Showing papers on "Fuzzy control system published in 2012"

Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault

Abstract: This paper is focused on reliable fuzzy H∞ controller design for active suspension systems with actuator delay and fault. The Takagi-Sugeno (T-S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy H∞ performance analysis criterion is derived for the proposed T-S fuzzy model. Then, a reliable fuzzy H∞ controller is designed such that the resulting T-S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed H∞ performance under given constraints. The existence condition of the reliable fuzzy H∞ controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter- vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.

Intelligent Frequency Control in an AC Microgrid: Online PSO-Based Fuzzy Tuning Approach

Abstract: Modern power systems require increased intelligence and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more significant today due to the increasing number of microgrids (MGs). The MGs mostly use renewable energies in electrical power production that are varying naturally. These changes and usual uncertainties in power systems cause the classic controllers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge, the present paper addresses a new online intelligent approach by using a combination of the fuzzy logic and the particle swarm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control synthesis is compared with the pure fuzzy PI and the Ziegler-Nichols PI control design methods.

Adaptive Fuzzy Output Feedback Tracking Backstepping Control of Strict-Feedback Nonlinear Systems With Unknown Dead Zones

Abstract: In this paper, an adaptive fuzzy backstepping control approach is considered for a class of nonlinear strict-feedback systems with unknown functions, unknown dead zones, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy filters state observer is designed to estimate the immeasurable states. By using the adaptive backstepping recursive design technique and constructing the dead-zone inverse, a new adaptive fuzzy backstepping output-feedback control approach is developed. It is mathematically proved that all the signals of the resulting closed-loop adaptive control system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin by appropriate choice of design parameters. The proposed approach cannot only solve the problem of the dead zones but also cancel the restrictive assumption in the previous literature that the states are all available for measurement. Two simulation examples are provided to show the effectiveness of the proposed approach.

A Novel Approach to Filter Design for T–S Fuzzy Discrete-Time Systems With Time-Varying Delay

Abstract: In this paper, the problem of l2- l∞ filtering for a class of discrete-time Takagi-Sugeno (T-S) fuzzy time-varying delay systems is studied. Our attention is focused on the design of full- and reduced-order filters that guarantee the filtering error system to be asymptotically stable with a prescribed H∞ performance. Sufficient conditions for the obtained filtering error system are proposed by applying an input-output approach and a two-term approximation method, which is employed to approximate the time-varying delay. The corresponding full- and reduced-order filter design is cast into 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.

Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach

Abstract: Controlling nonstrict-feedback nonlinear systems is a challenging problem in control theory. In this paper, we consider adaptive fuzzy control for a class of nonlinear systems with nonstrict-feedback structure by using fuzzy logic systems. A variable separation approach is developed to overcome the difficulty from the nonstrict-feedback structure. Furthermore, based on fuzzy approximation and backstepping techniques, a state feedback adaptive fuzzy tracking controller is proposed, which guarantees that all of the signals in the closed-loop system are bounded, while the tracking error converges to a small neighborhood of the origin. Simulation studies are included to demonstrate the effectiveness of our results.

Observer-Based Adaptive Fuzzy Backstepping Output Feedback Control of Uncertain MIMO Pure-Feedback Nonlinear Systems

Abstract: This paper is concerned with the problem of adaptive fuzzy tracking control for a class of uncertain multiple-input-multiple-output (MIMO) pure-feedback nonlinear systems with immeasurable states. The dynamic output feedback strategy begins with a state observer. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions. The filtered signals are introduced to circumvent algebraic loop problem encountered in the implementation of the controller, and an adaptive fuzzy output feedback is obtained via a backstepping recursive design technique. It is shown that the proposed control law can guarantee that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded and that the observer and tracking errors converge to a small neighborhood of the origin. Simulation studies are included to illustrate the effectiveness and potentials of the proposed techniques.

A review on the design and optimization of interval type-2 fuzzy controllers

Abstract: A review of the methods used in the design of interval type-2 fuzzy controllers has been considered in this work. The fundamental focus of the work is based on the basic reasons for optimizing type-2 fuzzy controllers for different areas of application. Recently, bio-inspired methods have emerged as powerful optimization algorithms for solving complex problems. In the case of designing type-2 fuzzy controllers for particular applications, the use of bio-inspired optimization methods have helped in the complex task of finding the appropriate parameter values and structure of the fuzzy systems. In this review, we consider the application of genetic algorithms, particle swarm optimization and ant colony optimization as three different paradigms that help in the design of optimal type-2 fuzzy controllers. We also mention alternative approaches to designing type-2 fuzzy controllers without optimization techniques. We also provide a comparison of the different optimization methods for the case of designing type-2 fuzzy controllers.

