The dynamic decoupling problem of the hypersonic flight vehicle (HFV) is considered in this paper. The Linear Parameter-Varying (LPV) model of the HFV is firstly obtained and smoothly transformed into a polytopic form by the Tensor-Product (TP) model transformation method. After that, a dynamic decoupling control method is derived by minimizing the H∞ norm of a virtual system, which is composed by the controlled system and the no coupling reference model. The necessary and sufficient condition for the existence of the controller is derived based on Linear Matrix Inequalities (LMIs). Next, the decoupling controller for the polytopic LPV model of HFV is designed. And the simulation results show that the proposed method has perfect performance in terms of dynamic decoupling.
The path planning problem of mobile robot in unknown dynamic environment (UDE) is discussed in this article by building a continuous dynamic simulation environment. To achieve a collision-free path in UDE, the reinforcement learning theory with deep Q-network (DQN) is applied for the mobile robot to learn optimal decisions. A reward function is designed with weight to balance the obstacle avoidance and the approach to the goal. Moreover, it is found that the relative motion between moving obstacles and robots may cause abnormal rewards and further lead to a collision between robot and obstacle. To address this problem, two reward thresholds are set to modify the abnormal rewards, and the experiments shows that the robot can avoid all obstacles and reach the goal successfully. Finally, double DQN (DDQN) and dueling DQN are applied in this article. This article compares the results of reward-modified DQN (RMDQN), reward-modified DDQN (RMDDQN), dueling RMDQN, and dueling RMDDQN and concludes that the result of RMDDQN is the best.
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