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

Abstract

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.

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“…This method involves building a search tree to iteratively find a set of branches along the tree that provide an obstacle-free path to the goal. Shojaei also uses a set of three, 2WD2WS robots in simulation alongside a neural network-based controller which assists in locally maintaining the formation and path of the robots during navigation [9]. Neural network-based methods for path planning have become quite popular in recent years due to increased computing power available, making these methods feasible.…”

Research on the rapid design technology of automotive inspection structure based on MBD model

“…At the same time, the BP algorithm can not only be used in the multi-layer pre-feedback neural network, but also can be used with other neural networks. 15 It has the characteristics of wide versatility. The neural network transfer structure is shown in Figure 12.…”

Dynamics control of topology switching for multi-functional unmanned ground vehicle platoon with VCBDL topology

“…6 Shojaei proposed a new control method for multiple Ackermann steering vehicle formation by combining an adaptive neural network robust controller. 7 Besselink and Johansson introduced a novel delay-based spacing policy for the control of vehicle platoons together with a notion of disturbance string stability. The delay-based spacing policy specifies the desired inter-vehicular distance between vehicles and guarantees that all vehicles track the same spatially varying reference velocity profile.…”