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基于自适应模糊PID控制器的非线性系统仿真 总被引:7,自引:1,他引:7
对于缺乏精确模型的过程或参数时变的滞后过程,传统PID控制难以达到良好的控制效果.普通模糊控制能够对一些非线性系统进行控制,并不需被控对象精确的数学模型,但是模糊控制难以消除系统的静态误差.针对复杂的非线性系统,设计了自适应模糊PID控制器.该控制器将模糊控制的动态性能好的优点和PID控制的稳态精度高的优点结合起来,采用模糊控制与PID控制分段控制策略,当偏差大于某一阈值时,采用模糊推理的方法调整系统的控制量,当偏差小于某一阈值时,切换到PID控制以消除系统的静态误差,较好地克服了传统PID控制和普通模糊控制所存在的主要问题.通过仿真实验分析,证明了该控制方法的有效性. 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 presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabilizes the overall closed‐loop system and keeps its error bounded, and the fuzzy compensator estimates and compensates for the nonlinear part of the system, thereby keeping the feedback gains reasonably low in the feedback controller. The fuzzy compensator is designed by applying the fuzzy approximation technique to the uncertain nonlinear term to be compensated. In the iterative learning controller, a simple learning control rule is used to achieve precise tracking of the reference signal and a parameter learning algorithm is used to update the parameters in the fuzzy compensator so as to identify the uncertain nonlinearity as much as possible. © 2000 John Wiley & Sons, Inc. 相似文献
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Yinggan Tang Mingyong CuiChangchun Hua Lixiang Li Yixian Yang 《Expert systems with applications》2012,39(8):6887-6896
Fractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared to PID controller, the tuning of FOPID is more complex and remains a challenge problem. This paper focuses on the design of FOPID controller using chaotic ant swarm (CAS) optimization method. The tuning of FOPID controller is formulated as a nonlinear optimization problem, in which the objective function is composed of overshoot, steady-state error, raising time and settling time. CAS algorithm, a newly developed evolutionary algorithm inspired by the chaotic behavior of individual ant and the self-organization of ant swarm, is used as the optimizer to search the best parameters of FOPID controller. The designed CAS-FOPID controller is applied to an automatic regulator voltage (AVR) system. Numerous numerical simulations and comparisons with other FOPID/PID controllers show that the CAS-FOPID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties. 相似文献
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《Engineering Applications of Artificial Intelligence》2003,16(5-6):425-430
Fuzzy model based predictive functional controller (FPFC) is applied to the magnetic suspension system—a pilot plant for magnetic bearing. High quality control requirements are short settle time with a-periodical step response and zero steady-state error. Open loop unstable process was stabilised with linear lead compensator. The FPFC was used as a cascade controller. Due to some model uncertainties, the Takagi–Sugeno fuzzy model of stabilised system was obtained using fuzzy identification. Comparing to PID, it improved quality and robustness performance. With its computational efficiency, it proved to be ideal solution for high sampling frequency systems. 相似文献
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一种基于Matlab的参数自调整模糊控制器的设计方法 总被引:1,自引:0,他引:1
本文介绍了一种在MATLAB的模糊控制工具箱中,通过编写S函数实现对量化因子和比例因子的在线自动调整来设计模糊控制器,从而有效地实现参数自调整模糊控制器的设计方法。为了验证参数自调整模糊控制器的优越性,分别进行了空调温度控制系统的PID控制、常规模糊控制和参数自调整模糊控制的仿真研究。结果表明,参数自调整模糊控制器较之常规的模糊控制器,在被控对象特性变化或较大扰动的情况下,控制系统能保持较好的性能,是一种较理想的控制方法,具有广阔的发展前景。 相似文献
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Many published papers show that a TSK-type fuzzy system provides more powerful representation than a Mamdani-type fuzzy system. Radial basis function (RBF) network has a similar feature to the fuzzy system. As this result, this article proposes a dynamic TSK-type RBF-based neural-fuzzy (DTRN) system, in which the learning algorithm not only online generates and prunes the fuzzy rules but also online adjusts the parameters. Then, a supervisory adaptive dynamic RBF-based neural-fuzzy control (SADRNC) system which is composed of a DTRN controller and a supervisory compensator is proposed. The DTRN controller is designed to online estimate an ideal controller based on the gradient descent method, and the supervisory compensator is designed to eliminate the effect of the approximation error introduced by the DTRN controller upon the system stability in the Lyapunov sense. Finally, the proposed SADRNC system is applied to control a chaotic system and an inverted pendulum to illustrate its effectiveness. The stability of the proposed SADRNC scheme is proved analytically and its effectiveness has been shown through some simulations. 相似文献
