Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer

Adaptive fuzzy fault-tolerant output feedback control of uncertain nonlinear systems with actuator faults based on dynamic surface technique

In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.

In this paper, a robust adaptive controller based on fuzzy observer for a class of unknown nonlinear systems with bounded external disturbances is presented. Firstly, a fuzzy observer based on fuzzy-basis-functions is designed to estimate the system state variables, and the fuzzy observer can guarantee the uniform ultimate bounds of the observer errors by SPR theory. Secondly, the adaptive controller with a fuzzy observer which requires only the system output variable is showed to control a class of unknown nonlinear systems with external disturbances, and the adaptive controller can guarantee the uniform ultimate bounds of the tracking errors. Finally, this simulation results presented adaptive controller applied to a single link robot control system demonstrate and confirm the effectiveness of the proposed method.

In this paper, a hybrid fuzzy adaptive output feedback control design approach is proposed for a class of multiinput and multioutput strict-feedback nonlinear systems with unknown time-varying delays, unmeasured states, and input saturation. First, fuzzy logic systems are employed to approximate unknown nonlinear functions in the system. Next, a smooth function is used to approximate the input saturation and an adaptive fuzzy state observer is constructed to solve the problem of unmeasured states. Based on the designed adaptive fuzzy state observer, a serial-parallel estimation model is established. By applying adaptive fuzzy dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws is developed based on Lyapunov-Krasovskii functional. It is proved that all variables of the closed-loop system are bounded and the system outputs can follow the given bounded reference signals as close as possible. A simulation example is provided to further show the effectiveness of this novel control scheme.

This paper aims to develop the adaptive fuzzy observer-based projective synchronization techniques for a class of uncertain chaotic systems. An adaptive fuzzy observer is introduced to estimate the unknown system state variables. And a projective synchronization between the master and the slave systems is obtained via an adequate fuzzy adaptive controller. The dynamics of the original projective synchronization error are Strictly Positive Real (SPR). The Genesio chaotic system is employed in the simulation studies, and the results show the effectiveness of the proposed methods.

State observer-based adaptive fuzzy output-feedback control for a class of uncertain nonlinear systems

This paper develops a L1 adaptive output-feedback fault-tolerant controller for uncertain nonlinear systems in the presence of unmodeled actuator dynamics and actuator faults. The proposed controller consists of the output predictor, adaptive laws, and the control law. The output predictor is a dynamic system expressed as a linear system with adaptive parameters which mean the matched uncertainties and the unmatched uncertainties. Piecewise-constant adaptive laws are designed to update the adaptive parameters so that the estimation error is driven to zero at each integration time step. The control law with low-pass filters is introduced to compensate for the performance degradation because of system uncertainties, unmodeled actuator dynamics and faults, and to guarantee the uniform boundedness of the input and output signals of the system. Two simulation examples verify the effectiveness of the proposed controller.

Based on the backstepping technique, an adaptive neural network (NN) based output feedback controller is proposed to achieve a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict-feedback form. The neural networks are utilized to approximate unknown functions in the systems. The adaptive output feedback controller needs only to adjust less adaptive parameters, therefore it is clear that the proposed approach can reduce on-line computation burden. It is proven that all the signals in the closed-loop system are semi-global uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. A simulation example is used to verify the effectiveness of the proposed approach.

This article proposes an adaptive fuzzy output-feedback control approach for a class of nonaffine multi-input multi-output nonlinear systems with prescribed performance and immeasurable states. For most of the existing fuzzy approximation-based approaches, there are many parameters in the fuzzy systems that need to be estimated online with adaptive laws. To overcome this difficulty, we propose a new fuzzy system; the remarkable advantage of this fuzzy system is that only one parameter needs to be estimated online. The fuzzy system is used to approximate the unknown nonlinear function, and an adaptive fuzzy state observer is developed to estimate the unmeasured states. By utilizing the state observer and the prescribed performance control scheme, an adaptive fuzzy prescribed performance output-feedback control method is developed. The number of the adaptive laws both for the state observer and the controller is reduced to the number of subsystems. It is proved that all the signals in the closed-loop systems are semiglobally uniformly ultimately bounded, and that the prescribed performance error constraints can be achieved. Simulation examples are provided to highlight the effectiveness of the proposed approach.

