【摘要】 针对农用机器人转向系统状态和控制具有复杂、时滞和增益时变的特性,将Adaline神经网络(ANN)与内模控制相结合,提出一种在线调整时滞时间和控制增益的自适应控制方法。建立基于Adaline网络的增益与时滞的辨识算法,即通过反馈误差在线优化,适应性地调整时滞时间和增益,克服参数时变对内模控制和被控对象模型的影响。仿真和试验结果表明,与常规的PID控制方法相比,该方法具有较高的控制精度、较强的自适应性和鲁棒性,完全适用于农用机器人转向系统的控制。更多还原

【Abstract】 Aiming at the characteristics of complex,time-delay and gain time-variation of agricultural robot steering system,the Adaline neural network(ANN) was applied to internal model control(IMC).An adaptive control method for time-delay online adjusting and gain control was proposed.An algorithm based on Adaline neural networks could adjust time-delay and gain adaptively,and overcome effects of time-variation parameters on IMC and plant model by online optimizing feedback error.Simulations and experimental results verified that compared to conventional PID control method,the proposed control method possessed the advantage of high precision,great adaptability and robustness,so it is feasible for agricultural robot steering system.更多还原