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非线性不确定系统的模糊自适应 输出反馈跟踪 总被引：2，自引：0，他引：2
本文研究了非仿射非线性系统的模糊自适应 输出反馈跟踪。在非仿射非线性模型存在不确定的情况下，使用模糊自适应控制器对系统进行控制，并基于Lyapunov稳定性定理得出自适应律。通过解一个代数Riccati方程实现了 跟踪性能。估计状态通过引入高增益观测器得到，实现了系统的输出反馈控制。最后，通过对一个数值例子的仿真验证了算法的有效性。 相似文献
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对一类不确定系统提出了模糊控制方法,基于矩阵的奇异值分解方法,采用模糊逻辑系统组成的矩阵逼近控制器中的逆矩阵以避免控制器的奇异性问题,用误差补偿项对模糊逻辑系统的逼近误差进行补偿。 相似文献
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由于变桨距风力发电系统具有复杂非线性,参数变化等特性,精确建立变桨距风电机组的数学模型是十分困难的,这就要求控制器对被控对象的模型误差、参数变化以及外部干扰有极佳的不敏感性。将提出的自适应模糊滑模控制器应用到变桨距风力发电机组的桨距角控制策略中。仿真结果表明：此方法具有很强的鲁棒性,能够较好的抑制风速波动的影响,改善了风力发电机组的变桨距控制效果,整个控制系统具有速度响应快、跟踪能力强、控制精度高、自适应能力优的特点。 相似文献
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针对一类不确定非线性时滞系统,提出了一种具有确定逼近域的自适应模糊控制器的设计方案。在动态面控制（DSC）的基础上,通过时滞代换技巧,使得自适应模糊逼近器的输入为参考信号,从而可以明确定义逼近域,同时可以处理系统中完全未知的时滞信号。基于Lyapunov-Krasovskii范函,证明闭环系统所有信号为半全局一致有界的,并且跟踪误差可以收敛到原点附件的一个小邻域内。仿真结果进一步说明了该方法的有效性。 相似文献
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针对非线性,不确定性的一级倒立摆系统,本文提出了基于切换模糊化的自适应模糊滑模控制器,通过自适应模糊控制方法,将滑模控制器中的切换项进行模糊逼近,可将切换项连续化,削弱了滑模控制的抖振现象。仿真结果证明,本控制系统有较强的鲁棒性和自适应跟踪能力。 相似文献
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在伺服电动机控制系统的使用中,由于电动机存在端部效应引起的动子磁链非正弦性、摩擦非线性以及负载的变化等都将使高精度位置伺服系统性能变坏,因此必须采用鲁棒性强的控制策略来抑制这些扰动。本文设计了一种带积分滑模面的自适应模糊滑模控制系统,并将其应用于伺服电动机的位置控制系统中。自适应模糊滑模控制系统由模糊控制和切换控制组成,运用模糊控制器来模拟反馈线性化控制率,使用切换控制来补偿滑模控制器的输出误差。调节算法是从李雅普洛夫稳定性理论得到的,从而可以保证系统的稳定性。经仿真验证,所设计的系统性能令人满意,而且对参数变化和外部负载扰动具有较强的鲁棒性。 相似文献
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为了提高轮式机器人控制精度,提出了一种新型的机器人速度控制方法。设计了一种基于大脑情感学习的速度误差自适应调节器,通过计算大脑情感数学模型内部节点权值的在线学习,对控制器中的参数进行自适应调整,从而实现机器人各轮子速度的自适应补偿。仿真结果表明,该速度控制算法速度跟踪响应更快,跟踪精度更高。 相似文献
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本文结合数控机床进给系统模型,将具有自学习和自适应能力的神经元模型与PID控制算法相结合,形成神经元自适应PID控制器应用于数控机床进给系统中,可以有效地适应各种工况,减少超调量,具有较好的鲁棒性和抗干扰性及跟踪性能。采用仿真和实验结果表明,该控制器具有一定的理论和实际应用价值。 相似文献
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本文通过系统地分析模糊控制和神经网络控制系统的结构、算法等问题,探讨了把模糊控制和神经网络控制技术结合起来的理论与实现,用神经网络的层和节点分别对应模糊系统的各个部分,将模糊控制规则和隶属函数隐含地分布在整个网络中,用神经网络实现模糊推理,以神经网络的在线自学习能力实现模糊控制规则的改变。设计了模糊神经网络,并应用到冷冻水泵变频调控制系统中,实现了水泵电机的转速智能控制。 相似文献
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《Journal of The Franklin Institute》2022,359(11):5458-5487
In this paper, a novel fast attitude adaptive fault-tolerant control (FTC) scheme based on adaptive neural network and command filter is presented for the hypersonic reentry vehicles (HRV) with complex uncertainties which contain parameter uncertainties, un-modeled dynamics, actuator faults, and external disturbances. To improve the performance of closed-loop FTC, command filter and neural network are introduced to reconstruct system nonlinearities that are related to complex uncertainties. Compared with the FTC scheme with only neural network, the FTC scheme with command filter and neural network has fewer controller design parameters so that the computational complexity is decreased and the control efficiency is improved, which is of great significance for HRV. Then, the adaptive backstepping fault-tolerant controller based on command filter and neural network is designed, which can solve the complexity explosion problem in the standard backstepping control and the small uncertainty problem in the backstepping control only containing command filter. Moreover, to improve the approximation accuracy of the neural network-based universal approximator, an adaptive update law of neural network weights is designed by using the convex optimization technique. It is proved that the presented FTC scheme can ensure that the closed-loop control system is stable and the tracking errors are convergent. Finally, simulation results are carried out to verify the superiority and effectiveness of the presented FTC scheme. 相似文献
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《Journal of The Franklin Institute》2019,356(12):5944-5960
Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods. 相似文献
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《Journal of The Franklin Institute》2021,358(17):9034-9060
This paper presents a new Takagi-Sugeno-Kang fuzzy Echo State Neural Network (TSKFESN) structure to design a direct adaptive control for uncertain SISO nonlinear systems. The proposed TSKFESN structure is based on the echo state neural network framework containing multiple sub-reservoirs. Each sub-reservoir is weighted with a TSK fuzzy rule. The adaptive law of the TSKFESN-based direct adaptive controller is derived by using a fractional-order sliding mode learning algorithm. Moreover, the Lyapunov stability criterion is employed to verify the convergence of the fractional-order adaptive law of the controller parameters. The evaluation of the proposed direct adaptive control scheme is verified using two case studies, the regulation problem of a torsional pendulum and the speed control of a direct current (DC) machine as a real-time application. The simulation and the experimental results show the effectiveness of the proposed control scheme. 相似文献
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从信息科学的角度详尽分析阐述了现代工业计算机控制系统从信息分立发展到信息共享的科学必然性及现场总线控制系统 (FCS)产生的重大意义 ,探索了将模糊控制和神经网络应用于现场总线控制系统中的方式和影响 ,并以智能模糊仪表为例给出了智能仪表新定义和功能特性 相似文献
