Multivariable Non-Linear UGV Controller Design Using Deep Reinforcement Learning
This paper presents a case study and the underlying methodology of controller design for nonlinear multiple input multiple output systems. Deep neural networks are used to represent the controller policy and a reinforcement learning technique, namely Double Deep Q-Learning, is used to train it. The controller is designed from a simplified virtual model of the system and directly ported to the real system. This frees the designer from extracting an accurate model of the system (classic control theory approach), translating expert knowledge (fuzzy controllers), or acquiring large sets of data (supervised training of neural networks). Without loss of generality, the case study corresponds to the design of a 2-input 2-output line follower controller for unmanned ground vehicles (UGVs). Results show that the virtually-designed controller can drive real UGVs through the target trajectories with deviations within acceptable margins.
This paper deals with a control strategy for underactuated underwater vehicle moving in 6-degrees of freedom whose target is the tracking of a space trajectory. The control algorithms proposed build upon Lyapunov's method and backstepping technique, which force the vehicle follow any smooth time-varying bounded trajectory, despite of the uncertainty in the damping coefficient and absence of the force of sway and heavy. The error of tracking and parameter estimation are ultimately confined to a ball that can be made arbitrarily small.