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This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.

To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.

The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.

The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

This paper aims to design an event-triggered adaptive prescribed performance controller for flexible manipulators, with the primary objectives of achieving output performance constraints and addressing communication resource limitations.

The authors propose a novel prescribed performance barrier Lyapunov function (PP-BLF) that considers both output and tracking performance constraints. The PP-BLF ensures that the system's output, transient behavior and steady-state performance, adhere to prescribed constraints. The boundary of the PP-BLF is established by an exponential function that decays over time. Notably, the PP-BLF can be applied seamlessly in unconstrained cases without necessitating controller redesign. Moreover, the controller design incorporates an event-triggered mechanism, effectively reducing the frequency of controller updates and optimizing the utilization of communication resources. Additionally, the authors employ adaptive techniques to estimate the system's unknown parameters and approximate unknown nonlinear functions using radial basis function neural networks (RBFNN). To address the challenge of “complexity explosion”, dynamic surface technology is employed.

Numerical simulations are conducted under five different cases to verify the effectiveness of the proposed controller. The results demonstrate that the controller successfully constrains the output tracking error within the prescribed performance boundary. Moreover, compared with the traditional time-triggered mechanism, the event-triggered mechanism significantly reduces the controller's update frequency, resolving the problem of limited communication resources.

The paper reduces the update frequency of control signals and improves resource utilization through an event-triggered mechanism in the form of relative thresholds. The authors recognize that the event-triggered mechanism may impact the output performance of the system. To address this challenge, the authors propose a prescribed performance Barrier Lyapunov Function (PP-BLF). The PP-BLF is designed to effectively constrain the output performance of the system, ensuring satisfactory control even when the control signal updates are reduced.

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.

A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.

Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.

In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

The purpose of this paper is to propose a pre-defined performance robust control method for pre-assembly configuration establishment of in-space assembly missions, and collision avoidance is considered during the configuration establishment process.

First, six-degrees-of-freedom error kinematic and dynamic models of relative translational and rotational motion between transportation systems are developed. Second, the prescribed transient-state performance bounds of tracking errors are designed. In addition, based on the backstepping, combining the pre-defined performance control method with a robust control method, a pre-defined performance robust controller is designed.

By designing prescribed transient-state performance bounds of tracking errors to guarantee that there is no overshoot, collision-avoidance can be achieved. Combining the pre-defined performance control method with a robust control method, robustness to disturbance is guaranteed.

This paper proposed a pre-defined performance robust control method. Simulation results demonstrate that the proposed controller can achieve a pre-assembly configuration establishment with collision avoidance in the existence of external disturbances.

This study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.

A prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.

From the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.

As revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization.

By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example.

Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed.

The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.

This paper aims to investigate the attitude regulation for spacecraft in the presence of time-varying inertia uncertainty and exogenous disturbances.

The high gain approaches are typically used in existing researches for theoretical advantages, bringing better performance but sensitive to parameter selection, making the controller conservative. A reset-control policy is presented to achieve the spacecraft attitude control with easy control parameter tuning.

The reset-control policy guarantees satisfying control performance despite using performance tuning function and saturation function besides reducing the conservativeness of the controller, thus reducing the effort in tuning control parameters.

Notably, the adaptive function owns a reset mechanism, which is reset to a preset condition when the controlled variable crosses zero. The mathematical analysis also shows the system trajectory can converge to a set centered at the origin.

This paper aims to investigate the problem of attitude control for a horizontal takeoff and horizontal landing reusable launch vehicle.

In this paper, a predefined-time attitude tracking controller is presented for a horizontal takeoff and horizontal landing reusable launch vehicle (HTHLRLV). Firstly, the attitude tracking error dynamics model of the HTHLRLV is developed. Subsequently, a novel sliding mode surface is designed with predefined-time stability. Furthermore, by using the proposed sliding mode surface, a predefined-time controller is derived. To compensate the external disturbances or model uncertainties, a fixed-time disturbance observer is developed, and its convergence time can be defined as a prior control parameter. Finally, the stability of the proposed sliding mode surface and the controller can be proved by the Lyapunov theory.

In contrast to other fixed-time methods, this controller only requires three control parameters, and the convergence time can be predefined instead of being estimated. The simulation results also demonstrate the effectiveness of the proposed controller.

A novel predefined-time attitude tracking controller is developed based on the predefined-time sliding mode surface (SMS) and fixed-time disturbance observer (FxTDO). The convergence time of the system can be selected as a prior control parameter for SMS and FxTDO.

The purpose of this study is to propose an adaptive fault-tolerant control approach based on output feedback for a class of quadrotor unmanned aerial vehicles system. In the event of a controlled actuator failure, a stable flying of the aircraft can be achieved by selecting an appropriate sliding mode surface.

Aiming at the actuator failure of quadrotor aircraft during flight in the controllable range, a dynamic surface sliding mode passive fault-tolerant controller based on output feedback is designed based on the strong robustness of sliding mode method. Due to the unknown nonlinearity dynamics and parameter uncertainties in the system, a nonlinear observer is used to estimate them online.

The stability of the suggested algorithm is established using appropriate Lyapunov functions, and the performance of the proposed control approach is demonstrated using hardware-in-the-loop simulation.

An error performance function is introduced into the controller to ensure the convergence speed and accuracy of errors are within the predetermined range. By using the norm estimation method, there is only one parameter that needs to be updated in each step of the control process, which considerably minimizes the calculation burden. Finally, the validity of the proposed control scheme is verified on the hardware-in-the-loop simulation, and the results show that the proposed control method has achieved the desired results.

This paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is constructed.

A novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control parameters.

The control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea conditions.

The proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.