Original Article: Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules

Abstract: As people have interests in their health recently, development of medical domain application has been one of the most active research areas. One example of the medical domain application is the detection system for heart disease based on computer-aided diagnosis methods, where the data are obtained from some other sources and are evaluated based on computer-based applications. Earlier, the use of computer was to build a knowledge based clinical decision support system which uses knowledge from medical experts and transfers this knowledge into computer algorithms manually. This process is time consuming and really depends on medical experts' opinions which may be subjective. To handle this problem, machine learning techniques have been developed to gain knowledge automatically from examples or raw data. Here, a weighted fuzzy rule-based clinical decision support system (CDSS) is presented for the diagnosis of heart disease, automatically obtaining knowledge from the patient's clinical data. The proposed clinical decision support system for the risk prediction of heart patients consists of two phases: (1) automated approach for the generation of weighted fuzzy rules and (2) developing a fuzzy rule-based decision support system. In the first phase, we have used the mining technique, attribute selection and attribute weightage method to obtain the weighted fuzzy rules. Then, the fuzzy system is constructed in accordance with the weighted fuzzy rules and chosen attributes. Finally, the experimentation is carried out on the proposed system using the datasets obtained from the UCI repository and the performance of the system is compared with the neural network-based system utilizing accuracy, sensitivity and specificity.

An Improved Direct Torque Control for Three-Level Inverter-Fed Induction Motor Sensorless Drive

Abstract: A sensorless three-level neutral-point-clamped inverter-fed induction motor drive is proposed in this paper. The conventional direct torque control (DTC) switching table fails to consider the circuit limitations, such as neutral-point-balance and smooth vector switching, caused by the topology of a three-level inverter. Two kinds of modified schemes for three-level DTC are proposed to solve these problems. They also provide performance enhancement while maintaining robustness and simplicity. Fuzzy logic control and the speed-adaptive flux observer (with novel gain and load toque observation) are introduced to enhance the performance of the system. The issue of large starting current is investigated and solved by introducing the technique of preexcitation. A 32-bit fixed-point DSP-based motor drive is developed to achieve high-performance sensorless control over a wide speed range. The effectiveness of the proposed schemes is confirmed by simulation implementation and experimental validation.

Fuzzy-Model-Based Robust Fault Detection With Stochastic Mixed Time Delays and Successive Packet Dropouts

Abstract: This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi-Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

Observer-Based Piecewise Affine Output Feedback Controller Synthesis of Continuous-Time T–S Fuzzy Affine Dynamic Systems Using Quantized Measurements

Abstract: This paper is concerned with the problem of robust H∞ output feedback control for a class of continuous-time Takagi-Sugeno (T-S) fuzzy affine dynamic systems using quantized measurements. The objective is to design a suitable observer-based dynamic output feedback controller that guarantees the global stability of the resulting closed-loop fuzzy system with a prescribed H∞ disturbance attenuation level. Based on common/piecewise quadratic Lyapunov functions combined with S-procedure and some matrix inequality convexification techniques, some new results are developed to the controller synthesis for the underlying continuous-time T-S fuzzy affine systems with unmeasurable premise variables. All the solutions to the problem are formulated in the form of linear matrix inequalities (LMIs). Finally, two simulation examples are provided to illustrate the advantages of the proposed approaches.

Fault-Tolerant Control for T–S Fuzzy Systems With Application to Near-Space Hypersonic Vehicle With Actuator Faults

Abstract: This paper addresses the problem of fault-tolerant control for Takagi-Sugeno (T-S) fuzzy systems with actuator faults First, a general actuator fault model is proposed, which integrates time-varying bias faults and time-varying gain faults 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 to estimate the actuator fault, which removes the classical assumption that the time derivative of the output errors should be known as in some existing work Further, a novel fault-estimation observer is designed Utilizing the estimated actuator fault, an accommodation scheme is proposed to compensate for the effect of the fault In addition, a sufficient condition for the existence of SMOs is derived according to Lyapunov stability theory Finally, simulation results of a near-space hypersonic vehicle are presented to demonstrate the efficiency of the proposed approach

Adaptive Fuzzy Logic Control of Permanent Magnet Synchronous Machines With Nonlinear Friction

Abstract: In this paper, an adaptive fuzzy control scheme is introduced for permanent magnet synchronous machines (PMSMs). The adaptive control strategy consists of a Lyapunov stability-based fuzzy speed controller that capitalizes on the machine's inverse model to achieve accurate tracking with unknown nonlinear system dynamics. As such, robustness to modeling and parametric uncertainties is achieved. Moreover, no explicit currents loop regulation is needed, which simplifies the control structure and unlike other control strategies, no a priori offline training, weights initialization, parameters knowledge, voltage, or current transducer is required. The system's convergence and stability are proved by Lyapunov stability theory, which yields an improved performance. Simulation results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions. Furthermore, the adaptive fuzzy systems inherent parallelism makes them a good candidate for implementation in real-time PMSM drive systems.

