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This is a study of adaptive constraint attitude control for a flexible spacecraft in the presence of inertia uncertainties, unknown disturbance, actuator saturation and faults. The proposed controller is designed by incorporating a Prescribed Performance Control (PPC) and fixed-time sliding mode control. First, a novel Nonsingular Fast Fixed-time Sliding Surface (NFFTSS) is introduced. Not only is the settling time independent of initial conditions, but also it is shorter than existing fixed-time attitude controls. Second, different from the conventional complex PPCs in the literature, a simple structure attitude controller is proposed to satisfy the transient and steady-state performance is proposed through a novel log-type PPC. An inherently continuous adaptive switching control is then presented in order to avoid the a priori not to require accurate information of the fault occurrence. Numerical simulations demonstrate that the proposed controller successfully accomplishes attitude control with high attitude pointing accuracy and stability.

This paper investigates an adaptive fuzzy controller prescribed performance for uncertain robot systems with actuator saturation. To mitigate the impact of model uncertainty and unknown disturbance, an adaptive fuzzy neural network (AFNN) is designed to approximate model uncertainty, and an adaptive disturbance observer (ADO) based on the AFNN is constructed to approximate the disturbance. For multi-degree-of-freedom robotic systems, an auxiliary system is constructed to alleviate the problem of actuator saturation. Combined with the barrier Lyapunov function, an adaptive prescribed performance controller is designed to realize finite-time tracking control for robotic systems with model uncertainty, external disturbance, and actuator saturation. The superiority and practicability of the designed control method are verified by simulations and experiments.

This paper investigates the adaptive coordinated control of multiple high-speed trains with input saturation and uncertain parameters. The motion of an ordered set of high-speed trains is modeled by a multi-agent system, in which each train adjusts its speed dynamically by communicating with its neighboring trains. For the uncertainties in systems and the possible actuator saturation, based on the LaSalles invariance principle, a new adaptive coordinated control algorithm for each train is designed to track the desired speed, and meanwhile under the control algorithm, the headway distances of each train with its neighboring trains are stabilized at stationary distances in a safety range, which ensures safe and efficient operation of high-speed trains. Numerical examples are given to illustrate the effectiveness of the proposed methods.

The formation control problem for a group of first-order agents with model uncertainty and actuator saturation is investigated in this manuscript. An algorithm-and-observer-based formation controller is developed to ensure the semi-global boundedness of the formation tracking error with actuator saturation. First, a fully local-error-related cooperative weight tuning procedure is proposed for the adaptive uncertainty estimation of each agent. The effect of actuator saturation on both the cooperative adaptive estimation and the controller design part is then analysed and discussed. A three-layer neural-based observer is further constructed to achieve finite-time uncertainty approximation with actuator saturation. Besides, the reverse effect led by coupled and saturated control inputs is defined and a new control input distribution algorithm is presented to attenuate the potential oscillation in system states. Finally, comparative simulations based on a multiple omnidirectional robot system are conducted to illustrate the performance of the proposed formation controllers and the new algorithm.

On-demand urban air transportation gains popularity in recent years with the introduction of the electric VTOL (eVTOL) aircraft concept. There is an emerging interest in short/medium range eVTOL air taxi considering the critical advantages of electric propulsion (i.e. low noise and carbon emission). Using several electric propulsion systems (distributed electric propulsion (DEP)) has further advantages such as improved redundancy. However, flight controller design becomes more challenging due to highly over-actuated and coupled dynamics. This study defines and resolves flight control problems of a novel DEP eVTOL air taxi. The aircraft has a fixed-wing surface to have aerodynamically efficient cruise flight, and uses only tilting electric propulsion units to achieve full envelope flight control via pure thrust vector control. The aircraft does not have conventional control surfaces such as aileron, rudder or elevator. Using pure thrust vector control has some design benefits, but the control problem becomes more challenging due to the over-actuated and highly coupled dynamics (especially in transition flight). A preliminary flight dynamics model is obtained considering the dominant effects at hover and high-speed forward flight. Hover and forward flight models are blended to simulate the transition dynamics. Two central challenges regarding the flight control are significant nonlinearities in aircraft dynamics during the transition and proper allocation of the thrust vector control specifically in limited control authority (actuator saturation). The former challenge is resolved via designing a sensor-based incremental nonlinear dynamic inversion (INDI) controller to have a single/unified controller covering the wide flight envelope. For the latter one, an optimisation-based control allocation (CA) approach is integrated into the INDI controller. CA requires special attention due to the pure thrust vector control’s highly coupled dynamics. The controller shows satisfactory performance and disturbance rejection characteristics. Moreover, the CA plays a vital role in guaranteeing stable flight in case of severe actuator saturation.

