共查询到19条相似文献,搜索用时 121 毫秒
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一种基于Wiener模型的非线性预测控制算法 总被引:3,自引:0,他引:3
针对一类Wiener模型描述的非线性系统,提出了一种改进的非线性预测控制算法.该算法利用Laguerre函数描述Wiener模型动态线性部分的控制信号,将预测控制中在预测时域内优化求解未来控制输入序列转化为优化求解一组无记忆的Laguerre系数,以减少优化所需的计算量.利用静态模糊模型来逼近Wiener模型的非线性部分,将非线性预测控制优化问题转化为线性预测控制优化问题,克服了求控制输入时解非线性方程的困难,进而推导出了预测控制输入的解析式.CSTR过程的仿真结果表明了本文算法的有效性和可行性. 相似文献
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基于支持向量机的非线性系统模型预测控制 总被引:6,自引:1,他引:5
支持向量机是基于统计学习理论的新一代机器学习技术。由于使用结构风险最小化原则代替经验风险最小化原则.使它较好的解决了小样本情况下的学习问题。又由于其采用了核函数思想.使它把非线性问题转化为线性问题来解决,降低了算法的难度.具有全局最优、良好泛化能力等优越性能.得到广泛的研究。基于上述特性提出了一种基于支持向量机的非线性模型预测控制结构.其中使用遗传算法来求解预测控制律.随后用计算机仿真证明了此控制算法的正确性和有效性。 相似文献
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四旋翼飞行器运行中具有不稳定、非线性和强耦合特性,较难建立其准确的数学模型,针对这个问题,提出了基于RBF-ARX模型的预测控制设计方法;RBF-ARX模型是线性自回归(Auto-Regressive eXogenous,ARX)模型和高斯径向基函数(Radial Basis Function,RBF)神经网络相结合设计的模型,可用于建立非线性系统的全局模型,描述非线性系统的非线性特征。预测控制算法根据系统输入、输出信号预测对象未来输出变化趋势,并将其与系统实际输出的误差反馈校正,使误差最小;该法首先建立四旋翼飞行器的RBF-ARX模型结构,就模型参数的辨识、优化给出了详细分析;并基于该模型设计了系统预测控制器,最后通过仿真和实时控制效果证实了该方法的可行性和有效性。 相似文献
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针对广义预测控制(GPC)算法稳定性分析困难,对参数未知非线性系统提出一种稳定广义预测控制(DGPC)方法。该方法首先将非线性系统转换为时变线性系统,然后利用三次样条基函数逼近时变系统中的系数,通过带时变遗忘因子的递推最小二乘算法辨识系数获得对象模型。基于模型通过性能指标中的前馈增益设计来保证控制系统稳定,仿真结果验证了该方法的有效性。 相似文献
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提出了基于小波变换的非线性广义预测控制算法。预测模型采用Hammerstein模型,对于其静态非线性部分采用小波网络来辨识,动态线性部分用最小二乘法来辨识。这种辨识方法比传统的多项式拟合的模型误差要小得多。基于这种预测模型广义预测控制器弥补了传统广义预测控制的模型失配问题。以CSTR为例对所设计的控制器进行仿真研究,结果表明控制器能够取得良好的控制效果。 相似文献
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基于神经网络非线性系统的广义预测 总被引:1,自引:0,他引:1
为了对复杂的非线性系统进行广义预测控制 ,避免较长的离线训练 ,采用受控自回归积分滑动平均模型来描述线性子系统 ,用神经网络来逼近非线性子系统 ,利用递推最小二乘法和 Davidon最小二乘法分别作为线性子系统和非线性子系统的在线学习算法 ,建立了一种适合于广义预测控制的非线性系统控制模型。仿真结果证明 ,该模型在非线性系统的广义预测中的有效性 ,在实时控制中具有极其广阔的应用前景。 相似文献
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A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed. 相似文献
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A hybrid pseudo-linear RBF-ARX model that combines Gaussian radial basis function (RBF) networks and linear ARX model structure is utilized for representing the dynamic behavior of a class of smooth nonlinear and non-stationary systems. This model is locally linear at each working point and globally nonlinear within whole working range. Based on the structural characteristics of the RBF-ARX model, three receding horizon predictive control (RBF-ARX-MPC) strategies are designed: (1) the RBF-ARX-MPC algorithm based on single-point linearization (MPC-SPL); (2) the RBF-ARX-MPC algorithm based on multi-point linearization (MPC-MPL); and (3) the RBF-ARX-MPC algorithm based on globally nonlinear optimization (MPC-GNO). In the MPC-SPL, the future multi-step-ahead predictive output of the system is obtained based on the local linearization of the RBF-ARX model at only current working-point, while in the MPC-MPL the future long-term output prediction is obtained according to the future local characteristics from previous online optimization results of the RBF-ARX model based MPC. In the MPC-GNO, the globally nonlinear characteristics of the RBF-ARX model are fully used for online getting control variables of the MPC. Real-time control experiments for the three type MPCs are carried out on a water tank system, which are also compared with a classical PID control and a traditional linear ARX model-based MPC. The results verify that the modeling method and the model-based predictive control strategies are realizable and effective for the nonlinear and unstable system. Moreover, it is also shown that the MPC-GNO can obtain better control performance but need more computation time compared to the other MPCs, which makes it possible to be applied into some slowly varying processes. 相似文献
