Support vector machine based nonlinear model multi-step-ahead optimizing predictive control

A support vector machine with guadratic polynomial kernel function based nonlinear model multi-step-ahead optimizing predictive controller was presented. A support vector machine based predictive model was established by black-box identification. And a quadratic objective function with receding horizon was selected to obtain the controller output. By solving a nonlinear optimization problem with equality constraint of model output and boundary constraint of controller output using Nelder-Mead simplex direct search method, a sub-optimal control law was achieved in feature space. The effect of the controller was demonstrated on a recognized benchmark problem and a continuous-stirred tank reactor. The simulation results show that the multi-step-ahead predictive controller can be well applied to nonlinear system, with better performance in following reference trajectory and disturbance-rejection.

Nonlinear Model Predictive Control Based on Support Vector Machine with Multi-kernel

非线性的系统和它的特定的鉴定方法的 Multi-kernel-based 支持向量机器(SVM ) 模型结构被建议，它 SVM 是镇静的，线性内核功能与花键内核功能由 SVM 在系列列在后面。在这个模型的帮助下，非线性的模型预兆的控制能被转变到线性模型预兆的控制，并且因而预兆的控制是可能的发源的 multi-step-ahead 的最佳的输入的一个统一分析答案。这个算法不要求联机反复的优化以便对有更少的计算的即时控制合适。pH 中立化过程和 CSTR 反应堆的模拟结果显示出介绍算法的有效性和优点。

Nonlinear decoupling controller design based on least squares support vector regression

Support Vector Machines (SVMs) have been widely used in pattern recognition and have also drawn considerable interest in control areas. Based on a method of least squares SVM (LS-SVM) for multivariate function estimation, a generalized inverse system is developed for the linearization and decoupling control of a general nonlinear continuous system. The approach of inverse modelling via LS-SVM and parameters optimization using the Bayesian evidence framework is discussed in detail. In this paper, complex high-order nonlinear system is decoupled into a number of pseudo-linear Single Input Single Output (SISO) subsystems with linear dynamic components. The poles of pseudo-linear subsystems can be configured to desired positions. The proposed method provides an effective alternative to the controller design of plants whose accurate mathematical model is un- known or state variables are difficult or impossible to measure. Simulation results showed the efficacy of the method.

Support Vector Machine-Based Nonlinear System Modeling and Control

This paper provides an introduction to a support vector machine, a new kernel-based technique introduced in statistical learning theory and structural risk minimization, then presents a modeling-control framework based on SVM. At last a numerical experiment is taken to demonstrate the proposed approach's correctness and effectiveness.

Inverse Learning Control of Nonlinear Systems Using Support Vector Machines

An inverse learning control scheme using the support vector machine (SVM) for regression was proposed. The inverse learning approach is originally researched in the neural networks. Compared with neural networks, SVMs overcome the problems of local minimum and curse of dimensionality. Additionally, the good generalization performance of SVMs increases the robustness of control system. The method of designing SVM inverse learning controller was presented. The proposed method is demonstrated on tracking problems and the performance is satisfactory.

SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control

A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

A novel excitation controller using support vector machines and approximate models

This paper proposes a novel excitation controller using support vector machines （SVM） and approximate models. The nonlinear control law is derived directly based on an input-output approximation method via Taylor expansion, which not only avoids complex control development and intensive computation, but also avoids online learning or adjustment. Only a general SVM modelling technique is involved in both model identification and controller implementation. The robustness of the stability is rigorously established using the Lyapunov method. Several simulations demonstrate the effectiveness of the proposed excitation controller.

Learning control of nonhonolomic robot based on support vector machine

A learning controller of nonhonolomic robot in real-time based on support vector machine(SVM)is presented.The controller includes two parts:one is kinematic controller based on nonlinear law,and the other is dynamic controller based on SVM.The kinematic controller is aimed to provide desired velocity which can make the steering system stable.The dynamic controller is aimed to transform the desired velocity to control torque.The parameters of the dynamic system of the robot are estimated through SVM learning algorithm according to the training data of sliding windows in real time.The proposed controller can adapt to the changes in the robot model and uncertainties in the environment.Compared with artificial neural network(ANN)controller,SVM controller can converge to the reference trajectory more quickly and the tracking error is smaller.The simulation results verify the effectiveness of the method proposed.

LS-SVM model based nonlinear predictive control for MCFC system

This paper describes a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). In order to improve MCFC’s generating performance, prolong its life and guarantee safety, it must be controlled efficiently. First, the output voltage of an MCFC stack is identified by a least squares support vector machine (LS-SVM) method with radial basis function (RBF) kernel so as to implement nonlinear predictive control. And then, the optimal control sequences are obtained by applying genetic algorithm (GA). The model and controller have been realized in the MATLAB environment. Simulation results indicated that the proposed controller exhibits satisfying control effect.

