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1.
提出一种新型的智能PID控制器。将前馈神经网络BP网络作用在弹性积分控制器上,在线调整控制器的参数,采用RBF神经网络作为辨识器在线辨识控制输出对控制输入对象变化的灵敏度信息,提高系统的控制精度。该智能控制器实现了整体性能优化和个别参数优化相结合的思想。通过MATLAB仿真,该新型控制器具有超调量低、鲁棒性好等控制效果。  相似文献   

2.
基于RBF神经网络空间矢量法对PMSM的控制   总被引:1,自引:0,他引:1  
将模糊径向基函数(f-RBF)神经网络算法用于永磁同步电机(PMSM)的速度控制.针对电机的动态和非线性特点,结合PMSM驱动的矢量控制方法,设计了f-RBF在线辨识器和速度控制器.在Matlab/Simulink下将该方法与传统的PID控制PMSM进行了仿真比较.实验结果表明了该方法的有效性,且系统响应速度快,动态性能优异,鲁棒性好.  相似文献   

3.
将线性网络应用于一类带扰动的线性对象,提出了一种基于该线性网络的自适应逆控制方案,该方案由辨识器、控制器和扰动消除器三部分构成,合理选择三个线性网络的输入,通过辨识器的在线学习,同时更新控制器和扰动消除器的权值,文章研究了该方案的收敛性和方案的跟踪性。根据可变步长权值收敛条件,设计了输入解相关变步长LMS算法调整辨识器权值方法。通过仿真研究了逆控制方法的有效性。  相似文献   

4.
郑一力  孙汉旭  刘晋浩 《机器人》2012,34(4):455-459
实现了一种对球形移动机器人的滚动速度进行控制的方法.球形移动机器人的控制输入和状态输出间存在难以精确数学描述的非线性关系,本文采用径向基函数神经网络,以在线训练的方式建立了球形机器人输入与输出的非线性映射;然后采用反馈线性化方法,设计了球形机器人的速度控制器,该控制器由反馈线性化控制器和减小神经网络逼近误差的补偿控制器构成;给出了该控制器的实现步骤.多次实验结果表明,该方法可以实现球形移动机器人稳定的速度控制.  相似文献   

5.
改进的Elman模型与递归反传控制神经网络   总被引:31,自引:0,他引:31       下载免费PDF全文
时小虎  梁艳春  徐旭 《软件学报》2003,14(6):1110-1119
在Elman网络的基础上提出了两种改进网络:输出-输入反馈Elman网络和输出-隐层反馈Elman网络模型,并以前者作为误差反传的通道,建立了递归反向传播控制神经网络模型.在Lyapunov稳定性意义下分别给出了改进网络的稳定性证明,得到了保证网络稳定收敛的最佳自适应学习速率.分别用Elman网络及其改进网络对超声马达进行了模拟.利用改进的Elman网络模型,除了可以较好地模拟马达速度以外,还得到了一些有意义的结果,据此可以根据现场数据采样的情况,选用不同的网络模型.模拟实验结果表明,递归反向传播控制神经网络对多种形式的超声马达参考速度都有很好的控制效果.  相似文献   

6.
基于永磁同步电动机(PMSM)的数学模型, 设计了由积分反步控制和滑模变结构模型参考自适应系统组成的无速度传感器矢量控制系统. 其中带有积分作用的反步控制作为矢量系统的速度和电流控制器, 实现给定速度和电流的无静差跟踪; 而滑模变结构模型参考自适应方法作为速度辨识器估计电机速度, 能够快速准确的跟踪实际速度. 通过Lyapunov定理证明了所设计的速度控制器和辨识器的稳定性. 仿真结果验证了所设计的无速度传感器矢量调速系统良好的速度跟踪性能和抗扰动性能.  相似文献   

7.
广义预测控制的直接算法   总被引:10,自引:0,他引:10  
采用三个辨识器分别辨识开环系统、闭环系统和控制器的参数,利用开环系统参数计算预测输出和参考轨迹,通过辨识闭环系统参数得到广义输出,用于辨识控制器的参数,并给出一种广义预测控制的直接算法。仿真结果表明该算法是有效可行的。  相似文献   