Fuzzy Adaptive Internal Model Control Schemes for PMSM Speed-Regulation System

Abstract: In this paper, the speed regulation problem for permanent magnet synchronous motor (PMSM) system under vector control framework is studied. First, a speed regulation scheme based on standard internal model control (IMC) method is designed. For the speed loop, a standard internal model controller is first designed based on a first-order model of PMSM by analyzing the relationship between reference quadrature axis current and speed. For the two current loops, PI algorithms are employed respectively. Second, considering the disadvantages that the standard IMC method is sensitive to control input saturation and may lead to poor speed tracking and load disturbance rejection performances, a modified IMC scheme is developed based on a two-port IMC method, where a feedback control term is added to form a composite control structure. Third, considering the case of large variations of load inertia, two adaptive IMC schemes with two different adaptive laws are proposed. A method based on disturbance observer is adopted to identify the inertia of PMSM and its load. Then a linear adaptive law is developed by analyzing the relationship between the internal model and identified inertia. Considering the control input saturation in practical applications, a fuzzy adaptive law based IMC scheme is developed based on apriori experimental tests and experiences, where a fuzzy inferencer based supervisor is designed to automatically tune the parameter of speed controller according to the identified inertia. The effectiveness of the proposed methods have been verified by Matlab simulation and TMS320F2808 DSP experimental results.

Fuzzy Logic-Based Load-Frequency Control Concerning High Penetration of Wind Turbines

Abstract: Load-frequency control (LFC) in interconnected power systems is undergoing fundamental changes due to rapidly growing amount of wind turbines, and emerging of new types of power generation/consumption technologies. The infrastructure of modern LFC systems should be able to handle complex multiobjective regulation optimization problems characterized by a high degree of diversification in policies, and widely distribution in demand and supply sources to ensure that the LFC systems are capable to maintain generation-load balance, following serious disturbances. Wind power fluctuations impose additional power imbalance to the power system and cause frequency deviation from the nominal value. This paper addresses a new decentralized fuzzy logic-based LFC schemes for simultaneous minimization of system frequency deviation and tie-line power changes, which is required for successful operation of interconnected power systems in the presence of high-penetration wind power. In order to obtain an optimal performance, the particle swarm optimization technique is used to determine membership functions parameters. The physical and engineering aspects have been fully considered, and to demonstrate effectiveness of the proposed control scheme, a time domain simulation is performed on the standard 39-bus test system. The results are compared with conventional LFC design for serious load disturbance and various rates of wind power penetrations.

jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation

Abstract: This work introduces jFuzzyLogic, an open source library for fuzzy systems which allow us to design Fuzzy Logic Controllers supporting the standard for Fuzzy Control Programming published by the International Electrotechnical Commission. This library is written in Java and is available as open source from jfuzzylogic.sourceforge.net. The use of jFuzzyLogic is illustrated through the analysis of one case study.

Fuzzy-Model-Based Control of an Overhead Crane With Input Delay and Actuator Saturation

Abstract: This paper investigates the problem of a Takagi-Sugeno (T-S) fuzzy-model-based control of a nonlinear overhead crane system with input delay and actuator saturation. The complex nonlinear dynamic system of the crane is modeled as a three-rule T-S fuzzy model with a saturated input. Based on the fuzzy model, a state-feedback controller is designed so that trajectories of the system that start from an ellipsoid will remain in it, where a decay rate is introduced to accelerate the response speed. Besides, since the input delay often appears in real equipment, the delayed feedback control is also considered with respect to the actuator saturation. Delay-dependent existence conditions of the fuzzy controller are established such that the load can be placed in a desired position by the crane with a much suppressed swing angle, where trajectories of the closed-loop system that start from a bounded set will asymptotically converge to a contractively invariant ellipsoid. The results are formulated in the form of linear matrix inequalities, which can be readily solved via standard numerical software. Simulations on the true plant are illustrated to show the feasibility and effectiveness of the proposed control method.

