Fuzzy Integral Sliding Mode Control Research Articles

Adaptive Fuzzy Integral Sliding-Mode Control for Robust Fault-Tolerant Control of Robot Manipulators With Disturbance Observer

This article develops a new strategy for robust fault-tolerant control (FTC) of robot manipulators using an adaptive fuzzy integral sliding-mode control (ISMC) and a disturbance observer (DO). First, an ISMC is developed for the FTC system. The major features of the approach are discussed. Then, to enhance the performance of the system, a fuzzy logic system approximation and a DO are introduced to approximate the unknown nonlinear terms, which include the model uncertainty and fault components, and to estimate the compounded disturbance and then are integrated into the ISMC. Next, a switching term based on an adaptive two-layer supertwisting algorithm is designed to compensate the disturbance estimated error and guarantee stability and convergence of the whole system. The nominal controller of the ISMC is reconstructed using a backstepping control technique to achieve the stability for the nominal system based on the Lyapunov criterion. The computer simulation results demonstrate the effectiveness of the proposed approach.

Soft computing-based fuzzy integral sliding mode control: a real-time investigation on a conical tank process

In this work, a fuzzy integral sliding mode controller (FISMC) for level control in a conical tank process is demonstrated in real time. In traditional sliding mode controller (SMC) algorithm, the robustness with respect to parameter variations and external disturbances can be achieved only after the reach of sliding phase. However, robustness is not guaranteed during the reaching phase. But integral sliding mode controller (ISMC) hunts to eliminate the reaching phase by imposing sliding mode throughout the system response. ISMC also mitigates chattering caused by discontinuity of controller. Hence, integral of error term is used in the sliding surface. A modified power rate reaching law is proposed to describe the dynamics of the switching function. The fuzzy logic system is integrated to approximate the sliding variable, and control law is formulated so as to alleviate the chattering effect of the control signal. In this paper, Takagi and Sugeno fuzzy logic is integrated with ISMC to achieve smoother sliding surface. Genetic algorithm (GA) is used to tune the membership functions of fuzzy logic and the parameters of the control law. GA-tuned FISMC integrates the features of fuzzy logic control, SMC and soft computing techniques. The effectiveness of algorithm is demonstrated in an experimental setup. The reported results confirm the superiority of GAFISMC compared with proportional integral controller and ISMC algorithm. The real-time implementation ensures the robustness of GAFISMC in terms of operating-level variations, parameter variations and disturbance rejection.

Robust Decentralized Adaptive Fuzzy Integral Sliding Mode Control of Mismatched Uncertain Large-scale Systems

Robust Decentralized Adaptive Fuzzy Integral Sliding Mode Control of Mismatched Uncertain Large-scale Systems

Design of Observer-Based Event-Triggered Fuzzy ISMC for T–S Fuzzy Model and its Application to PMSG

The main aim of this article is to design the observer-based event-triggered (ET) fuzzy integral sliding mode control (ETFISMC) for the generalized Takagi-Sugeno (T-S) fuzzy system which is formulated from a nonlinear system through blending the membership grades altogether. Distinct to the existing controller schemes, the proposed fuzzy integral sliding mode control (FISMC) scheme contains the ET condition which needs to be satisfied for the activation of the controller. Besides that, the network-induced communication constraints are considered into the derivation of sufficient conditions, and then the corresponding stabilization issue is attenuated in the sense of H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control performance. In addition, the appropriate Lyapunov-Krasovskii functional (LKF) candidate is constructed and evaluated through convex matrix inequality approach that ensures the stable H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance of the closed-loop system in terms of solvable linear matrix inequalities (LMIs). Further, instead of considering the general problem for the validation of the proposed result, the stabilization problem of nonlinear chaotic permanent magnet synchronous generator (PMSG) model is taken into account for the validation of the proposed sufficient conditions. The purpose of considering the PMSG model is because of its significance in the wind energy conversion systems.

