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.

中文翻译：

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多阿克曼转向车辆编队的自适应神经网络输出反馈控制，包括具有保证性能的电机动力学

本文讨论了电动阿克曼转向机器人车辆的规定性能输出反馈形成控制。对汽车和领头者之间的相对距离和角度误差施加了一些限制。然后，通过继承机器人动力学所有结构特性的规定性能函数技术，将约束误差转化为新的无约束误差的二阶欧拉-拉格朗日公式。基于动态表面控制设计，第一步提出了具有规定性能的基于观测器的比例-积分-微分虚拟控制器。然后，在执行器级别提出了一个实际控制器来生成输入电压控制信号。建议的控制器具有以下优点：(1) 基于相对距离和角度误差的一些预定义性能，避免了可能的超调和控制器奇异性，(2) 控制器不需要线性和角速度测量，(3) 未知的非线性和外生扰动通过以下方式得到有效补偿结合神经网络和自适应鲁棒控制器，以及 (4) 执行器动态在内环中得到补偿，而电压控制信号直接为每个机器人电机生成。Lyapunov 的稳定性分析证明了闭环控制系统的稳定性。最后，数值模拟结果将显示控制器的性能。(3) 神经网络和自适应鲁棒控制器相结合，有效地补偿了未知的非线性和外源干扰；(4) 执行器动态在内环中得到补偿，同时为每个机器人电机直接生成电压控制信号。Lyapunov 的稳定性分析证明了闭环控制系统的稳定性。最后，数值模拟结果将显示控制器的性能。(3) 神经网络和自适应鲁棒控制器相结合，有效地补偿了未知的非线性和外源干扰；(4) 执行器动态在内环中得到补偿，同时为每个机器人电机直接生成电压控制信号。Lyapunov 的稳定性分析证明了闭环控制系统的稳定性。最后，数值模拟结果将显示控制器的性能。