Finite-Time $H_{\infty}$ Fuzzy Control of Nonlinear Jump Systems With Time Delays Via Dynamic Observer-Based State Feedback

Abstract: This paper studies the finite-time H∞ control problem for time-delay nonlinear jump systems via dynamic observer-based state feedback by the fuzzy Lyapunov-Krasovskii functional approach The Takagi-Sugeno (T-S) fuzzy model is first employed to represent the presented nonlinear Markov jump systems (MJSs) with time delays Based on the selected Lyapunov-Krasovskii functional, the observer-based state feedback controller is constructed to derive a sufficient condition such that the closed-loop fuzzy MJSs is finite-time bounded and satisfies a prescribed level of H∞ disturbance attenuation in a finite time interval Then, in terms of linear matrix inequality (LMIs) techniques, the sufficient condition on the existence of the finite-time H∞ fuzzy observer-based controller is presented and proved The controller and observer can be obtained directly by using the existing LMIs optimization techniques Finally, a numerical example is given to illustrate the effectiveness of the proposed design approach

Robust Nonfragile $H_\infty$ Filtering of Fuzzy Systems With Linear Fractional Parametric Uncertainties

Abstract: This paper is concerned with the H∞ filtering problem for continuous-time Takagi-Sugeno (T-S) fuzzy systems. Different from existing results for fuzzy H∞ filtering, the proposed ones are toward uncertain fuzzy systems with linear fractional parametric uncertainties. Attention is focused on the design of a fuzzy filter such that the filtering error system preserves a prescribed H∞ performance, where the filter to be designed is assumed to have gain variations. By a descriptor representation approach, two sufficient conditions for the H∞ filter design are proposed in terms of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of the desired filter is given. A simulation example will be given to show the efficiency of the proposed methods.

Reliable $H_\infty$ Control for Discrete-Time Fuzzy Systems With Infinite-Distributed Delay

Abstract: In this paper, the problem of reliable H∞ control is investigated for discrete-time Takagi-Sugeno (T-S) fuzzy systems with infinite-distributed delay and actuator faults. A discrete-time homogeneous Markov chain is used to represent the stochastic behavior of actuator faults. In terms of a stochastic fuzzy Lyapunov functional, a sufficient condition is proposed to ensure that the resultant closed-loop system is exponentially stable in the mean-square sense with an H∞ performance index. Based on the derived condition, the reliable H∞ control problem is solved, and an explicit expression of the desired controller is also given. The case of no failure in the actuator is also considered. A numerical example is given to demonstrate that our results are effective and less conservative.

Fault Estimation Observer Design for Discrete-Time Takagi–Sugeno Fuzzy Systems Based on Piecewise Lyapunov Functions

Abstract: This paper studies the problem of robust fault estimation (FE) observer design for discrete-time Takagi-Sugeno (T-S) fuzzy systems via piecewise Lyapunov functions. Both the full-order FE observer (FFEO) and the reduced-order FE observer (RFEO) are presented. The objective of this paper is to establish a novel framework of the FE observer with less conservatism. First, under the multiconstrained design, an FFEO is proposed to achieve FE for discrete-time T-S fuzzy models. Then, using a specific coordinate transformation, an RFEO is constructed, which results in a new fault estimator to realize FE using current output information. Furthermore, by the piecewise Lyapunov function approach, less conservative results on both FFEO and RFEO are derived by introducing slack variables. Simulation results are presented to illustrate the advantages of the theoretic results that are obtained in this paper.

Rotating-Sliding-Line-Based Sliding-Mode Control for Single-Phase UPS Inverters

Abstract: A new method to the sliding-mode control of single-phase uninterruptible-power-supply inverters is introduced. The main idea behind this new method is to utilize a time-varying slope in the sliding surface function. It is shown that the sliding line with the time-varying slope can be rotated in the phase plane in such a direction that the tracking time of the output voltage is improved during load variations. The adjustment of the time-varying slope is achieved dynamically by employing a simple function which involves the error variables of the system. This function is obtained from the input/output relationship of the single-input fuzzy logic controller operating on the error variables. When a newly computed slope value is applied to the system, the position of the representative point is changed so as to achieve the desired response. The performance of the proposed control method has been tested through computer simulations and experiments using a triac-controlled resistive load and a diode bridge rectifier load. The results of the proposed method are compared with a classical sliding mode controller and a standard controller. It has been shown that the proposed method is capable of shortening the tracking and sliding times, resulting in a smaller total harmonic distortion in the output voltage.

