This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ...This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.展开更多
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 base...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.展开更多
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 ex...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.展开更多
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...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.展开更多
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 Squa...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。展开更多
选择静止无功补偿器(static var compensator,SVC)或其它类型的并联型无功补偿装置的安装地点对提高电力系统电压稳定性是一个重要而实际的课题。该文提出一种采用向量场正规形理论,以非线性参与因子为依据,确定SVC安装位置的新方法。...选择静止无功补偿器(static var compensator,SVC)或其它类型的并联型无功补偿装置的安装地点对提高电力系统电压稳定性是一个重要而实际的课题。该文提出一种采用向量场正规形理论,以非线性参与因子为依据,确定SVC安装位置的新方法。由于所提出的方法可计及电力系统非线性特性对电压稳定性的影响,因此与线性化分析方法相比,该文提出的方法在系统具有强非线性特性的条件下,仍能准确选择SVC的有效安装地点。为验证所提出方法的有效性,将所提出的方法用于New England39节点系统,确定在系统中使用SVC的最有效位置,通过对几种情况下系统电压稳定性指标的比较,验证所提出方法的有效性。展开更多
基金Supported by the National Natural Science Foundation of China(21076179)the National Basic Research Program of China(2012CB720500)
文摘This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
文摘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.
基金the National Natural Science Foundation of China (No.60375001,60775047,60402024).
文摘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.
文摘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.
文摘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。
文摘选择静止无功补偿器(static var compensator,SVC)或其它类型的并联型无功补偿装置的安装地点对提高电力系统电压稳定性是一个重要而实际的课题。该文提出一种采用向量场正规形理论,以非线性参与因子为依据,确定SVC安装位置的新方法。由于所提出的方法可计及电力系统非线性特性对电压稳定性的影响,因此与线性化分析方法相比,该文提出的方法在系统具有强非线性特性的条件下,仍能准确选择SVC的有效安装地点。为验证所提出方法的有效性,将所提出的方法用于New England39节点系统,确定在系统中使用SVC的最有效位置,通过对几种情况下系统电压稳定性指标的比较,验证所提出方法的有效性。
