Adaptive neural network output-feedback control of multiple Ackermann steering vehicles formation including motor dynamics with a guaranteed performance

A prescribed performance output-feedback formation control of electrically driven Ackermann steering robotic vehicles is addressed in this paper. Some constraints are imposed to relative distance and angle errors between the cars and a leader. Then, constrained errors are transformed into a new second-order Euler–Lagrange formulation of unconstrained errors via the prescribed performance function technique which inherits all structural properties of the robot dynamics. Based on dynamic surface control design, an observer-based proportional–integral–derivative virtual controller with a prescribed performance is proposed at the first step. Then, an actual controller is proposed at the actuator level to generate input voltage control signals. The proposed controller takes the following advantages: (1) the possible overshoots and controller singularities are avoided based on some predefined performances of relative distance and angle errors, (2) the controller does not require linear and angular velocity measurements, (3) the unknown nonlinearities and exogenous disturbances are effectively compensated by combining a neural network and adaptive robust controller, and (4) the actuator dynamics is compensated in the inner-loop while voltage control signals are directly generated for each robot motors. Lyapunov’s stability analysis proves the stability of the closed-loop control system. Finally, numerical simulation results will show the controller performance.