In this paper, the design problem of 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 on-line 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 override the nonlinearities, time delays, and external disturbances such that the H∞ tracking performance is achieved. The linguistic information is developped by setting the membership functions of the fuzzy logic system and the adaptation parameters to estimate the model uncertainties directly for using linear analytical results instead of estimating nonlinear system functions. The filtered tracking error dynamics are designed to satisfy the Strictly Positive Realness (SPR) condition. Based on the Lyapunov stability criterion and linear matrix inequalities (LMIs), some sufficient conditions are derived so that all states of the system are uniformly ultimately bounded and the effect of the external disturbances on the tracking error can be attenuated to any prescribed level and consequently an H∞ tracking control is achieved. Finally, a numerical example of a two-link robot manipulator is given to illustrate the effectiveness of the proposed control scheme.

In this paper, we present a new scheme to design adaptive fuzzy output-feedback controller for uncertain nonlinear systems in the presence of input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a state filter is designed to estimate unmeasured states. Combining the backstepping recursive design with modular design techniques, a new adaptive fuzzy output control scheme is synthesized. Unlike some existing control schemes for systems with input saturation, the developed controller dose not require assumptions on the states available and nonlinear systems satisfying the matching condition. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. A simulation is included to illustrate the effectiveness of the proposed approach.

In this paper, an actuator fault tolerant control (FTC) has been designed using dynamic sliding mode control (DSMC) for single input nonlinear systems which are affected from uncertainty. In DSMC the switching of input control signal is removed due to the integrator which is placed before the input control signal of the plant hence, its result is reducing of chattering. However, in DSMC the augmented system (the system plus the integrator) is one dimension bigger than the actual system and then, the plant model should be completely known. To overcome on this problem, an adaptive fuzzy observer (AFO) has been proposed. Finally, we show the combination result of DSMC and AFO is the compensation of the fault by input control signal of the plant, while the performance and robustness of the closed loop system are reserved. The main property of the proposed approach is its simplicity and we only use system output for FTC and moreover, the fault diagnosis (detection, isolation and reconstruction) is done at the same time. The upper bound of the uncertainty is not used in DSMC and AFO, which is important in practical implementation. Simulation results show the advantages of the proposed approach.

In this paper, an adaptive fuzzy output feedback control approach is presented for a class of single-input single-output uncertain nonlinear systems with unknown backlash-like hysteresis and unmeasured states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a state filter observer is designed to estimate unmeasured states. Combining the backstepping recursive design with modular design techniques, a new adaptive fuzzy output control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. A simulation is included to illustrate the effectiveness of the proposed approach. The important feature of the proposed control approach is that it can solve the states immeasurable problem of nonlinear systems with unknown backlash-like hysteresis and the problem of unknown virtual control coefficients.

Aiming at the research content of tracking control for a class of nonlinear uncertain switched systems including full state constraints, a novel event-triggered adaptive fuzzy output feedback control scheme is given. The systems studied need to use the approximation principle of fuzzy logic systems (FLSs) to solve the nonlinear smooth function with unknown terms. For ensuring that all states of the systems are within the time-varying constraint limits, the stability of the switched systems is verified by utilizing tangent barrier Lyapunov function (Tan-BLF). Based on the potential barrier Lyapunov function (BLF) and backstepping recursive construction method, the adaptive law, controller and event-triggered mechanism of the subsystem are designed. The proposed method will make that the signal is bounded. Moreover, the tracking error can be adjusted to the neighborhood closed to the origin. Simulation examples indicate the feasibility of the method.

This paper proposes a stable adaptive fuzzy fault-tolerant attitude-tracking controller for a rigid spacecraft in the presence of unavailable velocities, external disturbance, actuator faults, and actuator saturation. Fuzzy logic systems are applied to approximate the unknown nonlinear function vector, and a fuzzy adaptive observer is designed to estimate the unmeasured velocity of the rigid body. By using the backstepping technique, a novel adaptive fuzzy attitude-tracking fault-tolerant control scheme is developed. It has been testified that this control approach guarantees that all signals of the rigid spacecraft are bounded and the tracking error between the system output and the reference signal converges to a small neighborhood of zero. Simulation examples with constant faults and time-variant faults are provided to show the fault-tolerant effectiveness of the control method.