Adaptive Fractional Fuzzy Integral Sliding Mode Control for PMSM Model

This paper aims to address the stabilization problem of permanent magnet synchronous motor (PMSM) based wind energy conversion system (WECS) through a novel adaptive fractional fuzzy integral sliding mode control scheme in contrast to the traditional integer order control schemes. The main objective of modeling the fractional order control for nonlinear PMSM is to enhance the convergence rate which is effectively better when compared to integer order control schemes. In addition, this paper intensively investigates the performance of fractional order controllers in both PMSM and surface-mounted PMSM-based WECS through analyzing the global stability of closed-loop system based on Lyapunov stability theory. In this regard, the nonlinear PMSM model is transformed into equivalent linear submodels through an effective Takagi–Sugeno fuzzy membership rules. Then, a novel automated (adaptive) controller is designed along with fractional sliding surface, which involves an integral term to control the considered PMSM. In general, adaptive controllers are much more effective than manual controllers. Further, the sufficient conditions are derived in terms of linear matrix inequalities via constructing the novel fractional fuzzy Lyapunov functional with quadratic terms, which guarantees the global stabilization of PMSM-based WECS. Overall performance and effectiveness of the proposed theoretical results are demonstrated through numerical simulations.

Dissipativity-Based Asynchronous Fuzzy Sliding Mode Control for T-S Fuzzy Hidden Markov Jump Systems.

This paper investigates the problem of dissipativity-based asynchronous fuzzy integral sliding mode control (AFISMC) for nonlinear Markov jump systems represented by Takagi-Sugeno (T-S) models, which are subject to external noise and matched uncertainties. Since modes of original systems cannot be directly obtained, the hidden Markov model is employed to detect mode information. With the detected mode and the parallel distributed compensation approach, a suitable fuzzy integral sliding surface is devised. Then using Lyapunov function, a sufficient condition for the existence of sliding mode controller gains is developed, which can also ensure the stochastic stability of the sliding mode dynamics with a satisfactory dissipative performance. An AFISMC law is proposed to drive system trajectories into the predetermined sliding mode boundary layer in finite time. For the case with unknown bound of uncertainties, an adaptive AFISMC law is developed as well. The studied T-S fuzzy Markov jump systems involve both continuous-time and discrete-time domains. Finally, some simulation results are presented to demonstrate the applicability and effectiveness of the proposed approaches.

Adaptive fuzzy radial basis function neural network integral sliding mode tracking control for heavy vehicle electro-hydraulic power steering systems

Due to parametric uncertainties, unknown nonlinearities, and dynamic external disturbances, it is a challenging and valuable task for heavy vehicle electro-hydraulic power steering systems to realize high-precision tracking control. To cope with this complex nonlinear tracking control problem, the integral sliding mode control is an extremely potential control method, which has strong robustness to model uncertainties and unknown disturbances, and can effectively reduce the steady-state error in tracking control process. However, the inherent chattering phenomenon of integral sliding mode control seriously affects its control performance. In order to suppress the chattering while ensuring robustness, adaptive fuzzy technique is adopted as an effective auxiliary means, which can not only deal with the inherent chattering problem of integral sliding mode control and a priori knowledge of the disturbance upper bound in controller design but also dynamically adjust the parameters in the fuzzy rules. Moreover, the designed adaptive fuzzy–integral sliding mode control scheme still needs the precise mathematical models of the control systems. But it is difficult to obtain the model for heavy vehicle electro-hydraulic power steering systems with highly complex and coupling properties. Therefore, to further improve the method, this paper presents a novel adaptive fuzzy–radial basis function neural network–integral sliding mode control method for the complex systems to achieve timely and accurate steering angle tracking control. In addition to the advantages of adaptive fuzzy–integral sliding mode control, the modified controller no longer requires the precise mathematical models of heavy vehicle electro-hydraulic power steering systems and realizes the continuous adaptive updating of weights. Finally, the effectiveness and superiority of the proposed control scheme is illustrated by comparisons and extensive simulations.

Observer-Based Fuzzy Integral Sliding Mode Control For Nonlinear Descriptor Systems

This paper investigates observer-based stabilization for nonlinear descriptor systems using a fuzzy integral sliding mode control approach. Observer-based integral sliding mode control strategies for the Takagi–Sugeno (T–S) fuzzy descriptor systems are developed. A two-step design approach is first developed to obtain the observer gains and coefficients in the switching function using linear matrix inequalities (LMIs), and the results are used to facilitate the development of a single-step design approach, which is seen to be convenient but introduces some conservatism in the design. The potential application to a class of mechanical systems is also considered. Since the descriptor system representation of mechanical systems is adopted, it is shown that in contrast to the existing fuzzy sliding mode control methods based on the normal system representation, the resulting T-S fuzzy system does not contain different input matrices for each local subsystem and the required number of fuzzy rules is consequently markedly reduced. Finally, the balancing problem of a pendulum on a car is numerically simulated to demonstrate the effectiveness of the proposed method.

