Observer-Based Adaptive Fuzzy Fault-Tolerant Control for Stochastic Nonstrict-Feedback Nonlinear Systems With Input Quantization

In this article, an observer-based fuzzy adaptive inverse optimal output feedback control problem is studied for a class of nonlinear systems in strict-feedback form. The considered nonlinear systems contain unknown nonlinear dynamics and their states are not measured directly. Fuzzy logic systems are applied to identify the unknown nonlinear dynamics and an auxiliary nonlinear system is constructed. Based on this auxiliary system, a fuzzy state observer is first designed to estimate the immeasurable states. By using the inverse optimal principle and adaptive backstepping design theory, an observer-based fuzzy adaptive inverse optimal output feedback control scheme is then developed. The proposed inverse optimal control scheme need not assume that the states are measurable. It also guarantees that the closed-loop system is semiglobally uniformly ultimately bounded, and achieves the optimal control objective as well. Finally, two simulation examples are provided to check the validity of the presented control method.

This paper focuses on controller and observer design for the longitudinal model of an air-breathing hypersonic vehicle (AHV) subject to actuator faults and limited measurements of the states. The feedback linearization method is firstly employed for a modified AHV model with actuator faults, and dynamic effect caused by the actuator faults on the linearized model is analyzed. Based on full state information, an adaptive controller is designed using the Lyapunov method, which guarantees reference command tracking of the AHV under actuator faults. Next, to estimate the unmeasurable states used in the adaptive controller, a sliding observer is designed based on the sliding control method and the Filippov’s construction of the equivalent dynamics (FCED). Finally, the adaptive controller is combined with the sliding observer to generate the observer-based adaptive controller, which relies only on partial state information. Simulations demonstrate that the observer-based adaptive controller achieves desired tracking performance and good robustness in the presence of actuator faults.

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

In this paper, an observer-based robust adaptive fuzzy control strategy is presented for uncertain nonlinear dynamical systems subject to non-symmetric dead-zone nonlinearity. Within this scheme, fuzzy logic systems are used to estimate unknown nonlinear function of the dynamic system and unknown upper bounds of unmatched uncertainties, respectively. A state observer based on state variable filters is designed to estimate all the states which are not available for measurement in the system. Moreover, the adaptive scheme requires only the information about the bounds of the dead-zone slopes. By the Lyapunov function stability theorem, the asymptotical stability of the resulting closed-loop system can be guaranteed, and the output tracking performance of the whole system can be achieved. Finally, some simulation results are introduced to verify the effectiveness of the proposed controller.

A dual-loop fault-tolerant control scheme is investigated for UAV attitude control systems subject to actuator faults, input saturation, and external disturbances in this paper. In the outer loop of attitude angles, a nonlinear dynamic inversion controller is developed as baseline controller for fast response and is augmented by a neural network disturbance observer to enhance the adaptability and robustness. Considering input saturation, actuator faults, and external disturbances in the inner loop of attitude angle velocities, the unbalanced input saturation is first converted into a time-varying system with unknown parameters and disturbances using a nonlinear function approximation method. An L1 adaptive fault-tolerant controller is then introduced to compensate for the effects of lumped uncertainties including system uncertainties, actuator faults, external disturbances, and approximation errors, and the stability and performance boundaries are verified by Lyapunov theorem and L1 reference system. Some simulation examples are carried out to demonstrate its effectiveness.

In this paper, a novel observer-based adaptive backstepping control scheme is proposed for time-varying formation tracking of multiple unmanned aerial vehicle (multi-UAV) system consisting of multiple nonlinear UAVs with unknown parameters and immeasurable states. The communication network among the UAVs is represented by a directed graph and a part of the UAVs cannot obtain the reference information directly. A state observer is designed to estimate the immeasurable states. An adaptive control scheme is developed by employing backstepping control strategy and the designed state observer. It is proved that the proposed scheme can guarantee that the UAVs maintain the formation shape while tracking the reference trajectory. Besides, all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Simulation example is provided to show the effectiveness of the proposed scheme.

This paper investigates the adaptive fuzzy control problem of strict-feedback nonlinear systems with unknown dead-zone. Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear systems. The control input signal is nonlinear and experienced both dead-zone nonlinearity and actuator faults. By combining the effects of both dead-zone and actuator faults, the actual control input signal is expressed as a nonlinear function with bounded disturbance. The stability of the closed-loop nonlinear system is obtained via Lyapunov function and under the effects of unknown dead-zone, actuator fault, and unknown external disturbances. Finally, a numerical simulation example is given to verify the effectiveness of the proposed adaptive fuzzy control strategy.

