In this article, a study of the appointed fixed time control problem is presented for the quadrotor unmanned aerial vehicle attitude control system subject to external disturbances. Based on the appointed fixed time sliding mode variable and the adaptive technique, a novel sliding mode observer is first established to estimate the external disturbances within the appointed settling time. Subsequently, an appointed fixed time controller is proposed to track the desired attitude regardless of the initial states. To circumvent the singularity problem of the above controller, a novel improved adaptive sliding mode control law is designed by employing a nonlinear function and a modified sliding mode observer. Moreover, the chattering phenomenon is attenuated by replacing the sign function with hyperbolic tangent function. The appointed practical fixed time stability of the closed-loop attitude control system is proved and analyzed rigorously utilizing the Lyapunov theory. Finally, the effectiveness and fine performance of the proposed schemes are illustrated by numerical simulation results.

This paper addresses a long-standing yet well documented open problem on task-space trajectory tracking control of robotic manipulators subject to both uncertain dynamics and uncertain kinematics. The main contribution is to establish a theoretical framework for designing an observer-based controller to achieve exponential tracking control. Two observers are designed for precisely estimating the uncertain kinematics and dynamics. It is theoretically proved that the entire observer-controller system is proved to be globally exponentially stable. Both the estimation errors and the trajectory tracking error can globally exponentially converge to their stable equilibrium points, respectively. To the best knowledge of the author, this works may be the first result for robot exponential tracking control. The tracking performance is, therefore, more robust to system uncertainties. The settling time of the closed-loop tracking error system can be tuned to be small arbitrarily. Experimental tests are also conducted to validate the effectiveness of the designed control framework.

Multi-unmanned aerial vehicle (multi-UAV) cooperative trajectory planning is an extremely challenging issue in UAV research field due to its NP-hard characteristic, collision avoiding constraints, close formation requirement, consensus convergence and high-dimensional action space etc. Especially, the difficulty of multi-UAV trajectory planning will boost comparatively when there are complex obstacles and narrow passages in unknown environments. Accordingly, a novel multi-UAV adaptive cooperative formation trajectory planning approach is proposed in this paper based on an improved deep reinforcement learning algorithm in unknown obstacle environments, which innovatively introduces long short-term memory (LSTM) recurrent neural network (RNN) into the environment perception end of multiagent twin delayed deep deterministic policy gradient (MATD3) network, and develops an improved potential field-based dense reward function to strengthen the policy learning efficiency and accelerates the convergence respectively. Moreover, a hierarchical deep reinforcement learning training mechanism, including adaptive formation layer, trajectory planning layer and action execution layer is implemented to explore an optimal trajectory planning policy. Additionally, an adaptive formation maintaining and transformation strategy is presented for UAV swarm to comply with the environment with narrow passages. Simulation results show that the proposed approach is better in policy learning efficiency, optimality of trajectory planning policy and adaptability to narrow passages than that using multi-agent deep deterministic policy gradient (MADDPG) and MATD3.

This work solves the safe trajectory tracking control problem of robot manipulators with actuator faults, uncertain dynamics, and external disturbance. Another key issue met in practical robot engineering, i.e., joint velocity measurement uncertainty is also investigated. A robust control framework is presented. In this approach, a novel reconstruction law is preliminarily developed to estimate velocity measurement uncertainty, while the estimation error is finite-time stable. An adaptive control law is then designed by using the joint position, the estimated velocity, and the reconstructed knowledge of velocity measurement uncertainty. The key advantage of this methodology is that actual joint velocity and any prior knowledge of actuator faults are not required. The effectiveness of this proposed scheme is experimentally validated on a real robot arm.