Adaptive fuzzy integral sliding mode control of blood glucose level in patients with type 1 diabetes: In silico studies

Currently, artificial pancreas is an alternative treatment instead of insulin therapy for patients with type 1 diabetes mellitus. Closed-loop control of blood glucose level (BGL) is one of the difficult tasks in biomedical engineering field due to nonlinear time-varying dynamics of insulin-glucose relation that is combined with time delays and model uncertainties. In this paper, we propose a novel adaptive fuzzy integral sliding mode control scheme for BGL regulation. System dynamics is identified online using fuzzy logic systems. The presented method is evaluated in silico studies by nine different virtual patients in three different groups for two continuous days. Simulation results demonstrate effective performance of the proposed control scheme of BGL regulation in presence of simultaneous meal and physical exercise disturbances. Comparison of the proposed control method with proportional–integral–derivative (PID) control and model predictive control (MPC) shows a superiority of the adaptive fuzzy integral sliding mode control with regard to two conventional methods of BGL regulation (PID and MPC) and sliding mode control.

$$H_\infty $$ H ∞ tracking adaptive fuzzy integral sliding mode control for a train of self-balancing vehicles

A series of self-balancing vehicles is stabilized and controlled in this research work. The system is composed of several Segway $$\frac{{\mathrm{TM}}}{}$$ type platforms interconnected through flexible links. The system is inherently highly nonlinear and underactuated which makes the tracking task very challenging. In this paper, $$H_\infty $$ tracking adaptive fuzzy integral sliding mode control scheme is proposed for n-self-balancing interconnected vehicles system where uncertainties and disturbances are included. First, a nonlinear dynamic model with uncertainties for the train system with n-vehicles is derived using the Lagrangian method assuming the vehicles moving in tandem on a inclined path. Then, the dynamics of the train system with n-vehicles is formulated as an error dynamics according to a specified reference signal. A fuzzy technique with an on-line estimation scheme is developed to approximate the dynamics of the train system with n-vehicles. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear uncertain functions in dynamics with an online update law. Using the concept of parallel distributed compensation, the adaptive fuzzy scheme combined with the integral sliding mode control scheme is synthesized to address the system uncertainties and the external disturbances such that $$H_\infty $$ tracking performance is achieved. Simulation results for 2-self-balancing interconnected vehicles system are presented to show the effectiveness and performance of the proposed control scheme.

T-S 퍼지 시스템의 퍼지 적분 슬라이딩 모드 추적 제어기 설계

In this paper, a reference model tracking control for a nonlinear system expressed as a Takagi-Sugeno (T-S) fuzzy model is set through a fuzzy integral sliding mode controller. The stabilization conditions for the sliding motion are derived in terms of a linear matrix inequality, and the fuzzy integral sliding mode controller is designed. Moreover, it is verified through the Lyapunov stability condition that the designed controller allows the error dynamics model to reach the sliding surface. Finally, in the simulation example, it is shown that the proposed method successfully achieves the fuzzy tracking control. In addition, a performance comparison between the proposed method and the existing state feedback control technique is provided.

Notice of Violation of IEEE Publication Principles: Dissipativity-Based Fuzzy Integral Sliding Mode Control of Continuous-Time T-S Fuzzy Systems

This paper is concerned with dissipativity-based fuzzy integral sliding mode control (FISMC) of continuous-time Takagi-Sugeno (T-S) fuzzy systems with matched/unmatched uncertainties and external disturbance. To better accommodate the characteristics of T-S fuzzy models, an appropriate integral-type fuzzy switching surface is put forward by taking the state-dependent input matrix into account, which is the key contribution of the paper. Based on the utilization of Lyapunov function and property of the transition matrix for unmatched uncertainties, sufficient conditions are presented to guarantee the asymptotic stability of corresponding sliding mode dynamics with a strictly dissipative performance. A FISMC law is synthesized to drive system trajectories onto the fuzzy switching surface despite matched/unmatched uncertainties and external disturbance. A modified adaptive FISMC law is further designed for adapting the unknown upper bound of matched uncertainty. Two practical examples are provided to illustrate the effectiveness and advantages of developed FISMC scheme.

