Showing papers on "Fuzzy control system published in 2019"
TL;DR: This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states, and relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure- feedback non linear systems.
Abstract: This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a $priori$ knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.
408 citations
TL;DR: A finite-time fuzzy adaptive control scheme is presented to overcome the “explosion of complexity” problem for a class of multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems.
Abstract: This paper investigates the finite-time adaptive fuzzy control problem for a class of multi-input and multi-output (MIMO) nonlinear nonstrict feedback systems. During the control design process, fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and fuzzy state observer is constructed to estimate the unmeasured states. By combining adaptive backstepping with the dynamic surface control (DSC) technique, a finite-time fuzzy adaptive control scheme is presented to overcome the “explosion of complexity” problem. The stability of the close-loop systems can be proved based on the finite-time Lyapunov stability theory. The presented control scheme demonstrates that the closed-loop systems are semiglobal practical finite-time stability, and tracking errors converge to a small neighborhood of the origin in a finite time. Finally, two simulation examples are provided to show the effectiveness of the presented control method.
347 citations
TL;DR: It is proven that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded with full state constraints.
Abstract: The problem of adaptive fuzzy control is investigated for a class of nontriangular structural stochastic switched nonlinear systems with full state constraints in this paper. A remarkable feature of the nontriangular structural nonlinear system is the so-called algebraic loop problem in the existing backstepping-based analysis and design. Properties of fuzzy basis functions are utilized to circumvent this algebraic loop problem. Based on the Barrier Lyapunov function, an adaptive fuzzy stochastic switched control scheme is designed. It is proven that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded with full state constraints. The effectiveness of the proposed control scheme is verified via simulation studies.
297 citations
TL;DR: An observer-based adaptive fuzzy event-triggered control strategy is proposed for the full-state-constrained nonlinear system with actuator faults based on backstepping technique, which can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time.
Abstract: In this paper, an adaptive fuzzy output feedback control problem is investigated for a class of stochastic nonlinear systems in which the fuzzy logic systems are adopted to approximate the unknown nonlinear functions. A reduced-order observer and a general fault model are designed to observe the unavailable state variables and describe the actuator faults, respectively. An event-triggered control law is developed to reduce the communication burden from the controller to the actuator. Meanwhile, the barrier Lyapunov functions are constructed to guarantee that all the states of the stochastic nonlinear system are not to violate their constraints. Furthermore, an observer-based adaptive fuzzy event-triggered control strategy is proposed for the full-state-constrained nonlinear system with actuator faults based on backstepping technique, which can guarantee that all the signals in the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin in a finite time. Finally, simulation results are given to illustrate the effectiveness of the proposed control scheme.
213 citations
TL;DR: An adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object.
Abstract: Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.
203 citations
TL;DR: An adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems.
Abstract: In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method.
202 citations
TL;DR: This paper investigates fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact.
Abstract: In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment–robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov’s stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control.
199 citations
TL;DR: This paper reviews the key features of the three above types of fuzzy systems and points out the historical rationale for each type and its current research mainstreams, and focuses on fuzzy model-based approaches developed via Lyapunov stability theorem and linear matrix inequality (LMI) formulations.
Abstract: More than 40 years after fuzzy logic control appeared as an effective tool to deal with complex processes, the research on fuzzy control systems has constantly evolved. Mamdani fuzzy control was originally introduced as a modelfree control approach based on expert?s experience and knowledge. Due to the lack of a systematic framework to study Mamdani fuzzy systems, we have witnessed growing interest in fuzzy model-based approaches with Takagi-Sugeno fuzzy systems and singleton-type fuzzy systems (also called piecewise multiaffine systems) over the past decades. This paper reviews the key features of the three above types of fuzzy systems. Through these features, we point out the historical rationale for each type of fuzzy systems and its current research mainstreams. However, the focus is put on fuzzy model-based approaches developed via Lyapunov stability theorem and linear matrix inequality (LMI) formulations. Finally, our personal viewpoint on the perspectives and challenges of the future fuzzy control research is discussed.