8.
赵琴  段广仁 《控制理论与应用》2018,35(10):1503-1510
针对航天器交会问题存在外部干扰和输入饱和的情况, 本文提出了一个输出反馈跟踪控制器. 仅利用测量得到的相对位置信息, 设计了一个滑模观测器用来估计相对角速度, 并根据该估计值设计了一个鲁棒反步控制律. 通过引入一个辅助系统, 对输入饱和情况进行了分析. 采用Lyapunov 稳定性理论, 证明了本文提出的该控制器能够保证位置和速度跟踪误差的一致有界性. 最后通过数值分析验证了所设计的输出反馈控制器的有效性.  相似文献   

9.
本文提出了一种新的限制输出个数减少随机多变量自适应控制中辨识参数的方法,并给出了减少辨识参数的极点配置自适应算法。虽然采用n个输入1个输出的减少辨识参数的模型来设计控制器,但所提出的控制器能够保证被控系统的几个输出跟踪参考输入信号,仿真结果表明,所提出的方法是成功的。  相似文献   

10.
针对航天器交会问题存在外部干扰和输入饱和的情况,本文提出了一个输出反馈跟踪控制器.仅利用测量得到的相对位置信息,设计了一个滑模观测器用来估计相对角速度,并根据该估计值设计了一个鲁棒反步控制律.通过引入一个辅助系统,对输入饱和情况进行了分析.采用Lyapunov稳定性理论,证明了本文提出的该控制器能够保证位置和速度跟踪误差的一致有界性.最后通过数值分析验证了所设计的输出反馈控制器的有效性.  相似文献   

11.
This paper presents a discrete-time direct current (DC) motor torque tracking controller, based on a recurrent high-order neural network to identify the plant model. In order to train the neural identifier, the extended Kalman filter (EKF) based training algorithm is used. The neural identifier is in series-parallel configuration that constitutes a well approximation method of the real plant by the neural identifier. Using the neural identifier structure that is in the nonlinear controllable form, the block control (BC) combined with sliding modes (SM) control techniques in discrete-time are applied. The BC technique is used to design a nonlinear sliding manifold such that the resulting sliding mode dynamics are described by a desired linear system. For the SM control technique, the equivalent control law is used in order to the plant output tracks a reference signal. For reducing the effect of unknown terms, it is proposed a specific desired dynamics for the sliding variables. The control problem is solved by the indirect approach, where an appropriate neural network (NN) identification model is selected; the NN parameters (synaptic weights) are adjusted according to a specific adaptive law (EKF), such that the response of the NN identifier approximates the response of the real plant for the same input. Then, based on the designed NN identifier a stabilizing or reference tracking controller is proposed (BC combined with SM). The proposed neural identifier and control applicability are illustrated by torque trajectory tracking for a DC motor with separate winding excitation via real-time implementation.  相似文献   

12.
《Computers & Structures》2007,85(21-22):1611-1622
In this paper, we first present a learning algorithm for dynamic recurrent Elman neural networks based on a modified particle swarm optimization. The proposed algorithm computes concurrently both the evolution of network structure, weights, initial inputs of the context units and self-feedback coefficient of the modified Elman network. Thereafter, we introduce and discuss a novel control method based on the proposed algorithm. More specifically, a dynamic identifier is constructed to perform speed identification and a controller is designed to perform speed control for Ultrasonic Motors (USM). Numerical experiments show that the novel identifier and controller based on the proposed algorithm can both achieve higher convergence precision and speed than other state-of-the-art algorithms. In particular, our experiments show that the identifier can approximate the USM’s nonlinear input–output mapping accurately. The effectiveness of the controller is verified using different kinds of speeds of constant, step and sinusoidal types. Besides, a preliminary examination on a randomly perturbation also shows the robust characteristics of the two proposed models.  相似文献   

