems with unmodeled dynamics. In addition, a reduced-order observer is designed to estimate those immeasurable states. Based on the Lyapunov stability method, it is proven that all the signals in the closed-loop system are bounded, and the system outputs track the reference signals to a small neighborhood of the origin by choosing the design parameters appropriately. The simulation examples are given to verify the effectiveness of the proposed techniques.

A Review of the Application of Multiobjective Evolutionary Fuzzy Systems: Current Status and Further Directions

Abstract: Over the past few decades, fuzzy systems have been widely used in several application fields, thanks to their ability to model complex systems. The design of fuzzy systems has been successfully performed by applying evolutionary and, in particular, genetic algorithms, and recently, this approach has been extended by using multiobjective evolutionary algorithms, which can consider multiple conflicting objectives, instead of a single one. The hybridization between multiobjective evolutionary algorithms and fuzzy systems is currently known as multiobjective evolutionary fuzzy systems. This paper presents an overview of multiobjective evolutionary fuzzy systems, describing the main contributions on this field and providing a two-level taxonomy of the existing proposals, in order to outline a well-established framework that could help researchers who work on significant further developments. Finally, some considerations of recent trends and potential research directions are presented.

Static-Output-Feedback ${\mathscr H}_{\bm \infty }$ Control of Continuous-Time T - S Fuzzy Affine Systems Via Piecewise Lyapunov Functions

Abstract: This paper investigates the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems with parametric uncertainties and input constraints. The objective is to design a suitable constrained piecewise affine static output feedback controller, guaranteeing the asymptotic stability of the resulting closed-loop fuzzy control system with a prescribed H∞ disturbance attenuation level. Based on a smooth piecewise quadratic Lyapunov function combined with S-procedure and some matrix inequality convexification techniques, some new results are developed for static output feedback controller synthesis of the underlying continuous-time T-S fuzzy affine systems. It is shown that the controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). Finally, three examples are provided to illustrate the effectiveness of the proposed methods.

Sensor Networks With Random Link Failures: Distributed Filtering for T–S Fuzzy Systems

Abstract: The paper is concerned with the problem of distributed fuzzy filter design for a class of sensor networks described by discrete-time T-S fuzzy systems with time-varying delays and multiple probabilistic packet losses. In sensor network, each individual sensor can receive not only its own measurement but also its neighboring sensors' measurements according to the interconnection topology to estimate the system states. Our attention is focused on the design of distributed fuzzy filters to guarantee the filtering error dynamic system to be mean-square asymptotically stable with an average \mathscr H∞ performance. Sufficient conditions for the obtained filtering error dynamic system are proposed by applying an comparison model and the scaled small gain theorem. Based on the measurements and estimates of the system states and its neighbors for each sensor, the solution of the parameters of the distributed fuzzy filters is characterized in terms of the feasibility of a convex optimization problem. Finally, an illustrative example is provided to illustrate the effectiveness of the proposed approaches in sensor networks.

A Combined Backstepping and Stochastic Small-Gain Approach to Robust Adaptive Fuzzy Output Feedback Control

Abstract: In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.

Fuzzy-Model-Based Fault-Tolerant Design for Nonlinear Stochastic Systems Against Simultaneous Sensor and Actuator Faults

Abstract: This paper addresses the problem of fault estimation and fault-tolerant control for a class of Takagi-Sugeno (T-S) fuzzy Ito stochastic systems subject to simultaneously sensor and actuator faults. The main difficulty in this study is that sensor faults, actuator faults, and stochastic noise that are governed by Brownian motion are taken into simultaneous consideration in a unified framework, and traditional fault-tolerant approaches are not effective to solve this research issue. A new descriptor fuzzy sliding-mode observer approach is presented in this paper to obtain the simultaneous estimates of system state, sensor fault, and actuator fault vectors. Based on the state estimates, an observer-based fault-tolerant control (FTC) scheme is developed to stabilize the resulting closed-loop system. Finally, a simulation example is provided to show the effectiveness of the proposed fault-tolerant approach.

Adaptive Output Feedback Control for Nonlinear Time-Delay Systems by Fuzzy Approximation Approach

Abstract: In this paper, the problem of adaptive fuzzy tracking control via output feedback for a class of uncertain single-input single-output (SISO) strict-feedback nonlinear systems with unknown time-delay functions is investigated. Dynamic surface control technique is used to avoid the problem of “explosion of complexity,” which is caused by repeated differentiation of certain nonlinear functions in the backstepping design process. In addition, the fuzzy logic systems are utilized to approximate the unknown and desired control input signals directly instead of the unknown nonlinear functions. The designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded and the tracking error to converge to a small neighborhood of the origin. Simulations results are provided to demonstrate the effectiveness of the proposed methods.