186 citations
TL;DR: Based on Lyapunov stability theory, an adaptive event-triggered fuzzy control approach is proposed to guarantee the desired performance and simulation examples are presented to testify the feasibility of the approach.
Abstract: This paper studies the adaptive event-triggered fuzzy control issue for active vehicle suspension systems with uncertainties Takagi–Sugeno fuzzy model is applied for considered systems In the process of designing controller, a crucial problem, actuator failure, is taken into account An adaptive event-triggered mechanism is adopted to economize limited communication resource Compared with the traditional event-triggered scheme with a constant threshold, the adaptive event-triggered mechanism can save more resource effectively Based on Lyapunov stability theory, an adaptive event-triggered fuzzy control approach is proposed to guarantee the desired performance Meanwhile, suspension constrained requirements are also ensured Finally, simulation examples are presented to testify the feasibility of the approach proposed in this paper
185 citations
TL;DR: In the light of Lyapunov stability theory, a fuzzy dynamic output feedback controller is designed to guarantee the stochastic stability and $\mathcal {H}_{\infty }$ performance for considered systems.
Abstract: This paper studies the problem of adaptive event-triggered dynamic output feedback fuzzy control for nonlinear networked control systems. Two crucial factors, packet dropouts and actuator failure, are taken into consideration simultaneously. Takagi–Sugeno fuzzy model is introduced to describe considered systems. The Bernoulli random distribution process is employed to depict the phenomenon of data missing. The actuator failure model is adopted to depict actuator failure. An innovative adaptive event-triggered strategy is built to save computational resource. In the light of Lyapunov stability theory, a fuzzy dynamic output feedback controller is designed to guarantee the stochastic stability and $\mathcal {H}_{\infty }$ performance for considered systems. Finally, simulation results are provided to demonstrate the usefulness of the proposed control strategy.
179 citations
TL;DR: It will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area.
Abstract: Evolutionary fuzzy systems are one of the greatest advances within the area of computational intelligence. They consist of evolutionary algorithms applied to the design of fuzzy systems. Thanks to this hybridization, superb abilities are provided to fuzzy modeling in many different data science scenarios. This contribution is intended to comprise a position paper developing a comprehensive analysis of the evolutionary fuzzy systems research field. To this end, the "4 W" questions are posed and addressed with the aim of understanding the current context of this topic and its significance. Specifically, it will be pointed out why evolutionary fuzzy systems are important from an explainable point of view, when they began, what they are used for, and where the attention of researchers should be directed to in the near future in this area. They must play an important role for the emerging area of eXplainable Artificial Intelligence (XAI) learning from data.
TL;DR: A decentralized adaptive fuzzy control approach is proposed to compensate for the effects of actuator faults and the whole signals of the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded.
Abstract: For a class of large-scale nonlinear systems in nonstrict-feedback structure with immeasurable states, an adaptive decentralized fuzzy control strategy on the basis of event-triggered mechanism is investigated in this paper. Fuzzy logic systems are implemented to construct an observer, which approximates the unknown nonlinear function in the controller. In light of backstepping control technique and event-triggered mechanism, a decentralized adaptive fuzzy control approach is proposed to compensate for the effects of actuator faults. When the triggering condition is satisfied, the communication burden can be reduced. Moreover, the whole signals of the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded. Furthermore, the outputs of subsystems can track the desired reference signals. Finally, some simulation results are utilized to testify the effectiveness of the proposed control scheme.
TL;DR: An observer-based adaptive fuzzy control scheme is proposed for the considered systems to compensate for the effects of input quantization and actuator fault based on adaptive backstepping approach, which can guarantee that all the signals in the closed-loop system are bounded.