13.
In this paper, real‐time results for a novel continuous‐time adaptive tracking controller algorithm for nonlinear multiple input multiple output systems are presented. The control algorithm includes the combination of a recurrent high order neural network with block control transformation using a high order sliding modes technique as control law. A neural network is used to identify the dynamic plant behavior where a filtered error algorithm is used to train the neural identifier. A decentralized high order sliding mode, named the twisting algorithm, is used to design chattering‐reduced independent controllers to solve the trajectory tracking problem for a robot arm with three degrees of freedom. Stability analyses are given via a Lyapunov approach.  相似文献   

14.
Adaptive recurrent neural control for nonlinear system tracking   总被引:1,自引:0,他引:1  
We present a new indirect adaptive control law based on recurrent neural networks, which are linear on the input. For the identifier, we adapt a recently published algorithm to fit the neural network type used for identification; this algorithm ensures exponential stability for the identification error. The proposed controller is based on sliding mode techniques. Our main result, stated as a theorem, concerns tracking error asymptotic stability. Applicability of the proposed scheme is tested via simulations.  相似文献   

15.
Diagonal recurrent neural networks for dynamic systems control   总被引:48,自引:0,他引:48  
A new neural paradigm called diagonal recurrent neural network (DRNN) is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer, and the hidden layer comprises self-recurrent neurons. Two DRNN's are utilized in a control system, one as an identifier called diagonal recurrent neuroidentifier (DRNI) and the other as a controller called diagonal recurrent neurocontroller (DRNC). A controlled plant is identified by the DRNI, which then provides the sensitivity information of the plant to the DRNC. A generalized dynamic backpropagation algorithm (DBP) is developed and used to train both DRNC and DRNI. Due to the recurrence, the DRNN can capture the dynamic behavior of a system. To guarantee convergence and for faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. Convergence theorems for the adaptive backpropagation algorithms are developed for both DRNI and DRNC. The proposed DRNN paradigm is applied to numerical problems and the simulation results are included.  相似文献   

16.
吴志敏  李书臣 《控制工程》2004,11(3):216-219
提出一种基于动态递归神经网络的自适应PID控制方案,该控制系统由神经网络辨识器和神经网络控制器组成。辨识器采用单隐层的动态递归神经网络,网络结构为2-4-1;辨识算法为动态BP算法;控制器采用两层线性结构的神经网络,输入为系统偏差及其一阶、二阶微分,因此具有增量型PID控制结构。应用该控制系统对一非线性时变系统进行仿真研究,仿真结果表明该控制方案不仅具有良好的跟踪特性,而且对系统参数变化具有较强的鲁棒性。  相似文献   

17.
The use of a proposed recurrent hybrid neural network to control of walking robot with four legs is investigated in this paper. A neural networks based control system is utilized to the control of four-legged walking robot. The control system consists of four proposed neural controllers, four standard PD controllers and four-legged planar walking robot. The proposed neural network (NN) is employed as an inverse controller of the robot. The NN has three layers, which are input, hybrid hidden and output layers. In addition to feedforward connections from the input layer to the hidden layer and from the hidden layer to the output layer, there is also feedback connection from the output layer to the hidden layer and from the hidden layer to itself. The reason to use hybrid layer is that robot’s dynamics consists of linear and non-linear parts. The results show that the proposed neural control system has superior performance to control trajectory of walking robot with payload.  相似文献   

18.
局部递归神经网络控制器及其应用   总被引:2,自引:1,他引:2  
基于人工神经网络提出了一种局部递归神经网络控制器。在描述了带有输出反馈和激活反馈的网络控制器的结构组成并定义了作为设计目标的误差函数后,采用带有弹性的梯度下降法,获得适用于实时在线调整权值的修正公式,给出了所提的网络控制器的设计步骤及其控制策略。将所提出的网络控制器应用到典型的单级倒立摆的实验系统中,将实验所获得的结果与LQY方法的实验结果进行了对比。  相似文献   

19.
A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input–output data sets obtaining from mathematical model simulation. The Levenberg–Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases.  相似文献   

20.
为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani型摸糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器。该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani型控制器的仿真对比,表明该Sugeno型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。  相似文献   

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