Adaptive fuzzy integral sliding mode pressure control for cutter feeding system of trench cutter

A nonlinear pressure controller was presented to track desired feeding pressure for the cutter feeding system (CFS) of trench cutter (TC) in the presence of unknown external disturbances. The feeding pressure control of CFS is subjected to unknown load characteristics of rock or soil; in addition, the geological condition is time-varying. Due to the complex load characteristics of rock or soil, the feeding velocity of TC is related to geological conditions. What is worse, its dynamic model is subjected to uncertainties and its function is unknown. To deal with the particular characteristics of CFS, a novel adaptive fuzzy integral sliding mode control (AFISMC) was designed for feeding pressure control of CFS, which combines the robust characteristics of an integral sliding mode controller and the adaptive adjusting characteristics of an adaptive fuzzy controller. The AFISMC feeding pressure controller is synthesized using the backstepping technique. The stability of the overall closed-loop system consisting of the adaptive fuzzy inference system, integral sliding mode controller and the cutter feeding system is proved using Lyapunov theory. Experiments are conducted on a TC test bench with the AFISMC under different operating conditions. The experimental results demonstrate that the proposed AFISMC feeding pressure controller for CFS gives a superior and robust pressure tracking performance with maximum pressure tracking error within ±0.3 MPa.

Robust observer-based adaptive fuzzy sliding mode controller

In this paper, a new observer-based adaptive fuzzy integral sliding mode controller is proposed based on the Lyapunov stability theorem. The plant is subjected to a square-integrable disturbance and is assumed to have mismatch uncertainties both in state- and input-matrices. Based on the classical sliding mode controller, the equivalent control effort is obtained to satisfy the sufficient requirement of sliding mode controller and then the control law is modified to guarantee the reachability of the system trajectory to the sliding manifold. In order to relax the norm-bounded constrains on the control law and solve the chattering problem of sliding mode controller, a fuzzy logic inference mechanism is combined with the controller. An adaptive law is then introduced to tune the parameters of the fuzzy system on-line. Finally, for evaluating the controller and the robust performance of the closed-loop system, the proposed regulator is implemented on a real-time mechanical vibrating system.

Adaptive fuzzy integral sliding mode velocity control for the cutting system of a trench cutter

This paper presents a velocity controller for the cutting system of a trench cutter (TC). The cutting velocity of a cutting system is affected by the unknown load characteristics of rock and soil. In addition, geological conditions vary with time. Due to the complex load characteristics of rock and soil, the cutting load torque of a cutter is related to the geological conditions and the feeding velocity of the cutter. Moreover, a cutter’s dynamic model is subjected to uncertainties with unknown effects on its function. In this study, to deal with the particular characteristics of a cutting system, a novel adaptive fuzzy integral sliding mode control (AFISMC) is designed for controlling cutting velocity. The model combines the robust characteristics of an integral sliding mode controller with the adaptive adjusting characteristics of an adaptive fuzzy controller. The AFISMC cutting velocity controller is synthesized using the backstepping technique. The stability of the whole system including the fuzzy inference system, integral sliding mode controller, and the cutting system is proven using the Lyapunov theory. Experiments have been conducted on a TC test bench with the AFISMC under different operating conditions. The experimental results demonstrate that the proposed AFISMC cutting velocity controller gives a superior and robust velocity tracking performance.

Research of General Fuzzy Integral Sliding Mode Controller Based on Complex Networks

Research of General Fuzzy Integral Sliding Mode Controller Based on Complex Networks

Adaptive Fuzzy Integral Sliding-Mode Regulator for Induction Motor Using Nonlinear Sliding Surface

An adaptive fuzzy integral sliding-mode controller using nonlinear sliding surface is designed for the speed regulator of a field-oriented induction motor drive in this paper. Combining the conventional integral sliding surface with fractional-order integral, a nonlinear sliding surface is proposed for the integral sliding-mode speed control, which can overcome the windup problem and the convergence speed problem. An adaptive fuzzy control term is utilized to approximate the uncertainty. The stability of the controller is analyzed by Lyapunov stability theory. The effectiveness of the proposed speed regulator is demonstrated by the simulation results in comparison with the conventional integral sliding-mode controller based on boundary layer.