Abstract: This paper is focused on the observer-based adaptive fuzzy control problem for nonlinear stochastic systems with the nonstrict-feedback form, in which some complicated and challenging issues including unmeasurable states, input quantization and actuator faults are addressed. The fuzzy logic systems are introduced to approximate the nonlinear functions existing in the control system. A fuzzy observer is designed to observe the unavailable state variables. In order to handle the negative effects resulting from input quantization and actuator faults, a damping term with the estimation of unknown bounds as well as a positive time-varying integral function are constructed, respectively. Furthermore, an observer-based adaptive fuzzy control scheme is proposed for the considered systems to compensate for the effects of input quantization and actuator fault based on adaptive backstepping approach. The proposed control strategy can guarantee that all the signals in the closed-loop system are bounded. Finally, simulation results are provided to illustrate the effectiveness of the proposed adaptive control scheme.
TL;DR: A novel complete sampling-interval-dependent looped function is proposed, which depends not only on the interval from x(t) to x(tk) but also on the timing of the sampling period, ensuring the considered system strictly ( Q, S, R ) − γ -dissipative.
TL;DR: By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation.
Abstract: This paper reports our study on adaptive fuzzy tracking control for flexible-joint robots with full state constraints. In the control design, fuzzy systems are adopted to identify the totally unknown nonlinear functions and can properly avoid burdensome computations. The tan-type barrier Lyapunov functions are used to deal with state constraints so that even without state constraints, the controller is still valid. By combining the method of backstepping design with adaptive fuzzy control approaches, a novel simpler controller is successfully constructed to ensure that the output tracking errors converge to a sufficiently small neighborhood of the origin, while the constraints on the system states will not be violated during operation. Finally, comparison simulations are presented to demonstrate the effectiveness of the proposed control schemes.
TL;DR: The problem of sliding-mode fault-tolerant control is addressed for a class of uncertain nonlinear systems with distributed delays and parameter perturbations by using interval type-2 Takagi–Sugeno (T–S) fuzzy models, of which uncertain parameters and distributed state delays are represented in a unifiedtype-2 fuzzy framework.
TL;DR: A new Lyapunov–Krasovskii functional is designed to study the stability of continuous-time Takagi–Sugeno fuzzy systems with time-varying delay and a new stability criterion is derived by analyzing the sign of the time derivatives of membership functions.
Abstract: In this technical paper, a new Lyapunov–Krasovskii functional (LKF) is designed to study the stability of continuous-time Takagi–Sugeno fuzzy systems with time-varying delay. The integrand of the LKF depends on integral variable and time ${t}$ which can help to reduce the number of linear matrix inequalities (LMIs). Then, a new stability criterion is derived by analyzing the sign of the time derivatives of membership functions. Compared with the existing results, larger delay bounds can be obtained by applying the new criterion. In the end, two examples show the effectiveness of the conclusions.
TL;DR: An adaptive fuzzy fractional-order sliding-mode control strategy to control the mover position of a permanent magnet linear synchronous motor (PMLSM) system is developed in which an uncertainty observer is developed to observe uncertainties while an adaptive fuzzy reaching regulator is designed to concurrently compensate for observation deviations and suppress the chattering phenomenon.
Abstract: The aim of this study is to develop an adaptive fuzzy fractional-order sliding-mode control (AFFSMC) strategy to control the mover position of a permanent magnet linear synchronous motor (PMLSM) system. First, the mathematical model of the PMLSM is investigated by using the principle of field oriented control. Subsequently, a fractional-order sliding-mode control (FSMC) is designed by means of a new fractional-integral sliding surface. Because it is difficult to determine the hitting control gain for the FSMC in practice, the AFFSMC is further developed in which an uncertainty observer is developed to observe uncertainties while an adaptive fuzzy reaching regulator is designed to concurrently compensate for observation deviations and suppress the chattering phenomenon. The adaptive laws are derived to tune the control parameters online based on the Lyapunov stability theorem. Thus, the uncertainty bound information is not required while the chattering can be attenuated. Finally, experiments demonstrate that the proposed AFFSMC system performs the robust control performance and precise tracking response for the PMLSM drive system against the parameter variations and external disturbances.
