Time-varying Actuator Failures Research Articles

Event-triggered consensus tracking control of flexible manipulators with nonlinear time-varying fault-tolerant actuators and input constraint

This paper introduces a novel event-triggered consensus control strategy for a multi-agent system composed of underactuated flexible manipulators. Most existing research on consensus tracking control assumes that the model targeted is fully actuated, the control signal is continuous, and that both time-varying actuator failures and input constraints are managed separately. These challenges were tackled in this study within the context of multi-agent systems, addressing input constraints and nonlinear time-varying actuator failures simultaneously. Moreover, an event-triggered mechanism is proposed to reduce signal communication. A double closed-loop consensus tracking method is designed based on the multi-agent topology, enabling all flexible manipulators to track the angle and angular velocity of the leader and an adaptive control law is designed to solve actuator faults. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.

Neural network adaptive PID-like coupling control for double pendulum cranes with time-varying input constraints

Industrial cranes commonly experience the double pendulum swing effect as a result of a number of reasons, including the magnitude of the cargo and the non-negligible hook mass. Nevertheless, the majority of control strategies for double-pendulum cranes, whether open-loop or closed-loop, are created using linearized dynamics and necessitate accurate knowledge of the model parameters, rendering them susceptible to parametric uncertainties and unmodeled disturbances. To solve these problems, a new neural network adaptive PID-like coupling control method for the nonlinear double pendulum cranes is proposed in this paper. By making full use of the designed composite signal and its integral action, oscillation suppression and precise positioning are realized. In addition, the problem of conventional PID parameter adjustment is solved successfully by determining the PID gains through a neural network adaptive algorithm, automatically. Moreover, it has been shown that the systematic error converges into a compact set around zero for a variety of time-varying actuator failures and saturation situations that can easily occur in practical engineering. Finally, the effectiveness of the proposed control method is verified through hardware experiments, and the comparison results prove the superior performance of the designed controller.

Fuzzy adaptive fixed-time fault-tolerant attitude tracking control for tailless flying wing aircrafts

This work investigates the fault-tolerant attitude tracking control problem for the tailless flying wing aircraft in the presence of an external disturbance, uncertain parameters, and time-varying actuator failures. First, the external disturbance, uncertainty, and time-varying actuator failures are skillfully transformed into a lumped uncertainty, followed by an approximator constructed by employing an interval type-2 fuzzy logic system (IT2-FLS). On this basis, a fixed-time IT2 fuzzy fault-tolerant control scheme is proposed, which can guarantee that all the signals of the closed-loop system have fixed-time convergence. The desired control torque is allocated to each control surface through a recurrent neural network (RNN) based control allocation algorithm by considering the magnitude and rate constraints of the actuators. Finally, the two representative simulations have been conducted to validate the effectiveness and superiority of the proposed control scheme and control allocation algorithm.

Adaptive Fuzzy Output Feedback Fault-Tolerant Compensation for Uncertain Nonlinear Systems With Infinite Number of Time-Varying Actuator Failures and Full-State Constraints.

This paper focuses on the observer-based fuzzy adaptive fault-tolerant tracking control problem for uncertain nonlinear systems subject to unmeasured states and unmatched external disturbances. By designing a high gain state observer and a disturbance observer, unmeasured states and unmatched external disturbances are estimated and robust tracking performance is improved. Moreover, the barrier-type functions are introduced to the backstepping design procedure to address the problem that all states do not violate their constraint bounds. Finally, a novel fault-tolerant control scheme for output feedback is proposed by combining with the projection technique. By designing appropriate Lyapunov functions, it is concluded that all signals of the plant are bounded and the desired tracking error can be regulated to a small neighborhood around the origin. The simulation results show the effectiveness of the designed control scheme.

Event-triggered reinforcement learning [formula omitted] control design for constrained-input nonlinear systems subject to actuator failures

In the paper, a novel input-constrained H∞ fault-tolerant control approach is developed by using sliding mode control technology and event-triggered reinforcement learning (RL) algorithm. To reduce or even eliminate the impacts of the time-varying actuator failures, a properly sliding mode control strategy is proposed for the controlled system, while the event-triggered H∞ control scheme is established via RL algorithm for the equivalent sliding mode dynamics. By utilizing a single neural network (NN), the Hamilton–Jacobi–Bellman (HJB) equation can be solved approximately, thereby gaining time-triggered worst-case disturbance law, as well as event-triggered optimal control policy. Besides, it is unnecessary to given a initial stabilizing control input in the learning process of neural networks (NNs) in this paper. Moreover, the Lyapunov stability principle is applied to guarantee that the controlled system is uniformly ultimately bounded (UUB). Finally, to verify the feasibility and efficient performance of the developed approach, three simulations are carried out.

