Output Feedback Adaptive Fuzzy Control Research Articles

Output-Feedback Adaptive Fuzzy Control for Nonlinear Systems Under Time-Varying State Constraints

Output-Feedback Adaptive Fuzzy Control for Nonlinear Systems Under Time-Varying State Constraints

Filter-Based Event-Triggered Adaptive Fuzzy Control for Discrete-Time MIMO Nonlinear Systems With Unknown Control Gains

In this article, an event-triggered output feedback adaptive fuzzy control scheme is developed for a class of uncertain discrete-time multiinput–multioutput (MIMO) nonlinear systems with immeasurable states and unknown control gains. Due to the existence of unmeasured states, a set of fuzzy filters are designed, then a filter-based event-triggered adaptive fuzzy control scheme is developed for discrete-time MIMO nonlinear systems. To solve the problem of state estimation caused by the coupling of control input and unknown control gains, a fuzzy filters-based state observer is developed by combining filter states. And then, a parameterized state observer is constructed to effectively estimate immeasurable state signals by the combination of the fuzzy filter states and the gradient-based fuzzy parameter updating law. Based on filter states and estimation states, an event-based adaptive fuzzy control scheme is proposed by novel intermediate errors and backstepping. To overcome the causal problem caused by the low-triangular structure, the variable substitution and a predictor are, respectively, employed to forecast the future state and reference signals. A series of stability analyses illustrates that the proposed scheme achieves immeasurable state estimations, guarantees the ultimately uniformly boundedness of the closed-loop system, and obtains the good tracking performance, while reducing communication occupancy. Finally, simulation studies on a numerical and a practical example are conducted to demonstrate the effectiveness of the proposed scheme.

Output Feedback Adaptive Fuzzy Control for Uncertain Fractional-Order Nonlinear Switched System with Output Quantization

In this paper, a novel adaptive fuzzy controller is developed for the uncertain fractional-order switched nonlinear systems whose output is quantized by a class of sector-bounded quantizers. Since the states are not completely measurable, an observer with quantized output signal is designed to estimate the unknown system states. Meanwhile, based on the fractional Lyapunov stability criterion, the Lyapunov function with sum functions and the virtual control function with hyperbolic tangent functions are designed. Besides, in order to improve the approximation accuracy of the unknown nonlinear functions generated by fractional differential, the prediction errors and auxiliary variables of series-parallel estimation model are introduced in backstepping procedures. The simulation results show that the control scheme ensures that all the signals of the considered system remain semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin regardless of arbitrary switching.

Event-Based Dynamic Output Feedback Adaptive Fuzzy Control for Stochastic Nonlinear Systems

This paper focuses on the problem of decentralized event-based dynamic output feedback adaptive fuzzy control for a class of interconnected stochastic nonlinear systems. In order to relax the Lipschitz condition for the nonlinearity, a novel dynamic gain observer is constructed to estimate the unmeasured state variables. The funnel-like control technique is proposed to ensure that the output of each subsystem satisfies the prescribed performance requirement. To save energy in signal transmission, the controller and its triggered mechanism are codesigned based on backstepping method. By using the approximation theory of fuzzy logic systems, an unknown continuous function is approximated, and the difficulty caused by unmodeled dynamics is removed with the aid of changing supply function idea. By applying the Lyapunov stability theory, it is proved that all the signals of the resulting closed-loop system with the designed controller are bounded in probability. Finally, simulation results are given to verify the effectiveness of the theoretical results.

Adaptive fuzzy observer-based stabilization of a class of uncertain time-delayed chaotic systems with actuator nonlinearities

An observer-based output feedback adaptive fuzzy controller is proposed to stabilize a class of uncertain chaotic systems with unknown time-varying time delays, unknown actuator nonlinearities and unknown external disturbances. The actuator nonlinearity can be backlash-like hysteresis or dead-zone. Based on universal approximation property of fuzzy systems the unknown nonlinear functions are approximated by fuzzy systems, where the consequent parts of fuzzy rules are tuned with adaptive schemes. The proposed method does not need the availability of the states and an observer based output feedback approach is proposed to estimate the states. To have more robustness and at the same time to alleviate chattering an adaptive discontinuous structure is suggested. Semi-global asymptotic stability of the overall system is ensured by proposing a suitable Lyapunov–Krasovskii functional candidate. The approach is applied to stabilize the time-delayed Lorenz chaotic system with uncertain dynamics amid significant disturbances. Analysis of simulations reveals the effectiveness of the proposed method in terms of coping well with the modeling uncertainties, nonlinearities in actuators, unknown time-varying time-delays and unknown external disturbances while maintaining asymptotic convergence.