As a typical nonlinear link, actuator saturation exists in most network security protection systems. It easily disrupts the closed-loop performance of the system, leading to instability. The event triggering control policy generally adopts an aperiodic sampling mechanism and transmits information only when the triggering condition is met, which can save system communication and computing resources. An event-triggered controller is designed for a network security system with actuator saturation, and the system stabilization is realized. The attractor domain of the system is estimated by solving optimization problems in the form of linear matrix inequalities. Simulation results show the validity of the conclusions obtained in this paper.

The problem of designing a controller for a multi-vectored propeller airship with independent amplitude and rate saturations is addressed. First, a linear Proportional-Integral-Derivative (PID) controller is introduced for position control without considering the input saturations. Then, two design methods are applied to the traditional PID control output to satisfy the independent amplitude and rate constraints: the nested saturated PID controller (N-PID) and the transformed PID controller (T-PID). The bounded magnitudes and rate outputs of the modified controllers are given. Simulation results showed both controllers have good tracking performance while satisfying independent amplitude and rate saturations. However, the transformed PID controller has the advantage of expressing explicitly the relationship of the actuator magnitude and rate saturations with the parameters of the transformed function such that the actuator saturations are suppressed by calculation but not by trial and error.

We study the global stabilization problem for first- and second-order plants driven by saturated dynamic actuators under constant disturbances. We consider the dynamic actuator modeled by first-order dynamics with two types of saturations: input saturation and output saturation. Then, to compensate the constant disturbances, we construct proportional-integral (PI) controllers and show that the global stabilization of the overall system is achieved. Applying the Lasalle’s invariance principle, sufficient conditions are investigated for the global asymptotic stability of the plants. Finally, numerical examples are presented to demonstrate the theoretical results.

This paper addresses the problem of bipartite consensus for discrete-time multi-agent systems with Markovian-jumping parameters under the influence of time-varying communication delay and actuator saturation. The main aim of this work is to propose an observer-based control protocol such that the bipartite consensus of the uncertain Markovian-jumping multi-agent system under consideration can be achieved. An undirected structurally balanced signed graph is utilized to describe the cooperative and antagonistic interaction among neighboring agents. Combining algebraic graph theory together with Lyapunov stability theory, a new set of sufficient conditions is derived by using Jensen’s inequality and Abel-lemma based finite sum inequality to achieve bipartite consensus. At last, a numerical example is provided with simulations to validate the effectiveness of the developed theoretical results.

This paper presents a novel adaptive reinforcement learning control method with interval type‐3 fuzzy neural networks to improve the trajectory tracking control performance of quadrotor unmanned aerial vehicles in challenging flight conditions. The proposed reinforcement learning controller is independent of the system's dynamics, and only relies on measurable signals of the system. An adaptive robust controller in collaboration with the suggested reinforcement learning method is designed to significantly improve the robustness of the control system. The maximum overshoot/undershoot, convergence rate and final tracking accuracy are ensured a priori by the prescribed performance control methodology. To develop the proposed controller and to achieve a high‐performance closed‐loop system, a high‐gain observer is employed in order to estimate the velocity and acceleration of the quadrotor unmanned aerial vehicles system. The uniform ultimate boundedness stability of the proposed control algorithm is achieved by a Lyapunov‐based stability analysis. Finally, in the simulation section, it is shown that the presented intelligent controller with the proposed learning algorithm result in a better performance in contrast to the other kind of conventional control techniques. 1. A novel adaptive reinforcement learning controller with interval type‐3 fuzzy neural networks is proposed for quadrotor unmanned aerial vehicles. 2. The transient and steady‐state characteristics are guaranteed a priori by prescribed performance control. 3. A high‐gain observer is employed to estimate the velocity and acceleration of quadrotor unmanned aerial vehicles.