This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unkn...This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.展开更多
针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统,提出了一种鲁棒自适应神经网络控制算法.本算法采用RBF神经网络(Radial based function neural network,RBFNN)逼近模型不确定性,外界干扰和建模误差采用非线性阻尼...针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统,提出了一种鲁棒自适应神经网络控制算法.本算法采用RBF神经网络(Radial based function neural network,RBFNN)逼近模型不确定性,外界干扰和建模误差采用非线性阻尼项进行补偿,将动态面控制(Dynamic surface control,DSC)与后推方法结合,消除了反推法的计算膨胀问题,降低了控制器的复杂性;尤其是采用Nussbaum函数处理系统中方向未知的不确定虚拟控制增益函数,不仅可以避免可能存在的控制器奇异值问题,而且还能使得整个系统的在线学习参数显著减少,与DSC方法优点结合,使得控制算法的计算量大为减少,便于计算机实现.稳定性分析证明了所得闭环系统是半全局一致最终有界(Semi-global uniformly ultimately bounded,SGUUB)的,并且跟踪误差可以收敛到原点的一个较小邻域.最后,计算机仿真结果表明了本文所提出控制器的有效性.展开更多
基金partially supported by the National Natural Science Foundation of China(61703402,61374048)
文摘This paper focuses on designing an adaptive radial basis function neural network(RBFNN) control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively.The uniform ultimate boundedness of the closed-loop system is guaranteed by the proposed controller. A coupled motor drives(CMD) system, which satisfies the structure of nonlinear system,is taken for simulation to confirm the effectiveness of the method.Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
文摘针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统,提出了一种鲁棒自适应神经网络控制算法.本算法采用RBF神经网络(Radial based function neural network,RBFNN)逼近模型不确定性,外界干扰和建模误差采用非线性阻尼项进行补偿,将动态面控制(Dynamic surface control,DSC)与后推方法结合,消除了反推法的计算膨胀问题,降低了控制器的复杂性;尤其是采用Nussbaum函数处理系统中方向未知的不确定虚拟控制增益函数,不仅可以避免可能存在的控制器奇异值问题,而且还能使得整个系统的在线学习参数显著减少,与DSC方法优点结合,使得控制算法的计算量大为减少,便于计算机实现.稳定性分析证明了所得闭环系统是半全局一致最终有界(Semi-global uniformly ultimately bounded,SGUUB)的,并且跟踪误差可以收敛到原点的一个较小邻域.最后,计算机仿真结果表明了本文所提出控制器的有效性.