Fault Estimation and Tolerant Control for Fuzzy Stochastic Systems

Abstract: This paper deals with the problem of fault estimation and fault-tolerant control for Takagi-Sugeno (T-S) fuzzy stochastic systems with sensor failures, where the system under consideration contains Ito-type stochastic disturbances A new robust observer technique is presented to obtain the estimates of the system states and the sensor faults simultaneously, and a fuzzy fault-tolerant control scheme is developed to guarantee the closed-loop system to be exponentially stable in mean square Sufficient conditions are obtained for the existence of admissible controllers, and it is shown that the reachability of the sliding-mode dynamics can be guaranteed under the proposed control techniques Finally, a numerical example is provided to illustrate the effectiveness and applicability of the theoretic results obtained

Fuzzy Control of Distributed PV Inverters/Energy Storage Systems/Electric Vehicles for Frequency Regulation in a Large Power System

Abstract: This paper presents a fuzzy based frequency control strategy by the Megawatt (MW) class distributed PV systems and electric vehicles (EVs). The frequency control is proposed from the view point of the frequency fluctuation problem produced by the large penetration of PV power and sudden load variation. The fuzzy based frequency control has three inputs: average insolation, change of insolation and frequency deviation. Following these three inputs, a frequency control system for the distributed PV inverters is proposed. For the case of different insolations in the different areas of the power system, a coordinated control method of the distributed PV inverters, energy storage systems (ESSs) and EVs is presented. The proposed method is simulated by considering dual power and information flows between supply and demand sides in a large power system and is found satisfactory to provide frequency control and to reduce tie-line power fluctuations.

H ∞ Step Tracking Control for Networked Discrete-Time Nonlinear Systems With Integral and Predictive Actions

Abstract: This paper investigates the step tracking control problem for discrete-time nonlinear systems in a networked environment with a limited capacity. The nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy system, and a network-induced delay is incorporated in the modeling of the connection link. In order to compensate for the network link effects and eliminate the tracking error, we employ some techniques mainly used in the predictive control and the integral control. Moreover, a quadratic cost function which includes terms related to the performance of the system and the actuating capacity is used. We assume that the lumped network-induced delay lies within a known set, and that the occurrence probability for each element in the set is known a priori. Then, the delay information will be incorporated into the delay-dependent tracking controllers. The parameters for the tracking controller are derived by solving an optimization problem. A networked inverted pendulum is used to illustrate the efficacy of the proposed design method.

jFuzzyLogic: a Java Library to Design Fuzzy Logic Controllers According to the Standard for Fuzzy Control Programming

Abstract: Fuzzy Logic Controllers are a specific model of Fuzzy Rule Based Systems suitable for engineering applications for which classic control strategies do not achieve good results or for when it is too difficult to obtain a mathematical model. Recently, the International Electrotechnical Commission has published a standard for fuzzy control programming in part 7 of the IEC 61131 norm in order to offer a well defined common understanding of the basic means with which to integrate fuzzy control applications in control systems. In this paper, we introduce an open source Java library called jFuzzyLogic which offers a fully functional and complete implementation of a fuzzy inference system according to this standard, providing a programming interface and Eclipse plugin to easily write and test code for fuzzy control applications. A case study is given to illustrate the use of jFuzzyLogic.

Control Synthesis of Discrete-Time T–S Fuzzy Systems Based on a Novel Non-PDC Control Scheme

Abstract: This paper proposes relaxed stabilization conditions of discrete-time nonlinear systems in the Takagi-Sugeno (T-S) fuzzy form. By using the algebraic property of fuzzy membership functions, a novel nonparallel distributed compensation (non-PDC) control scheme is proposed based on a new class of fuzzy Lyapunov functions. Thus, relaxed stabilization conditions for the underlying closed-loop fuzzy system are developed by applying a new slack variable technique. In particular, some existing fuzzy Lyapunov functions and non-PDC control schemes are special cases of the new Lyapunov function and fuzzy control scheme, respectively. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method.

Induced l 2 filtering of fuzzy stochastic systems with time-varying delays

Abstract: This paper is concerned with the problem of induced l2 filter design for a class of discrete-time Takagi-Sugeno fuzzy Ito stochastic systems with time-varying delays. Attention is focused on the design of the desired filter to guarantee an induced l2 performance for the filtering error system. A new comparison model is proposed by employing a new approximation for the time-varying delay state, and then, sufficient conditions for the obtained filtering error system are derived by this comparison model. A desired filter is constructed by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms. Finally, simulation examples are provided to illustrate the effectiveness of the proposed approaches.