TL;DR: A new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously.
Abstract: In this paper, we study the exponential stabilization problem for continuous-time Takagi–Sugeno fuzzy systems subject to aperiodic sampling. By aiming to transmission reduction, an appropriate aperiodic event-triggered communication scheme with adaptive mechanism is put forward, which covers the existing periodic mechanisms as special cases. For the sake of reduction in design conservativeness, both the available information of sampling behavior and threshold error are fully acquired by constructing a novel time-dependent Lyapunov functional. Then, a new exponential stability criterion is presented to establish the quantitative relationship among the adaptive adjusted event threshold, the decay rate, the upper bound, and the lower bound of variable sampling period, simultaneously. By resorting to a matrix transformation, the corresponding stabilization criterion is further derived by which the sampled-data controller can be obtained. Finally, two illustrative examples are provided to demonstrate the virtue and applicability of proposed design method.
TL;DR: A hybrid task scheduling algorithm named FMPSO that is based on Fuzzy system and Modified Particle Swarm Optimization technique to enhance load balancing and cloud throughput and achieves the goal of minimizing the execution time and resource usage is proposed.
TL;DR: It is shown that with the proposed event-triggered robust adaptive control scheme, all the signals in the closed-loop system are guaranteed to be semiglobally bounded, and the output of the system converges to a small neighborhood of the origin.
Abstract: This paper considers a robust adaptive fuzzy control problem for a class of uncertain nonlinear systems via an event-triggered control strategy. Fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. A novel robust adaptive control scheme together with a novel event-triggering mechanism (ETM) is proposed to reduce communication burden. It should be noted that both the control signal and the adaptive parameters are updated only at the triggering time instants in the proposed scheme, which further saves the system energy and resources. It is shown that with the proposed event-triggered robust adaptive control scheme, all the signals in the closed-loop system are guaranteed to be semiglobally bounded, and the output of the system converges to a small neighborhood of the origin. Moreover, with the proposed ETM, Zeno behavior can be strictly excluded. Finally, a one-link manipulator system is used to demonstrate the effectiveness of the proposed control scheme.
TL;DR: A proportional interval T2 hesitant fuzzy TOPSIS approach is developed to provide linguistic decisionmaking under uncertainty and represents a novel paradigm for linguistic group decision making under the umbrella of T2 fuzzy logic and systems for computing with words that has potential for application in real-life scenarios.
TL;DR: Using the universal approximation ability, the nonlinear functions and their bounding functions may be completely unknown to the designer, which is more in line with reality than the existing finite-time researches for stochastic nonlinear systems.
TL;DR: Based on a novel Lyapunov function which is both fuzzy-basis-dependent and mode-dependent, the existence criterion for the desired controller is established to ensure the stochastic stability as well as a predefined H ∞ performance index of the resulting closed-loop system.
TL;DR: The impact of CES units in automatic generation control (AGC) of interconnected power system is analysed and contrasted critically, and the proposed approach asserts better and vigorous results to supply reliable and high-quality electric power to the end user.
TL;DR: It can be shown that under the novel adaptive fuzzy controller, all the closed-loop signals are semiglobally uniformly ultimately bounded, and especially the tracking error satisfies the accuracy assigned a priori.
Abstract: This paper investigates the fuzzy adaptive practical tracking problem for a class of nonlinear pure-feedback systems with quantized input signal. In the control scheme design process, the considered system is transformed into a plant with a strict-feedback form by borrowing the mean value theorem of differential, then fuzzy logic systems are used to compensate for some uncertain nonlinearities in the considered plant and the classical adaptive technique is employed to handle some unknown parameters. In the backstepping design, some non-negative switching functions are introduced to develop the desired fuzzy controller, and Barbalat’s lemma is used to analyze the stability and the control performance of the closed-loop system. It can be shown that under the novel adaptive fuzzy controller, all the closed-loop signals are semiglobally uniformly ultimately bounded, and especially the tracking error satisfies the accuracy assigned a priori . A simulation example is presented to verify the effectiveness of the proposed control method.