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This paper presents the design of a new robust model predictive control algorithm for nonlinear systems represented by a linear model with unstructured uncertainty. The linear model is obtained by linearizing the nonlinear system at an operating point and the difference between the nonlinear and linear model is considered as a Lipschitz nonlinear function. The controller is designed for the linear model, which fulfills the stabilization condition for the nonlinear term. Unlike previous studies that have not considered a valid Lipschitz matrix of nonlinear term in the design process, we propose an algorithm in this paper in which it is considered. Therefore, the closed loop stability of the nonlinear system is guaranteed. A novel SOS optimization problem to determine design parameters is introduced, which leads to improved closed‐loop performance in comparison to a trial and error tuning procedure. Furthermore, an algorithm is presented to enlarge the region of attraction for the nonlinear closed‐loop system. Stability is improved by checking some additional conditions if which the system may be unstable if not considered. The validity of the proposed algorithm is confirmed by examples. 相似文献
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Modeling and Control Approach to Coupled Tanks Liquid Level System Based on Function‐Type Weight RBF‐ARX Model 下载免费PDF全文
A multi‐input multi‐output (MIMO) FWRBF‐ARX model, which adopts radial basis function (RBF) neural networks with function‐type weights (FWRBF) to approximate the coefficients of the state‐dependent AutoRegressive model with eXogenous input variables (SD‐ARX), is utilized for describing the dynamics of a coupled tanks liquid system. Based on local linearization information of the MIMO FWRBF‐ARX model, a predictive control strategy is proposed. In the algorithm, the control actions of the model predictive control (MPC) are calculated based on the local linearization of the MIMO FWRBF‐ARX model at current working point. Real‐time control experiments are carried out on the coupled tanks liquid system. The detailed comparative experiments demonstrate the feasibility and effectiveness of the proposed modeling and model‐based control strategy for the coupled tanks plant. 相似文献
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永磁同步电机高效非线性模型预测控制 总被引:6,自引:0,他引:6
永磁电机控制器要求电机有很强的转速跟踪能力,并且要保证系统参数变化及负荷扰动下系统的鲁棒性. 永磁电机包含很多不确定因素,是强耦合的非线性系统,传统的线性控制器很难对其进行控制. 针对永磁电机的转速控制构造非线性模型预测控制方法. 非线性永磁电机模型通过输入-输出反馈线性化策略解耦成为新的线性系统. 为保证可行解的收敛性,提出一种迭代二次规划方法来处理由输入-输出反馈线性化导致的非线性约束. 仿真结果表明,控制器能有效降低计算负担,具有很好的动态控制性能,能抑制转矩脉动,并保证在参数变化和负荷扰动下控制系统的鲁棒性. 相似文献
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采用基于径向基神经网络(RBFNN)模型的非线性模型预测控制方法,被控对象选择火花塞点火(SI)发动机的空燃比(AFR)高度非线性复杂系统,利用渐消记忆最小二乘法实现基于RBFNN的SI发动机AFR系统建模以及参数在线自适应更新。针对非线性模型预测控制中寻优问题,运用序列二次规划滤子算法对最优控制序列进行求解,并加入滤子技术避免了罚函数的使用。在相同的实验环境下,与PI控制算法和Volterra模型预测控制方法进行仿真对比实验,结果表明,所提算法的控制效果明显优于其他两种方法。 相似文献