Intelligent cruise control systems can detect the relative distance and speed between the preceding vehicle and the host vehicle by sensors, which can be used to calculate the required throttle angle or brake pressure, and regulate the speed or maintain the desired distance automatically. Intelligent cruise control systems are critical to improve driving safety, comfort and energy efficiency. The intelligent cruise control problem can be viewed as an optimal tracking control problem for a class of nonlinear system, which involves system dynamic uncertainty and environment disturbance. Although extensive researches and achievements have been obtained, but there is still much work to be done, such as the adaptability to drivers and the complex driving environment. On the other hand, due to the adaptive learning ability and the approximate optimal control performance, adaptive dynamic programming is widely investigated and applied in robotics and intelligent transportation systems. However, the low learning efficiency is the main limit of traditional adaptive dynamic programming approaches. For the above reasons, this thesis investigates the intelligent cruise control problem with adaptive dynamic programming methods. Supervised adaptive dynamic programming theory and algorithms are proposed in the thesis, which greatly improve the learning efficiency. The presented algorithms are used to design the intelligent cruise control system. The main contributions are as follows. 1. A hierarchical ACC system is designed in the dSPACE simulation system. The upper level controller is to learn the desired acceleration, which is implemented by the proposed supervised adaptive dynamic programming methods. The lower level controller provides required throttle angle or braking pressure through vehicle dynamics. Typical driving scenarios are designed in the dSPACE simulator to verify the effectiveness and superiority of the proposed algorithms in the thesis. 2. Based on adaptive dynamic programming, an iterative dual heuristic programming (DHP) algorithm is proposed to deal with the optimal control problem for a class of nonlinear discrete-time system, which contains parameter uncertainty and state time-delay. Detailed convergence proof is given to demonstrate that this algorithm can converge to the optimal cost function and the optimal control policy as well. Three feed-forward neural networks are presented to build Model network, Critic network and Action network, whic...
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