Fuzzy Logic System-Based Adaptive Fault-Tolerant Control for Near-Space Vehicle Attitude Dynamics With Actuator Faults

Abstract: This paper addresses the problem of fault-tolerant control (FTC) for near-space vehicle (NSV) attitude dynamics with actuator faults, which is described by a Takagi-Sugeno (T-S) fuzzy model. First, a general actuator fault model that integrated varying bias and gain faults, which are assumed to be dependent on the system state, is proposed. Then, sliding mode observers (SMOs) are designed to provide a bank of residuals for fault detection and isolation. Based on Lyapunov stability theory, a novel fault diagnostic algorithm is proposed, which removes the classical assumption that the time derivative of the output error should be known. Further, for the two cases where the state is available or not, two accommodation schemes are proposed to compensate for the effect of the faults. These schemes do not need the condition that the bounds of the time derivative of the faults should be known. In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory. Finally, simulation results of NSV are presented to demonstrate the efficiency of the proposed FTC approach.

Adaptive fuzzy output feedback control for a single-link flexible robot manipulator driven DC motor via backstepping ☆

Abstract: In this paper, an adaptive fuzzy output feedback approach is proposed for a single-link robotic manipulator coupled to a brushed direct current (DC) motor with a nonrigid joint. The controller is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring the measurements of link position. Using fuzzy logic systems to approximate the unknown nonlinearities, an adaptive fuzzy filter observer is designed to estimate the immeasurable states. By combining the adaptive backstepping and dynamic surface control (DSC) techniques, an adaptive fuzzy output feedback control approach is developed. Stability proof of the overall closed-loop system is given via the Lyapunov direct method. Three key advantages of our scheme are as follows: (i) the proposed adaptive fuzzy control approach does not require that all the states of the system be measured directly, (ii) the proposed control approach can solve the control problem of robotic manipulators with unknown nonlinear uncertainties, and (iii) the problem of “explosion of complexity” existing in the conventional backstepping control methods is avoided. The detailed simulation results are provided to demonstrate the effectiveness of the proposed controller.

Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference

Abstract: Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with morphological operators and fuzzy inference is proposed in this paper. The effectiveness of the morphological operators lies with the correct selection of structuring elements (SEs). This paper also proposes a new algorithm for this SE selection based on kurtosis, thereby making the analysis free of empirical methods. When analyzed with three different sets of faults, the results show that this method is effective and robust in bringing out the impulses. With fuzzy inference being coupled to this new technique, it makes the algorithm to be able to detect early faults also.

Nonsynchronized Robust Filtering Design for Continuous-Time T–S Fuzzy Affine Dynamic Systems Based on Piecewise Lyapunov Functions

Abstract: This paper investigates the problem of robust H∞ state estimation for a class of continuous-time nonlinear systems via Takagi-Sugeno (T-S) fuzzy affine dynamic models. Attention is focused on the analysis and design of an admissible full-order filter such that the resulting filtering error system is asymptotically stable with a guaranteed H∞ disturbance attenuation level. It is assumed that the plant premise variables, which are often the state variables or their functions, are not measurable so that the filter implementation with state-space partition may not be synchronous with the state trajectories of the plant. Based on piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality linearization techniques, some new results are presented for the filtering design of the underlying continuous-time T-S fuzzy affine systems. Illustrative examples are given to validate the effectiveness and application of the proposed design approaches.

Robust Stability and Stabilization of Uncertain T–S Fuzzy Systems With Time-Varying Delay: An Input–Output Approach

Abstract: An input-output approach to the stability and stabilization of uncertain Takagi-Sugeno (T-S) fuzzy systems with time-varying delay is proposed in this paper. The time-varying parameter uncertainties are assumed to be norm-bounded, and the delay is intervally time varying. A novel method is employed to approximate the time-varying delay, based on which the considered system is transformed into a feedback interconnection form. The new formulation of the system is comprised of a forward subsystem with constant time delay and a feedback subsystem embedding the uncertainties. By applying the scaled small-gain theorem to the converted system, less conservative stability and stabilization criteria are obtained. Moreover, the applicability of the proposed approach to the robust case is simpler since both delay and parameter uncertainties are processed in a unified framework. Numerical experiments are performed to illustrate the advantage of the proposed techniques.

Particle swarm optimization of interval type-2 fuzzy systems for FPGA applications

Abstract: This paper proposes the optimization of the type-2 membership functions for the average approximation of an interval of type-2 fuzzy controller (AT2-FLC) using PSO, where the optimization only considers certain points of the membership functions and, the fuzzy rules are not modified so that the algorithm minimizes the runtime. The AT2-FLC regulates the speed of a DC motor and is coded in VHDL for a FPGA Xilinx Spartan 3A. We compared the results of the optimization using PSO method with a genetic algorithm optimization of an AT2-FLC under uncertainty and the results are discussed. The main contribution of the paper is the design, simulation and implementation of PSO optimization of interval tye-2 fuzzy controllers for FPGA applications.