SVM Approximate-based Internal Model Control Strategy

一台支持向量机器(SVM ) 近似底的内部模型控制(IMC ) 策略为同步发电机的蒸气 valving 控制被介绍。建议 SVM IMC 策略包括二主要部分：在内部模型结构的 SVM 近似反的控制器和无常赔偿。SVM 反的控制器经由泰勒扩大用一条输入产量近似途径直接被导出，并且没有进一步的联机训练，它通过非线性的系统鉴定被实现。而且，一个坚韧性过滤器在内部模型结构被用于无常赔偿。模拟为蒸气 valving 控制显示出 SVM IMC 策略的有效性。

基于整体辨识策略的非线性自适应控制方法

针对一类离散时间下的未知动态非线性系统,为解决传统自适应控制方法在交替辨识非线性系统时由于辨识精度低而导致的控制性能差的问题,本文提出了一种基于整体辨识策略的未建模动态补偿的自适应控制方法.利用随机向量函数链接(RVFL)网络的直链与增强结构特性挖掘其与低阶线性模型和高阶未建模动态项的等价对应关系,并融入权值偏差惩罚项,设计了网络模型参数在线更新算法辨识非线性系统参数.根据在线辨识的线性模型参数和未建模动态估计量,采用一步超前最优控制策略设计线性控制器和未建模动态补偿器.数值仿真表明,所提方法优于交替辨识下的非线性自适应控制方法,并通过工业应用的仿真研究验证所提方法在工业上的可用性.最后,对本文控制方法在实际应用中的潜在问题及理论受限条件的放松进行分析和展望.

Nonlinear model predictive control with relevance vector regression and particle swarm optimization

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.

永磁同步电机的自适应逆控制

设计了自适应逆控制的永磁同步电机(PMSM)控制系统,控制系统采用双闭环结构的矢量控制,将自适应逆控制方法引入速度控制。运用非线性自适应滤波器,实现系统的建模与逆建模,并引入滤波器构成了速度控制器,采用最小均方差(Least Mean Square,LMS)自适应滤波算法在线调整其权函数,实现速度的精确控制。在基于DSP的永磁同步电机速度控制系统平台上的实验结果表明,非线性滤波器能够建立电流环模型,提出的非线性自适应逆控制方法能够实现精确的速度控制。与PID控制方法相比,具有更精确的速度跟踪性及更快的响应速度。

推力矢量控制与飞机过失速机动仿真研究

非线性动态逆为非线性系统控制律的设计提供了一种重要手段。针对过失速机动飞机探讨了非线性动态逆方法在飞行控制系统设计中的应用。根据非线性动态逆的特点将飞机的动态特性分为快慢不同的状态变量。对快变量应用非线性动态逆进行控制律设计是将飞机气动控制面和推力矢量控制作为输入；慢状态变量控制律的设计是利用快状态变量作为输入。并采用伪逆的优化计算方法对各控制面的操纵权限分配进行了探讨。“Cobra”和“Herbst”两个过失速机动的实现证明该设计方法和仿真方法是成功的。

基于支持向量机的非线性预测控制技术

探讨了利用支持向量机进行非线性系统辨识的方法 ,并将支持向量机模型应用到非线性预测控制 ,提出了基于支持向量机模型的非线性预测控制算法 .对一个CSTR反应器的仿真表明 ,支持向量机在小样本情况下具有良好的非线性建模能力和泛化能力 .基于支持向量机的预测控制具有很好的控制性能 。

基于支持向量机的非线性系统预测控制

针对离散非线性系统,提出一种可用于非线性过程的支持向量机预测控制方法,并给出了控制律的收敛性分析.该方法将复杂的非线性预测方程转化成直观而有效的线性形式,同时利用线性预测控制方法求得解析的控制律,避免了复杂的非线性优化求解,对非线性工业焦化装置温度控制的仿真结果表明了算法的有效性.

综合飞行/推力矢量控制系统设计与仿真

对带推力矢量控制的飞控设计问题进行了研究．按飞机状态变化快慢将其分成快变和慢状态，然后采用非线性动态逆理论分别进行设计．针对战斗机大迎角飞行，以及将所设计控制律联入综合飞行／火力控制系统的瞄准攻击过程在六自由度全量飞机方程上进行数学仿真．仿真结果表明，综合飞行／推力矢量控制可实现战斗机大迎角飞行，并能够满足综合飞行／火力控制系统性能需求，所采用的设计方法在工程上具有应用前景．

基于自抗扰控制器的感应电机变频调速系统

设计了由4个一阶自抗扰控制器构成的多环调速系统.该系统将电机模型中的交叉耦合项、易变参数以及负载统一归为未知"扰动",用自抗扰控制器的扩张状态观测器进行观测,并采用非线性状态误差反馈控制器补偿,实现控制系统的精确解耦和简单线性化.该控制方案不需要精确的电机参数,使得自抗扰控制器的设计能够独立于感应电机的精确数学模型.半物理仿真软件Saber Designer中的器件级仿真实验表明,相对于经典PID控制器,自抗扰控制器对负载扰动和电机参数变化具有更好的鲁棒性和动态性能.

支持向量机的多层动态自适应参数优化

首先提出了基于多层动态自适应搜索技术的最小二乘支持向量机参数优化方法,然后采用最小二乘支持向量机对典型非线性控制系统的辨识进行了研究.辨识结果表明,最小二乘支持向量机可以用于非线性控制系统辨识,多层动态自适应搜索方法确定了最优支持向量机参数,从而获得精确的非线性控制系统辨识结果.

基于支持向量机的参数自整定PID非线性系统控制

对非线性系统提出了一种基于支持向量机的自整定PID控制新方法.用支持向量机辨识系统的非线性关系,并对之进行线性化,提取出瞬时线性模型,采用最小方差的准则获取PID控制器的最优参数.为改善控制器的性能,提出了一些改进措施,包括使用一阶滤波器、控制器参数更新标准及惩罚系数的调整等.通过对典型非线性系统的仿真.验证了该方法的有效性和可行性.