共查询到19条相似文献,搜索用时 125 毫秒
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一类非线性非最小相位系统的直接自适应控制 总被引:1,自引:0,他引:1
针对一类不确定的离散时间非线性非最小相位动态系统,提出了一种基于神经网络和多模型的直接自适应控制方法.该控制方法由线性直接自适应控制器,神经网络非线性直接自适应控制器以及切换机构组成.线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能.切换策略通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善了系统性能.理论分析以及仿真结果表明了所提出的直接自适应控制方法的有效性. 相似文献
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一类非线性离散系统的直接自适应模糊控制 总被引:1,自引:0,他引:1
针对一类含延迟非线性离散系统,提出了一种直接自适应模糊控制器设计的新方案.将系统用T-S模糊模型来表示,并基于并行分布补偿(PDC)基本思想设计了一种具有未知参数的模糊控制器,同时采用梯度下降算法对该控制器的参数进行在线辨识.通过输入到状态稳定(ISS)方法,证明了系统输出和参考输出的误差有界且满足一定的平均性能.仿真表明本方法的有效性. 相似文献
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Chih-Min Lin Ang-Bung Ting Ming-Chia Li Te-Yu Chen 《Neural computing & applications》2011,20(4):557-563
In this study, a robust adaptive control (RAC) system is developed for a class of nonlinear systems. The RAC system is comprised
of a computation controller and a robust compensator. The computation controller containing a radial basis function (RBF)
neural network is the principal controller, and the robust compensator can provide the smooth and chattering-free stability
compensation. The RBF neural network is used to approximate the system dynamics, and the adaptive laws are derived to on-line
tune the parameters of the neural network so as to achieve favorable estimation performance. From the Lyapunov stability analysis,
it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. To investigate the effectiveness
of the RAC system, the design methodology is applied to control two nonlinear systems: a wing rock motion system and a Chua’s
chaotic circuit system. Simulation results demonstrate that the proposed RAC system can achieve favorable tracking performance
with unknown of the system dynamics. 相似文献
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Chun-Fei Hsu Chao-Ming Chung Chih-Min Lin Chia-Yu Hsu 《Expert systems with applications》2009,36(9):11836-11843
The cerebellar model articulation controller (CMAC) has the advantages such as fast learning property, good generalization capability and information storing ability. Based on these advantages, this paper proposes an adaptive CMAC neural control (ACNC) system with a PI-type learning algorithm and applies it to control the chaotic systems. The ACNC system is composed of an adaptive CMAC and a compensation controller. Adaptive CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between adaptive CMAC and ideal controller. Based on the Lyapunov stability theorems, the designed ACNC feedback control system is guaranteed to be uniformly ultimately bounded. Finally, the ACNC system is applied to control two chaotic systems, a Genesio chaotic system and a Duffing–Holmes chaotic system. Simulation results verify that the proposed ACNC system with a PI-type learning algorithm can achieve better control performance than other control methods. 相似文献
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An adaptive recurrent cerebellar-model-articulation-controller (RCMAC) sliding-mode control (SMC) system is developed for the uncertain nonlinear systems. This adaptive RCMAC sliding-model control (ARCSMC) system is composed of two systems. One is an adaptive RCMAC system utilized as the main controller, in which an RCMAC is designed to identify the system models. Another is a robust controller utilized to achieve system’s robust characteristics, in which an uncertainty bound estimator is developed to estimate the uncertainty bound so that the chattering phenomenon of control effort can be eliminated. The on-line adaptive laws of the ARCSMC system are derived in the sense of Lyapunov so that the system stability can be guaranteed. Finally, a comparison between SMC and ARCSMC for a chaotic system and a car-following system are presented to illustrate the effectiveness of the proposed ARCSMC system. Simulation results demonstrate that the proposed control scheme can achieve favorable control performances for the chaotic system and car-following systems without the knowledge of system dynamic functions. 相似文献
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为了提高三相异步电机矢量控制的性能,在传统的转速PID控制器的基础上,建立模糊PID控制器,利用Matlab/Simulink搭建基于三相异步电动机转速控制的模糊PID系统,分别使用常规PID控制器与模糊PID控制器进行控制,并进行比较。仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性。 相似文献
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This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme. 相似文献
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This paper investigates an intelligent adaptive control system for multiple-input–multiple-output (MIMO) uncertain nonlinear systems. This control system is comprised of a recurrent-cerebellar-model-articulation-controller (RCMAC) and an auxiliary compensation controller. RCMAC is utilized to approximate a perfect controller, and the parameters of RCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and RCMAC. Finally, two MIMO uncertain nonlinear systems, a mass–spring–damper mechanical system and a Chua’s chaotic circuit, are performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance with desired robustness. 相似文献
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A deterministic learning theory was recently presented which states that an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of nonlinear systems in normal form. In this paper, we further investigate deterministic learning of the class of nonlinear systems with relaxed conditions, and neural control of the class of system toward improved performance. Firstly, without the assumption on the upper bound of the derivative of the unknown affine term, an adaptive neural controller is proposed to achieve stability and tracking of the plant states to that of the reference model. When output tracking is achieved, a partial PE condition is satisfied, and deterministic learning from adaptive neural control of the class of nonlinear systems is implemented without the priori knowledge on the upper bound of the derivative of the affine term. Secondly, by utilizing the obtained knowledge of system dynamics, a neural controller with constant RBF networks embedded is presented, in which the learned knowledge can be effectively exploited to achieve stability and improved control performance. Simulation studies are included to demonstrate the effectiveness of the results. 相似文献
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This article presents a direct adaptive fuzzy control scheme for a class of uncertain continuous-time multi-input multi-output nonlinear (MIMO) dynamic systems. Within this scheme, fuzzy systems are employed to approximate an unknown ideal controller that can achieve control objectives. The adjustable parameters of the used fuzzy systems are updated using a gradient descent algorithm that is designed to minimize the error between the unknown ideal controller and the fuzzy controller. The stability analysis of the closed-loop system is performed using a Lyapunov approach. In particular, it is shown that the tracking errors are bounded and converge to a neighborhood of the origin. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance. 相似文献
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This paper focuses on the adaptive finite-time neural network control problem for nonlinear stochastic systems with full state constraints. Adaptive controller and adaptive law are designed by backstepping design with log-type barrier Lyapunov function. Radial basis function neural networks are employed to approximate unknown system parameters. It is proved that the tracking error can achieve finite-time convergence to a small region of the origin in probability and the state constraints are confirmed in probability. Different from deterministic nonlinear systems, here the stochastic system is affected by two random terms including continuous Brownian motion and discontinuous Poisson jump process. Therefore, it will bring difficulties to the controller design and the estimations of unknown parameters. A simulation example is given to illustrate the effectiveness of the designed control method. 相似文献