LMI Solution for Robust Static Output Feedback Control of Discrete Takagi–Sugeno Fuzzy Models

Abstract: This paper deals with the stabilization problem of discrete-time Takagi-Sugeno (T-S) fuzzy systems via static output controller (SOFC). The proposed method uses the descriptor approach to study this problem and leads to strict linear matrix inequality (LMI ) formulation. In contrast with the existing results, the method allows coping with multiple output matrices, as well as uncertainties. Moreover, the new proposed method can lead to less conservative results by introducing slack variables and considering multiple Lyapunov matrices. A robust SOFC for uncertain T-S fuzzy models is also derived in strict LMI terms. Numerical examples are given to illustrate the effectiveness of the proposed design results.

An Improved Input Delay Approach to Stabilization of Fuzzy Systems Under Variable Sampling

Abstract: In this paper, we investigate the problem of stabilization for sampled-data fuzzy systems under variable sampling. A novel Lyapunov-Krasovskii functional (LKF) is defined to capture the characteristic of sampled-data systems, and an improved input delay approach is proposed. By the use of an appropriate enlargement scheme, new stability and stabilization criteria are obtained in terms of linear matrix inequalities (LMIs). Compared with the existing results, the newly obtained ones contain less conservatism. Some illustrative examples are given to show the effectiveness of the proposed method and the significant improvement on the existing results.

Fuzzy Sliding Mode Speed Controller for PM Synchronous Motors With a Load Torque Observer

Abstract: This paper investigates the robust stabilization problem of a permanent magnet synchronous motor (PMSM). A fuzzy sliding mode speed controller with a load torque observer is designed, which can effectively mitigate chattering and guarantee robust speed control of a PMSM under model parameter and load torque variations. Furthermore, the proposed control method considers the disturbance inputs representing the system nonlinearity or the unmodeled uncertainty. The proposed control algorithm is implemented using a TMS320F28335 floating point DSP. Finally, simulation and experimental results are presented to validate the effectiveness of the proposed scheme.

DSP-Based Cross-Coupled Synchronous Control for Dual Linear Motors via Intelligent Complementary Sliding Mode Control

Abstract: A digital signal processor (DSP)-based cross-coupled intelligent complementary sliding mode control (ICSMC) system is proposed in this paper for the synchronous control of a dual linear motor servo system. The dual linear motor servo system with two parallel permanent magnet linear synchronous motors is installed in a gantry position stage. The dynamic model of single-axis motion control system with a lumped uncertainty, which comprises parameter variations, external disturbances, and nonlinear friction force, is introduced first. Then, to achieve an accurate trajectory tracking performance with robustness, a cross-coupled ICSMC is developed. In this approach, a Takagi-Sugeno-Kang-type fuzzy neural network estimator with accurate approximation capability is implemented to estimate the lumped uncertainty. Moreover, since a cross-coupled technology is incorporated into the proposed intelligent control scheme for the gantry position stage, both the position tracking and synchronous errors of the dual linear motors will simultaneously converge to zero. Furthermore, to effectively demonstrate the control performance of the proposed intelligent control approach, a 32-b floating-point DSP-based control computer is developed for the implementation of the proposed cross-coupled ICSMC system. Finally, some experimental results are illustrated to show the validity of the proposed control approach.

Robust Adaptive Control of MEMS Triaxial Gyroscope Using Fuzzy Compensator

Abstract: In this paper, a robust adaptive control strategy using a fuzzy compensator for MEMS triaxial gyroscope, which has system nonlinearities, including model uncertainties and external disturbances, is proposed. A fuzzy logic controller that could compensate for the model uncertainties and external disturbances is incorporated into the adaptive control scheme in the Lyapunov framework. The proposed adaptive fuzzy controller can guarantee the convergence and asymptotical stability of the closed-loop system. The proposed adaptive fuzzy control strategy does not depend on accurate mathematical models, which simplifies the design procedure. The innovative development of intelligent control methods incorporated with conventional control for the MEMS gyroscope is derived with the strict theoretical proof of the Lyapunov stability. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances compared with conventional adaptive control method.

On ${\cal H}_{\infty }$ Filtering for Discrete-Time Takagi–Sugeno Fuzzy Systems

Abstract: In this paper, we present a new design method for the H∞ filtering of discrete-time Takagi-Sugeno (TS) fuzzy systems. The parameters of the filter are assumed to be linearly dependent on the normalized fuzzy weighting functions. By using an augmentation technique, the design parameters are incorporated into a filtering error system. In order to derive less-conservative results and reduce the filtering error, a new condition is established to ensure the H∞ performance of the filtering error system. By introducing more slack matrices, the solution set of the filter parameters is extended. By using a partitioning technique, a design method for the H∞ filter is proposed in terms of linear matrix inequalities (LMIs). An example demonstrates the improvement of the proposed design method over an existing approach.