To deal with the nonlinear interference caused by chassis movement and road surface undulations with the tracking and aiming of unmanned combat ground vehicles, a tracking and aiming adaptive control method for unmanned combat ground vehicles on the move based on reinforcement learning compensation is proposed. This method consists of a main controller and a compensation controller. The main controller uses the PID control algorithm combined with the current tracking error to obtain the main control quantity, and the compensation controller uses the Dueling DQN reinforcement learning network to process the current state of the combat vehicle as well as the road surface undulation information near the local planning path to obtain the compensation control quantity. Firstly, the integrated kinematics model of the unmanned combat ground vehicle is established. Then, the compensation control algorithm based on reinforcement learning is described. Finally, simulation and verification are performed in three-dimensional scenes based on the V-REP dynamic software. The experimental results show that the tracking and aiming control method based on reinforcement learning compensation has good adaptive ability for chassis movement and road surface undulations, which effectively improves the tracking/aiming accuracy and stability of unmanned combat vehicles.

The hybrid electric system has good potential for unmanned tracked vehicles due to its excellent power and economy. Due to unmanned tracked vehicles have no traditional driving devices, and the driving cycle is uncertain, it brings new challenges to conventional energy management strategies. This paper proposes a novel energy management strategy for unmanned tracked vehicles based on local speed planning. The contributions are threefold. Firstly, a local speed planning algorithm is adopted for the input of driving cycle prediction to avoid the dependence of traditional vehicles on driver's operation. Secondly, a prediction model based on Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) is proposed, which is used to process both the planned and the historical velocity series to improve the prediction accuracy. Finally, based on the prediction results, the model predictive control algorithm is used to realize the real-time optimization of energy management. The validity of the method is verified by simulation using collected data from actual field experiments of our unmanned tracked vehicle. Compared with multi-step neural networks, the prediction model based on CNN-LSTM improves the prediction accuracy by 20%. Compared with the traditional regular energy management strategy, the energy management strategy based on model predictive control reduces fuel consumption by 7%.