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Trajectory interception is a critical synchronization element in the transportation and manufacturing sectors using robotic platforms. This is usually performed by matching the position and velocity of a target object with the position and velocity of the robot interceptor. However, the synchronization task is exasperated by (i) the proper gain tuning of the controller, (ii) the dynamic response of the robotic platform, (iii) the velocity constraints in the actuators, and (iv) the trajectory profile exhibited by the moving object. This means that the interception time is not controlled, which is critical for energy optimization, resources, and production. This paper proposes a prescribed time trajectory interception algorithm for robot manipulators. The approach uses the finite-time convergence properties of sliding mode control combined with a terminal attractor based on a time base generator. The combined approach guarantees trajectory interception in a prescribed time with robust properties. Simulation studies are conducted using the first three degrees of freedom (DOFs) of a RV-M1 robot under single- and multi-object interception tasks. The results verify the effectiveness of the proposed methodology under different hyperparameter configurations. [ABSTRACT FROM AUTHOR]

Recent advances in radar seeker technologies have considerably improved missile precision and efficacy during target interception. This is especially concerning in the arenas of protection and safety, where appropriate countermeasures against enemy missiles are required to ensure the protection of naval facilities. In this study, we present a reinforcement-learning-based strategy for deploying decoys to enhance the survival probability of a target ship against a missile threat. Our approach involves the coordinated operation of three decoys, trained using the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithms. The decoys operate in a leader–follower dynamic with a circular formation to ensure effective coordination. We evaluate the strategy across various parameters, including decoy deployment regions, missile launch directions, maximum decoy speeds, and missile speeds. The results indicate that, decoys trained with the MATD3 algorithm demonstrate superior performance compared to those trained with the MADDPG algorithm. Insights suggest that our decoy deployment strategy, particularly when utilizing MATD3-trained decoys, significantly enhances defensive measures against missile threats. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is an effective method to design a robust controller for unknown nonlinear systems. Uncertainty in the worst case requires a large state-action space. Hence, it is natural to use continuous-time RL methods rather than the discretisation of the spaces. In this paper, we propose a novel continuous-time RL using neural network approximation. Our method uses worst-case uncertainty to train the continuous-time RL algorithm. The backward Euler approximation is used to approximate the time derivative of the value function. Compared with the actor–critic (AC) algorithm, our method finds the robust control policy in the presence of worst-case uncertainty by taking into account the applied actions. It is shown that the AC algorithm finds the robust controller in less episodes, but its robustness is less than the results presented by our approach. The convergence of the proposed algorithm is analysed using the contraction property and differential equation techniques. The experiments show that our approach is more robust than the model-based LQR method and the well-known AC method. [ABSTRACT FROM AUTHOR]