Variable speed wind turbine for maximum power capture using adaptive fuzzy integral sliding mode control

This paper presents a nonlinear control approach to variable speed wind turbine (VSWT) with a wind speed estimator. The dynamics of the wind turbine (WT) is derived from single mass model. In this work, a modified Newton Raphson estimator has been considered for exact estimation of effective wind speed. The main objective of this work is to extract maximum energy from the wind at below rated wind speed while reducing drive train oscillation. In order to achieve the above objectives, VSWT should operate close to the optimal power coefficient. The generator torque is considered as the control input to achieve maximum energy capture. From the literature, it is clear that existing linear and nonlinear control techniques suffer from poor tracking of WT dynamics, increased power loss and complex control law. In addition, they are not robust with respect to input disturbances. In order to overcome the above drawbacks, adaptive fuzzy integral sliding mode control (AFISMC) is proposed for VSWT control. The proposed controller is tested with different types of disturbances and compared with other nonlinear controllers such as sliding mode control and integral sliding mode control. The result shows the better performance of AFISMC and its robustness to input disturbances. In this paper, the discontinuity in integral sliding mode controller is smoothed by using hyperbolic tangent function, and the sliding gain is adapted using a fuzzy technique which makes the controller more robust.

Universal Fuzzy Integral Sliding-Mode Controllers Based on T–S Fuzzy Models

This paper addresses the universal fuzzy integral sliding-mode controllers' problem for continuous-time multi-input multi-output nonlinear systems based on Takagi-Sugeno (T-S) fuzzy models. By using the approximation capability of T-S fuzzy models, the nonlinear systems are expressed by uncertain T-S fuzzy models with norm-bounded approximation errors. A novel fuzzy dynamic integral sliding-mode control (DISMC) scheme is then developed for the nonlinear systems based on their T-S fuzzy approximation models. One of the key features of the new DISMC scheme is that the restrictive assumption that all local linear systems share a common input matrix, which is required in most existing fuzzy integral sliding-mode control (ISMC) approaches, is removed. Furthermore, the results of universal fuzzy ISMCs for two classes of nonlinear systems, along with constructive procedures to obtain the universal fuzzy ISMCs, are provided, respectively. Finally, the advantages and effectiveness of the proposed approaches are illustrated via a numerical example.

Formula omitted] tracking adaptive fuzzy integral sliding mode control for parallel manipulators

Highly nonlinear coupling phenomenon is an inherently inevitable in parallel manipulators in which limbs/links undergo rotating and sliding with/without fixed base. In this paper, an H∞ tracking adaptive fuzzy integral sliding mode control scheme is proposed for controlling parallel manipulators with nonlinear unmodeled dynamics, external disturbances, and limb-to-limb couples in which each coupled uncertainty is assumed to be bounded by an unknown gain. The dynamics of the parallel manipulator is formulated as an error dynamics according to a specified reference model; then, a fuzzy model is used to approximate the uncertainties. Two on-line estimation schemes are developed to overcome the uncertainties and identify the gains of the unknown coupled uncertainty bounds from limb-to-limb couples, simultaneously. The advantage of employing an adaptive fuzzy system is the use of linear analytical results instead of estimating nonlinear uncertain functions with an on-line update law. By the concept of parallel distributed compensation (PDC), the adaptive fuzzy scheme uses an integral sliding mode control scheme to resolve the system uncertainties, unknown limb-to-limb coupled uncertainties, and the external disturbances such that H∞ tracking performance is achieved. The control laws are derived based on a Lyapunov criterion and the Riccati-inequality such that all states of the system are uniformly ultimately bounded (UUB) and the effect on the tracking error can be attenuated to any prescribed level to achieve H∞ tracking performance. Finally, a numerical example of a planar 2-dof parallel robot system is given to verify the effectiveness of the proposed control scheme.