Output feedback adaptive fuzzy control of uncertain MIMO nonlinear systems with unknown input nonlinearities

An adaptive fuzzy output feedback controller is proposed for a class of uncertain MIMO nonlinear systems with unknown input nonlinearities. The input nonlinearities can be backlash-like hysteresis or dead-zone. Besides, the gains of unknown input nonlinearities are unknown nonlinear functions. Based on universal approximation theorem, the unknown nonlinear functions are approximated by fuzzy systems. The proposed method does not need the availability of the states and an observer based on strictly positive real (SPR) theory is designed to estimate the states. An adaptive robust structure is used to cope with fuzzy approximation error and external disturbances. The semi-global asymptotic stability of the closed-loop system is guaranteed via Lyapunov approach. The applicability of the proposed method is also shown via simulations.

Delayed Output Feedback Control for Nonlinear Systems With Two-Layer Interval Fuzzy Observers

In this paper, the design problem of a delayed output feedback control scheme using two-layer interval fuzzy observers for a class of nonlinear systems with state and output delays is investigated. The Takagi-Sugeno-type fuzzy linear model with an online update law is used to approximate the nonlinear system. Based on the fuzzy model, a two-layer interval fuzzy observer is used to reconstruct the system states according to equal interval output time delay slices. Subsequently, a delayed output feedback adaptive fuzzy controller is developed to overcome the nonlinearities, time delays, and external disturbances such that H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> tracking performance is achieved. The linguistic information is developed by setting the membership functions of the fuzzy logic system and the adaptation parameters to estimate the model uncertainties directly using linear analytical results instead of estimating nonlinear system functions. The tracking error dynamics are designed to enable our adaptive controller to avoid the filtering of the basis vectors whose dimension is much larger than that of the state vector of the controlled system. This is achieved by not imposing the strictly positive real condition. Based on the Lyapunov stability criterion and linear matrix inequalities, some sufficient conditions are derived so that all the states of the system are uniformly ultimately bounded. Therefore, the effect of the external disturbances on the tracking error can be attenuated to any prescribed level, and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> tracking control is achieved. Building on our previous work in this area, the proposed control scheme is extended to handle a class of uncertain nonlinear systems with state and output delays and external disturbances. This is achieved through the use of a robust variable structure scheme and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control techniques. Finally, a numerical example of a two-link robot manipulator is studied to illustrate the effectiveness of the proposed control scheme.

Output-feedback adaptive fuzzy control for a class of nonlinear systems with input delay and unknown control directions

In this paper, an adaptive fuzzy control scheme is proposed for a class of nonlinear systems with input delay, unknown time-varying delays and disturbances via dynamic output-feedback approach. During the controller design procedure, the input delay is dealt with by combining the smoothed dead-zone approach with the integral mean value theorem. Moreover, fuzzy logic systems (FLS) are employed to approximate the unknown continuous functions. The main advantages of this paper are that (1) the output-feedback adaptive fuzzy controller can dispose a class of nonlinear time-varying delay systems with input delay, in which the virtual control coefficients are all unknown and (2) only two parameters need to be adjusted online in controller design procedure, which reduces the online computation burden greatly. It is proven that the proposed control scheme renders the closed-loop system stable in the sense of semiglobal UUB. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Output-feedback adaptive fuzzy control for a class of non-linear time-varying delay systems with unknown control directions

In this study, an adaptive fuzzy control scheme is proposed for a class of non-linear systems with unknown discrete and distributed time-varying delays via dynamic output-feedback approach. Unlike the system with only one unknown control coefficient, the so-called high-frequency gain, the system we will consider is more general. During the controller design procedure, novel Lyapunov–Krasovskii functionals are introduced to compensate for the unknown time-varying delay terms and all unknown functions are lumped into a suitable unknown function which can be approximated by only one fuzzy logic system (FLS). The main advantages of this study are that (i) the output-feedback adaptive fuzzy controller can dispose a class of non-linear systems with unknown time-varying delays, in which the virtual control coefficients are all unknown, (ii) it does not need to know the time delays and their upper bounds and (iii) only one parameter needs to be adjusted online in controller design procedure, which reduces the online computation burden greatly. It is proven that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, whereas the tracking error converges to a small neighbourhood of the origin. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Fuzzy adaptive output tracking control of nonlinear systems

In this paper, a stable adaptive fuzzy output tracking control scheme is developed for a single-input–single-output unknown nonlinear system which can be represented globally by an input–output model. The main characteristics of the proposed adaptive fuzzy control are (i) it does not need the assumption that all the states of the system are available for full feedback, but introduces a high gain observer to estimate them. (ii) It is composed of a robust control term and an equivalence fuzzy control so that it not only ensures the stability of closed-loop system, but also attenuates the effect of fuzzy approximation error on the tracking error of the system to arbitrary small level. (iii) It is proved that the designed output feedback adaptive fuzzy control can recover the performance achieved under the state feedback controller.