Neural networks-based adaptive finite-time control of switched nonlinear systems under time-varying actuator failures

This paper is dedicated to neural networks-based adaptive finite-time control design of switched nonlinear systems in the time-varying domain. More specifically, by employing the approximation ability of neural networks system, an integrated adaptive controller is constructed. The main aim is to make sure the closed-loop system in arbitrary switching signals is semi-global practical finite-time stable (SGPFS). A backstepping design with a common Lyapunov function is proposed. Unlike some existing control schemes with actuator failures, the key is dealing with the time-varying fault-tolerant job for the switched system. It is also proved that all signals in the system are bounded and the tracking error can converge in a small field of the origin in finite time. A practical example is presented to illustrate the validity of the theory.

Adaptive Compensation for Infinite Number of Time-Varying Actuator Failures in Fuzzy Tracking Control of Uncertain Nonlinear Systems

The problem of direct adaptive compensation for infinite number of time-varying actuator failures/faults is of both theoretical and practical importance in the fuzzy tracking control of uncertain nonlinear systems. However, due to the technical difficulties, so far, very limited result is available in addressing such an issue, which thus motivates us to propose a new fuzzy control methodology in this paper. The proposed scheme is established from the techniques of projection adaptation design, a new piecewise Lyapunov function analysis, and an optimized fuzzy adaptation. It is shown that all the closed-loop signals are bounded and the steady-state tracking error converges to a residual around zero, irrespective of a possibility that there are infinite number of time-varying actuator failures. Simulations are provided to illustrate the effectiveness and applicability of the proposed scheme.

Robust adaptive actuator failure compensation for a class of uncertain nonlinear systems

This paper presents a robust adaptive state feedback control scheme for a class of parametric-strict-feedback nonlinear systems in the presence of time varying actuator failures. The designed adaptive controller compensates a general class of actuator failures without any need for explicit fault detection. The parameters, times, and patterns of the considered failures are completely unknown. The proposed controller is constructed based on a backstepping design method. The global boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. The proposed approach is employed for a two-axis positioning stage system as well as an aircraft wing system. The simulation results show the correctness and effectiveness of the proposed robust adaptive actuator failure compensation approach.

Adaptive control of uncertain nonlinear time delay systems in the presence of actuator failures and applications to chemical reactor systems

This paper presents an adaptive actuator failure compensation scheme for a class of uncertain nonlinear systems with unknown nonlinearities, unknown time varying delays and in the presence of time varying actuator failures. The considered actuator failure is modeled to cover both loss of effectiveness and stuck at some time varying values where the values, times and patterns of the failures are unknown. With the help of Neural Networks to approximate the unknown nonlinear functions, a novel adaptive actuator failure compensation controller is developed based on the backstepping design method. The appropriate Lyapunov–Krasovskii functionals are introduced to design new adaptive laws to compensate the unknown time varying actuator failures as well as uncertainties from unknown nonlinearities and time varying delays. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighborhood of the origin. Finally, the developed approach is applied to the control design of a chemical reactor with delayed recycle streams. The simulation results are provided to show the effectiveness of the proposed approach.

Adaptive control for a class of MIMO nonlinear time delay systems against time varying actuator failures

This paper investigates an adaptive controller for a class of Multi Input Multi Output (MIMO) nonlinear systems with unknown parameters, bounded time delays and in the presence of unknown time varying actuator failures. The type of considered actuator failure is one in which some inputs may be stuck at some time varying values where the values, times and patterns of the failures are unknown. The proposed approach is constructed based on a backstepping design method. The boundedness of all the closed-loop signals is guaranteed and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark and a chemical reactor system. The simulation results show the effectiveness of the proposed method.

Adaptive compensation for actuator failure in a class of non‐linear time‐delay systems

This study presents a state feedback adaptive controller for a class of parametric-strict-feedback non-linear systems with multiple bounded time-varying state delays and in the presence of time-varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some unknown time-varying values where the parameters, times and patterns of the failures are also unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. The adaptive state feedback control scheme is constructed based on a backstepping design method. The boundedness of all the closed-loop signals is guaranteed and the tracking error is proved to converge to a small neighbourhood of the origin. The proposed approach is employed for a second-order time delay plant as well as a two-stage chemical reactor with delayed recycle streams. The simulation results show the correctness and effectiveness of the proposed adaptive compensation in the case of actuator failures.

Adaptive actuator failure compensation with unknown control gain signs

This study deals with the problem of adaptive control for a class of non-linear systems with parameterisable time-varying actuator failures. An adaptive output-feedback actuator failure compensation scheme is proposed based on the backstepping technique and the Nussbaum gain approach. The assumption on the control gain signs is removed. The boundedness of all the closed-loop signals and the asymptotic output tracking are guaranteed in spite of the unknown actuator failures. Finally, an example is given to show the effectiveness of the proposed design method.