【摘要】 传统PID的控制参数难以精确整定,且依赖于对象的精确数学模型,适应性较差,对复杂过程不能保证其控制精度。针对工业控制领域中大滞后系统,采用传统PID控制不能获得满意的控制效果,提出基于RBF神经网络的PID控制参数自整定的方法。该方法利用RBF神经网络的自学习、自适应能力自调整系统的控制参数。仿真表明,该方法可实现有效的控制,并且与常规PID相比,具有更好的自适应性和鲁棒性。更多还原

【Abstract】 It is difficult to get precise parameters of classic PID controller, and the PID control method is relied on the precise math-ematical model badly. Since it can’ t acquire the satisfied control result using the traditional PID control for the big lag system in industry control field, a self-turning PID control strategy based on RBF network is put forward in this paper. This method uses the liability of self-study and self-adaptability of RBF network to turning parameters of system. Simulation results indicates that it can get satisfied control result, better self-adaptability and robustness using this method .更多还原