In this paper, the T-S fuzzy observer-based adaptive tracking control of the biological system with stage structure is studied. First, a biological model with stage structure is established, and its stability at the equilibrium points is analyzed. Considering the impact of reducing human activities on the biological population, an adaptive controller is applied to the system. Since it is difficult to measure density directly, a fuzzy state observer is designed, which is used to estimate the density of biological population. At the same time, the density of predators can track the desired density through the adjustment of adaptive controller. The stability of the biological system is guaranteed, and the observer error and tracking error are shown to converge to zero. Finally, the effectiveness of the proposed adaptive control method is verified by numerical simulation.

This paper addresses the finite-time tracking control problem for a class of quadrotor unmanned aerial vehicle (QUAV) subject to unknown mixed faults and external disturbances. The considered mixed faults include both input quantization and actuator faults. First, radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear dynamics of the QUAV system, with adaptive control laws designed for online weights updates. Second, since the neural network approximation errors and external disturbances can be treated as unknown but bounded constants, adaptive control laws are developed to estimate these parameters. Third, to address the design complexity caused by unknown control coefficients arising from mixed faults, a Nussbaum gain function is introduced. Subsequently, based on the designed global fast terminal sliding mode (GFTSM) functions, adaptive GFTSM neural network control strategies are proposed for position and attitude tracking control. Theoretical analysis confirms that these control strategies guarantee the QUAV system’s position and attitude outputs converge to reference trajectories, with tracking errors reaching a very small neighborhood of zero within a finite time. Finally, the effectiveness of proposed control strategies is validated through an actual system.

Traffic engineering control is a major challenge in marine transportation. Cost efficiency and high performance demand advanced technologies for the ship control systems. This paper develops an autopilot heading control scheme based on a fuzzy state observer for an intelligent ship on this subject to track the prescribed function while calling for performance limitation and order execution time. A fuzzy logic system (FLS) is adopted to approximate the unknown uncertainties caused by the changes in water depth, wind, wave, ship loading, and speed in navigation. State observer is required to obtain unknown yaw rate. By adopting performance function and tracking error transformation techniques, the heading tracking error can converge to prescribed performance bounds. Taking settling time into account, the finite-time adaptive prescribed performance control algorithm can save more resources effectively. Based on the Lyapunov stability theory, the observer-based adaptive fuzzy control approach does not cause any unbounded signal, the system remains stable. Meanwhile, the autopilot heading control system with an unknown yaw rate and constraint state can benefit from the given design.

In this paper, an adaptive control strategy is developed for discrete‐time switched nonlinear systems with actuator faults and dead‐zone input under arbitrary switching conditions. The actuator faults considered include loss‐of‐effectiveness and bias faults, which are unknown but bounded. The complex structure of these systems, combined with actuator faults and dead‐zone inputs, presents significant challenges for control and this problem is addressed by approximating the unknown functions of each subsystem using radial basis function neural networks. Under arbitrary switching signals, the suggested controller and adaptive laws ensure that all signals remain bounded and the system output tracks the reference signal with a small bounded tracking error. The efficiency of the control technique is proven by two numerical examples.

In this article, a practical fixed-time adaptive neural network (NN) trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to uncertain dynamics, unknown time-varying disturbances, an unknown dead zone, actuator faults and input quantization is developed for the first time. Here, a hysteresis quantizer is introduced to decrease the oscillation in the signal quantization process. Then, the radial basis function NN is employed to compensate the uncertainty term in the AUVs trajectory tracking control system. By incorporating the bounded estimate, smoothing functions and parameter adaptive technique, the problem of unknown dead zone, actuator fault and input quantization are addressed. The restrictive conditions of boundedness for the disturbance-like item in conventional sector bounded quantizer is resolved. Subsequently, a practical fixed-time adaptive NN trajectory tracking control law is designed does not require any parameter information of the quantizer under the backstepping design framework. The theoretical analysis further confirms that all signals in the AUV trajectory tracking closed-loop control system remain bounded, and the developed control scheme is shown to be effective through simulation results.

A dual adaptive fault-tolerant control for a quadrotor helicopter against actuator faults and model uncertainties without overestimation

In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two experiments. The first experiment is adaptive interior noise control using the intelligent plate, in which the adaptive feedforward control scheme is employed. Obvious noise reduction is obtained for constant frequency, swept frequency and varying amplitude harmonic disturbances. The second experiment is adaptive control of sound-induced vibration of the plate, where two embedded PZT patches are used as an actuator and a sensor, respectively, and the hybrid adaptive controller is applied. The full vibration reduction for various harmonic excitations is obtained, verifying the advantage of the hybrid adaptive control. It is demonstrated that active control of structural acoustics can be efficiently achieved by employing intelligent structures, advanced adaptive control schemes and the low-cost DSP board.

This article studies the bipartite containment control problem for nonlinear multiagent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol such that the followers converge to a convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition approach of input quantization, an event-triggered control scheme is developed via backstepping technique. A fuzzy observer is constructed to estimate unmeasurable states. Moreover, the bipartite containment control scheme for nonlinear MASs is designed. It is demonstrated that all signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior is excluded. Finally, a simulation example is given to verify the validity of the designed method.