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房间温度控制是一复杂的控制系统,很难建立精确数学模型,用传统的PID控制很难达到较好的控制效果。本控制系统根据模糊控制技术和实际经验,采用模糊控制器以控制房间温度。仿真结果表明,设计的模糊自适应控制器控制效果良好,具有优良的鲁棒性和稳定性。 相似文献
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Though the control performances of the fuzzy neural network controller are acceptable in many previous published papers, the applications are only parameter learning in which the parameters of fuzzy rules are adjusted but the number of fuzzy rules should be determined by some trials. In this paper, a Takagi–Sugeno-Kang (TSK)-type self-organizing fuzzy neural network (TSK-SOFNN) is studied. The learning algorithm of the proposed TSK-SOFNN not only automatically generates and prunes the fuzzy rules of TSK-SOFNN but also adjusts the parameters of existing fuzzy rules in TSK-SOFNN. Then, an adaptive self-organizing fuzzy neural network controller (ASOFNNC) system composed of a neural controller and a smooth compensator is proposed. The neural controller using the TSK-SOFNN is designed to approximate an ideal controller, and the smooth compensator is designed to dispel the approximation error between the ideal controller and the neural controller. Moreover, a proportional-integral (PI) type parameter tuning mechanism is derived based on the Lyapunov stability theory, thus not only the system stability can be achieved but also the convergence of tracking error can be speeded up. Finally, the proposed ASOFNNC system is applied to a chaotic system. The simulation results verify the system stabilization, favorable tracking performance, and no chattering phenomena can be achieved using the proposed ASOFNNC system. 相似文献
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Marko Lepetič Igor Škrjanc Héctor G. Chiacchiarini Drago Matko 《Journal of Intelligent and Robotic Systems》2003,36(4):467-480
The implementation of the fuzzy predictive functional control (FPFC) on the magnetic suspension system is presented in the paper. The magnetic suspension system was in our case the pilot plant for magnetic bearing and is an open-loop unstable process, therefore a lead compensator was used to stabilize it. The high quality control requirements were a-periodical step response and zero steady-state error. Adding the integrator to a feedback causes overshoot. The solution to the problem was cascade control with fuzzy predictive functional controller in the outer loop. To cope with the unknown model parameters and the nonlinear nature of the magnetic system, a fuzzy identification based on FNARX model was used. After successful validation the obtained fuzzy model was used for controller design. The FPFC is compared with a cascade linear predictive functional control (PFC) and PID control. The results we obtained with the FPFC are very promising and hardly comparable with conventional control techniques. 相似文献
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针对复杂的非线性时变系统,研究了将模糊系统与普通PID相结合的自适应模糊PID控制系统,总结了该控制器的设计过程及设计方法。仿真结果表明,这种控制器是一种易于理解、便于实现、性能良好的控制器,能适用于非线性、时变、较强干扰的复杂系统。 相似文献
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研究了具有不确定项的非线性Willis环上脑动脉瘤系统的混沌控制和同步问题,提出了一种自适应模糊滑模变结构控制方法,设计了模糊滑模变结构控制器及自适应控制律,并从理论上证明了控制系统的稳定性。在该控制器的作用下,受控Willis脑动脉瘤系统能够达到任意目标轨道,且不受不确定性的影响,具有很强的鲁棒性。定值跟踪和同步控制的仿真结果表明了控制器的有效性。 相似文献
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对于具有非线性、大滞后和时变性的中央空调冷冻水系统,根据专家经验设计了专家PID控制器.MATLAB仿真研究表明,专家PID控制器同常规PID控制器相比,具有较高的动态特性,能够达到理想的控制效果. 相似文献
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Chun-Fei Hsu 《Engineering Applications of Artificial Intelligence》2013,26(4):1221-1229
Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme. 相似文献
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Ching-Hung Lee Bo-Hang Wang 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2009,13(1):1-12
This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive
supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for
obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For
nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal.
The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive
SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control
of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach
for nonlinear uncertain systems. 相似文献
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随着系统复杂度的提高和对象不确定性因素的增加,为克服线性PID动态性能和稳态性能差的缺陷,分析了非线性PID控制器各控制参数对误差的理想变化过程,构造非线性PID控制器。由于增益参数大量增加,传统参数优化方法不再适用,在分析蚁群算法的基础上,提出了基于感知自适应蚁群算法,并加入模糊自适应信息素更新机制,用于优化非线性PID控制器的设计方法。通过仿真实验将该控制器与基于蚁群算法的非线性PID控制器和基于蚁群算法、Z-N法的PID控制器进行对比,并对控制性能和收敛性能进行了分析,结果表明该算法有效克服了传统蚁群算法收敛速度较慢、容易陷入局部最优而停滞的缺陷,该控制器具有更好的动态性能和稳态性能。 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