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Speed tracking control via online continuous actor-critic learning

Due to complex and nonlinear dynamic model, speed tracking control with high precision and robustness is always a hard-deal task for autonomous vehicle control. In recent years, learning methods are gradually used to solve many complex control problems through the observed data from the real control systems. In this paper, a speed tracking controller is designed based on online continuous actor-critic learning. The input of the learning is the current vehicle state defined beforehand and the output is the actual brake or fuel command to track the desired speed. The online learning task is performed on a longitudinal model established based on the collected real vehicle data. An algorithm named continuous actor-critic learning Automaton(Cacla) is used for the policy learning. Simulation results illustrate that the designed controller is competent for tracking the expected speed with satisfied precision.