Fuzzy Sliding Model Controller Research Articles

Adaptive Fuzzy Sliding Mode Control for a 3-DOF Parallel Manipulator with Parameters Uncertainties

Parallel manipulators possess the advantages of being compact structure, high stiffness, stability and high accuracy, so such parallel manipulators have been widely employed in application fields as diverse as parallel kinematic machine, motion simulator platform, medical rehabilitation device, and so on. Due to the complexity of the closed-loop structural system, an accurate dynamic model is very difficult to be derived in the absence of some uncertainties parameters and external disturbances. In order to improve the trajectory tracking accuracy with time-varying and nonlinear parameters, this paper addresses the design and implement of adaptive fuzzy sliding mode control (AFSMC) for a three-degree-of-freedom (DOF) parallel manipulator, where internal force term can be linearly separated into a regression matrix and a parameter vector that contains the estimated errors. Furthermore, fuzzy inference unit is utilized to modify the gain parameters in real-time by using the state feedback from the task space and the adaptive law is performed to update uncertainties in dynamic parameters. The proposed controller is deduced in the sense of Lyapunov theory to guarantee the stability while improving the trajectory tracking performance. Finally, simulation experiment results demonstrate that the proposed control method is insensitive to uncertainties and disturbances and permits to decrease the requirement for the bound of these uncertainties, which validate the effectiveness of the developed control method and exhibit good trajectory tracking performance compared with sliding mode control (SMC) and fuzzy sliding model control (FSMC).

Simulation and analysis of laser seeker servo control system based on fuzzy sliding model control

The laser seeker, which is the main dependent object of the missile to achieve reliable target attack, provides the position information of the target for the laser-guided weapon. During the missile flight, the missile's disturbance was coupled to the servo stabilization system of the seeker in various ways. This will affect the stability of the visual axis as well as reducing the accuracy of the seeker in locating the target. The basic structure of the seeker servo system and the basic principle of achieving the stability of the visual axis were analyzed in this paper. In order to solve the disturbance problem of the seeker, a Fuzzy Sliding Mode Control(FSMC) method was proposed. The Sliding Mode Control(SMC) has the advantage of fully adaptive to the disturbances. Besides, it was combined with fuzzy control, which can reduce chattering of SMC. In this way, the robustness of the seeker servo system was improved by FSMC, so did the isolation of the seeker. The simulation results show that compared with PID control, fuzzy sliding mode control can improve the control quality of the system and achieve better control accuracy and response speed.

Double Closed-Loop General Type-2 Fuzzy Sliding Model Control for Trajectory Tracking of Wheeled Mobile Robots

In this paper, a double-loop control scheme, combining general type-2 fuzzy logic controller (GT2FLC) and non-singular terminal sliding mode control (NTSMC), is proposed to stabilize the non-holonomic wheeled mobile robot. In the double-loop tracking controller, the outer ring adopts the exponential approach law used to track the position state quickly and the inner ring adopts the double-power approaching law, which makes the attitude convergence faster than the position to ensure the stability of the closed-loop system and deal with the random disturbances. NTSMC is designed for both loops to achieve fast convergence of the system in a finite time. The GT2FLC is used to adjust the gain of the approaching law, so as to enhance the adaptability to random disturbances and weaken the chattering of sliding mode input. The simulation results show that the proposed method can achieve small error and fast tracking of position and attitude under random disturbance, and its performance is better than other controllers.

Robotic Impedance Control with Fuzzy Sliding Model Control Strategy

During robotic assembly and interactive applications, the robot end-effector must follow a motion trajectory and exert an appropriate force profile against the contacted environment to provide a specified dynamic working compliance. It is a difficult control problem. Here, an embedded robotic control structure is constructed and the related hybrid control software programs are developed. The model-free intelligent fuzzy sliding mode controller is introduced to design force and position controllers, respectively for hybrid impedance control purpose. The experimental results are provided to demonstrate the effectiveness of the proposed hybrid impedance control system.