TL;DR: The motive of this paper is to design a peak-to-peak filter such that the filtering error system is stochastically stable and the prescribed peak- to-peak performance index is guaranteed.
Abstract: In this paper, the peak-to-peak filtering problem is studied for a class of networked nonlinear systems. The nonlinear physical plant is represented by the Takagi–Sugeno fuzzy system. Assume that the data packet dropout phenomenon occurs when the measurement output signal and the performance output signal of the nonlinear systems are transmitted by the digital communication channel. Two stochastic variables satisfying the Bernoulli random binary distribution are used to model this phenomenon. The motive of this paper is to design a peak-to-peak filter such that the filtering error system is stochastically stable and the prescribed peak-to-peak performance index is guaranteed. The developed theoretical results for designing a peak-to-peak filter are expressed in the form of linear matrix inequalities. Finally, a simulation example is presented to illustrate the validity of theoretical analysis.
TL;DR: This paper investigates the sliding-mode control (SMC) problem of Takagi–Sugeno (T–S) fuzzy multiagent systems (MASs) with a cooperative fuzzy-based dynamical sliding- mode (SM) controller and a new model transformation method for T–S fuzzy MASs.
Abstract: This paper investigates the sliding-mode control (SMC) problem of Takagi–Sugeno (T–S) fuzzy multiagent systems (MASs). A cooperative fuzzy-based dynamical sliding-mode (SM) controller is designed and the overall closed-loop T–S fuzzy MAS is constructed. A new model transformation method for T–S fuzzy MASs is presented to transform the fuzzy weighting matrix into a set of fuzzy weighting scalars. By applying the method of linear matrix inequality, a general stability analysis approach for T–S fuzzy MASs is proposed. Moreover, the energy-cost constraint problem is studied by using the linear quadratic regulator method. Finally, numerical examples are provided to illustrate the effectiveness of the proposed theoretical approaches and the improved performance compared to existing results.
TL;DR: An observer-based SMC law is synthesized to guarantee finite-time reachability of the predefined sliding surface before the prescribed time and sufficient conditions in terms of linear matrix inequalities are established to guarantee the required boundedness performance of the overall closed-loop controlled system during the two phases.
Abstract: This paper is concerned with finite-time sliding mode control (SMC) of continuous-time semi-Markovian jump systems with immeasurable premise variables via fuzzy approach. First, an integral sliding surface is constructed based on fuzzy observer. Second, an observer-based SMC law is synthesized to guarantee finite-time reachability of the predefined sliding surface before the prescribed time. Third, through finite-time boundedness analysis, the required boundedness performance is conducted at the reaching phase first and then the sliding motion phase, respectively. Furthermore, sufficient conditions in terms of linear matrix inequalities (LMIs) are established to guarantee the required boundedness performance of the overall closed-loop controlled system during the two phases with generally uncertain transition rates (TRs) simultaneously. Finally, a practical example is given to show the validity of the established method numerically.
TL;DR: A state feedback controller is derived that guarantees the closed-loop fuzzy system being asymptotically stable with an $H_{\infty }$ performance index.
Abstract: This paper investigates the problem of robust $H_{\infty }$ control for a class of nonlinear systems with state and input time-varying delays. The nonlinearity is presented by a continuous-time Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties. A sufficient asymptotic stability condition is first proposed by constructing a delay-product-type augmented Lyapunov–Krasovskii functional and utilizing an extended reciprocally convex matrix inequality together with a Wirtinger-based integral inequality. Then, a state feedback controller is derived that guarantees the closed-loop fuzzy system being asymptotically stable with an $H_{\infty }$ performance index. Finally, four numerical examples are given to reveal the effectiveness and merits of the developed new design techniques.