Despite being a widely adopted development framework for unmanned aerial vehicle (UAV), deep reinforcement learning is often considered sample inefficient. Particularly, UAV struggles to fully explore the state and action space in environments with sparse rewards. While some exploration algorithms have been proposed to overcome the challenge of sparse rewards, they are not specifically tailored for UAV platform. Consequently, applying those algorithms to UAV path planning may lead to problems such as unstable training processes and neglect of action space comprehension, possibly causing negative impacts on the path planning results. To address the problem of sparse rewards in UAV path planning, we propose an information-theoretic exploration algorithm, Entropy Explorer (EE), specifically for UAV platform. The proposed EE generates intrinsic rewards based on state entropy and action entropy to compensate for the scarcity of extrinsic rewards. To further improve sampling efficiency, a framework integrating EE and Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithms is proposed. Finally, the TD3-EE algorithm is tested in AirSim and compared against benchmarking algorithms. The simulation outcomes manifest that TD3-EE effectively stimulates the UAV to comprehensively explore both state and action spaces, thereby attaining superior performance compared to the benchmark algorithms in the realm of path planning.

This paper investigates the H∞ control problem of the attitude stabilisation of a rigid spacecraft with external disturbances using prediction-based sampled-data control strategy. Aiming to achieve a ‘virtual’ closed-loop system, a type of parameterised sampled-data controller is designed by introducing a prediction mechanism. The resultant closed-loop system is equivalent to a hybrid system featured by a continuous-time and an impulsive differential system. By using a time-varying Lyapunov functional, a generalised bounded real lemma GBRL is first established for a kind of impulsive differential system. Based on this GBRL and Lyapunov functional approach, a sufficient condition is derived to guarantee the closed-loop system to be asymptotically stable and to achieve a prescribed H∞ performance. In addition, the controller parameter tuning is cast into a convex optimisation problem. Simulation and comparative results are provided to illustrate the effectiveness of the developed control scheme.

This paper is concerned with the stability analysis and $L_{1}$ -gain characterization for positive Takagi-Sugeno fuzzy systems subject to impulse effects. The main tool utilized is the construction of an impulse-time-dependent discretized copositive Lyapunov function, where the impulse interval partitioning idea is exploited to derive less conservative results. For the disturbance-free case, stability criteria are developed to guarantee global exponential stability of the concerned systems under ranged and constant dwell-time constraints. The analysis of the influence of the discretizing parameter on the reduction of conservativeness is carefully made. We further employ the proposed impulse-time-dependent discretized copositive Lyapunov function to analyze the $L_{1}$ -gain performance for the case where external disturbances are considered. By preforming a grid search for a scalar in an interval, all the proposed criteria can be checked via linear programming. Finally, numerical simulations are discussed to illustrate the effectiveness of the theoretical results.