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DSP-based synchronous control of dual linear motors via Sugeno type fuzzy neural network compensator
A digital signal processor (DSP)-based complementary sliding mode control (CSMC) with Sugeno type fuzzy neural network (SFNN) compensator is proposed in this study for the synchronous control of a dual linear motors servo system installed in a gantry position stage. The dual linear motors servo system comprises two parallel permanent magnet linear synchronous motors (PMLSMs). The dynamics of the single-axis motion system with a lumped uncertainty which contains parameter variations, external disturbances and nonlinear friction force is briefly introduced first. Then, a CSMC is designed to guarantee the precision position tracking requirement in single-axis control for the dual linear motors. Moreover, to enhance the robustness to uncertainties and to eliminate the synchronous error of dual linear motors, the CSMC with a SFNN compensator is proposed where the SFNN compensator is designed mainly to compensate the synchronous error. Furthermore, to increase the control performance of the proposed intelligent control approach, a 32-bit floating-point DSP, TMS320VC33, is adopted for the implementation of the proposed CSMC and SFNN. In addition, some experimental results are illustrated to show the validity of the proposed control approach. 相似文献
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Auto-structuring fuzzy neural system for intelligent control 总被引：1，自引：0，他引：1
Kuo-Hsiang Cheng 《Journal of The Franklin Institute》2009,346(3):267-288
An auto-structuring fuzzy neural network-based control system (ASFNS), which includes the auto-structuring fuzzy neural network (ASFNN) controller and the supervisory controller, is proposed in this paper. The ASFNN is used as the main controller to approximate the ideal controller and the supervisory controller is incorporated with the ASFNN for coping with the chattering phenomenon of the traditional sliding-mode control. In the ASFNS, an automatic structure learning mechanism is proposed for network structure optimization, where two criteria of node-adding and node-pruning are introduced. It enables the ASFNN to determine the nodes autonomously while ensures the control performance. In the ASFNS, all the parameters are evolved by the means of the Lyapunov theorem and back-propagation to ensure the system stability. Thus, an intelligent control approach for adaptive control is presented, where the structure and parameter can be evolved simultaneously. The proposed ASFNS features the following salient properties: (1) on-line and model-free control, (2) relax design in controller structure, (3) overall system stability. To investigate the capabilities, the ASFNS is applied to a kind of nonlinear system control. Through the simulation results the advantages of the proposed ASFNS can be validated. 相似文献
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Jinliang Liu Yuda Wang Lijuan Zha Huaicheng Yan 《Journal of The Franklin Institute》2019,356(16):9451-9473
This paper investigates the event-based control for networked T-S fuzzy cascade control systems with quantization and cyber attacks. In order to solve the problem of limited communication resources, an event-triggered scheme and a quantization mechanism are adopted, which can effectively reduce the burden of communication and save the network resources of the system. By considering the influence of cyber attacks, a newly quantized T-S fuzzy model for networked cascade control systems (NCCSs) under the event-triggered scheme is established. By using the Lyapunov stability theory, sufficient conditions guaranteeing the asymptotical stability of networked T-S fuzzy cascade control systems are obtained. In addition, the controller gains are derived by solving a set of linear matrix inequalities. Finally, a numerical example is presented to verify the validity of the proposed method. 相似文献
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In this paper, an adaptive TSK-type CMAC neural control (ATCNC) system via sliding-mode approach is proposed for the chaotic symmetric gyro. The proposed ATCNC system is composed of a neural controller and a supervisory compensator. The neural controller uses a TSK-type CMAC neural network (TCNN) to approximate an ideal controller and the supervisory compensator is designed to guarantee system stable in the Lyapunov stability theorem. The developed TCNN provides more powerful representation than the traditional CMAC neural network. Moreover, all the control parameters of the proposed ATCNC system are evolved in the Lyapunov sense to ensure the system stability with a proportional–integral (PI) type adaptation tuning mechanism. Some simulations are presented to confirm the validity of the proposed ATCNC scheme without the occurrence of chattering phenomena. Further, the proposed PI type adaptation laws can achieve faster convergence of the tracking error than that using integral type adaptation laws in previous published papers. 相似文献