Decoupled Third-order Fuzzy Sliding Model Control For Cart-Inverted Pendulum System

Cart Inverted Pendulum (CIP) system is a benchmark problem in nonlinear automatic control. It is commonly used to verify the robustness of any proposed nonlinear controller. CIP is mostly represented by two second order differential equations to avoid complexity due to the DC motor dynamics. This representation is not practical for the real CIP dynamics and might lead to instability. Therefore in this paper, two third-order differential equations were derived to combine the pendulum system and DC motor dynamics to have a more realistic mathematical model. Friction between the cart and rail was included in the system equations through a nonlinear friction model. To stabilize the obtained nonlinear electromechanical CIP model, a third-order Fuzzy Sliding Mode Controller (FSMC) was designed. The chattering of the control signal was eliminated using general bill shape membership functions for the Fuzzy controller. Simulation results proved the robustness of the proposed FSMC over Linear Quadratic Regulator Controller (LQRC). For instant, the overshoot in the cart position response was reduced by 300%.

Nonlinear control of benchmark problems using TSK-type fuzzy neural network

This paper proposes a TSK-type fuzzy neural network system (TFNN) for identifying and controlling nonlinear control benchmark problem system. It is available for nonlinear dynamic system with uncertainties. The TFNN system can construct and learn its knowledge base from the input–output training data firstly. Thus, a nonlinear system can be represented by several if-then rules with Gaussian membership functions and TSK-type consequent parts. Based on the learned TFNN system, a robust fuzzy controller is proposed, which combines linear matrix inequality-based fuzzy controller and fuzzy sliding model controller. Rigorous proof of asymptotic stability for the closed-loop system is presented via Lyapunov stability theorem. Several examples are presented to illustrate the effectiveness of our approach.

Fuzzy Sliding-Mode Formation Control for Multirobot Systems: Design and Implementation

This paper mainly addresses the decentralized formation problems for multiple robots, where a fuzzy sliding-mode formation controller (FSMFC) is proposed. The directed networks of dynamic agents with external disturbances and system uncertainties are discussed in consensus problems. To perform a formation control and to guarantee system robustness, a novel formation algorithm combining the concepts of graph theory and fuzzy sliding-model control is presented. According to the communication topology, formation stability conditions can be determined so that an FSMFC can be derived. By Lyapunov stability theorem, not only the system stability can be guaranteed, but the desired formation pattern of a multirobot system can be also achieved. Simulation results are provided to demonstrate the effectiveness of the provided control scheme. Finally, an experimental setup for the e-puck multirobot system is built. Compared to first-order formation algorithm and fuzzy neural network formation algorithm, it shows that real-time experimental results empirically support the promising performance of desire.

Linear Matrix Inequality Conditions of Nonlinear Systems by Genetic Algorithm-based H ∞ Adaptive Fuzzy Sliding Mode Controller

In this paper, the stability analysis of a genetic algorithm-based (GA-based) H∞ adaptive fuzzy sliding model controller (AFSMC) for a nonlinear system is discussed. First, a nonlinear plant is well-approximated and described with a reference model and a fuzzy model, both involving fuzzy logic control (FLC) rules. Then, FLC rules and the consequent parameter are decided on via a genetic algorithm. After this, we guarantee a new H∞ tracking performance inequality for the control system. The H∞ tracking problem is characterized to solve an eigenvalue problem. Next, an AFSMC is proposed to stabilize the system so as to achieve good H∞ control performance. Lyapunov’s direct method can be used to ensure the stability of the nonlinear system. It is shown that the stability analysis can reduce nonlinear systems into a linear matrix inequality problem. Finally, a numerical simulation is provided to demonstrate the control methodology.

A NOVEL STABILITY CONDITION AND ITS APPLICATION TO GA-BASED FUZZY CONTROL FOR NONLINEAR SYSTEMS WITH UNCERTAINTY

In this study, we strive to combine the advantages of fuzzy logic control (FLC), genetic algorithms (GA), H(subscript ∞) tracking control schemes, smooth control and adaptive laws to design an adaptive fuzzy sliding model controller for the rapid and efficient stabilization of complex and nonlinear systems. First, we utilize a reference model and a fuzzy model (both involving FLC rules) to describe and well-approximate an uncertain, nonlinear plant. The FLC rules and the consequent parameter are decided on via GA. A boundary-layer function is introduced into these updated laws to cover modeling errors and to guarantee that the state errors converge into a specified error bound. After this, a H(subscript ∞) tracking problem is characterized. We solve an eigenvalue problem (EVP), and derive a modified adaptive neural network controller (MANNC) to simultaneously stabilize and control the system and achieve H(subscript ∞) control performance. Furthermore, a stability criterion is derived utilizing Lyapunov's direct method to ensure the stability of the nonlinear system. Finally, the control methodology is demonstrated via a numerical simulation.

Adaptive Fuzzy Sliding Controller with Dynamic Compensation for Multi-Axis Machining

The precision of multi-axis machining is deeply influenced by the tracking error of multi-axis control system. Since the multi-axis machine tools have nonlinear and time-varying behaviors, it is difficult to establish an accurate dynamic model for multi-axis control system design. In this paper, a novel adaptive fuzzy sliding model controller with dynamic compensation is proposed to reduce tracking error and to improve precision of multi-axis machining. The major ad-vantage of this approach is to achieve a high following speed without overshooting while maintaining a continuous CNC machine tool process. The adaptive fuzzy tuning rules are derived from a Lyapunov function to guarantee stability of the control system. The experimental results on GJ-110 show that the proposed control scheme effectively minimizes tracking errors of the CNC system with control performance surpassing that of a traditional PID controller.

GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems

In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented. First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules. Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. The initial values of the consequent parameter vector are decided via the genetic algorithm. After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller. The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov’s direct method. Finally, a numerical simulation is provided as an example to demonstrate the control methodology.

Active Queue Management Algorithm Based on Fuzzy Sliding Model Controller

The active queue management (AQM) problem of networks is discussed. An AQM scheme is presented based on improved sliding model controller. The proposed controller combines the excellent characteristics of linear sliding model controller (LSMC) and the terminal sliding model controller (TSMC). The LSMC is used to speed up the error convergence when the error is greater than one, and the TSMC is adopted to guarantee the error convergence to zero in a finite time when the error is around the zero. The chattering in the conventional sliding model control systems is avoided with the employed continuous controller. The simulation results show that the proposed scheme has strong robust against the network modeling uncertainties and disturbances, as well as leads to the convergence of the output queue to the desired value quickly and precisely than employing either LSMC or TSMC alone.

GA‐Based Fuzzy Sliding Mode Controller for Nonlinear Systems

Generally, the greatest difficulty encountered when designing a fuzzy sliding mode controller (FSMC) or an adaptive fuzzy sliding mode controller (AFSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. In this paper, we describe a method of stability analysis for a GA‐based reference adaptive fuzzy sliding model controller capable of handling these types of problems for a nonlinear system. First, we approximate and describe an uncertain and nonlinear plant for the tracking of a reference trajectory via a fuzzy model incorporating fuzzy logic control rules. Next, the initial values of the consequent parameter vector are decided via a genetic algorithm. After this, an adaptive fuzzy sliding model controller, designed to simultaneously stabilize and control the system, is derived. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov′s direct method. Finally, an example, a numerical simulation, is provided to demonstrate the control methodology.

Observer-based indirect adaptive fuzzy sliding mode control with state variable filters for unknown nonlinear dynamical systems

This paper proposes an observer-based indirect adaptive fuzzy sliding mode controller with state variable filters for a certain class of unknown nonlinear dynamic systems in which not all the states are available for measurement. To design the proposed controller, we first construct the fuzzy models to describe the input/output behavior of the nonlinear dynamic system. Then, an observer is employed to estimate the tracking error vector. Based on the observer, a fuzzy sliding model controller is developed to achieve the tracking performance. Then, a filtered observation error vector is obtained by passing the observation error vector to a set of state variable filters. Finally, on the basis of the filtered observation error vector, the adaptive laws are proposed to adjust the free parameters of the fuzzy models. The stability of the overall control system is analyzed based on the Lyapunov method. Simulation results illustrate the design procedures and demonstrate the tracking performance of the proposed controller.