多电机变频调速同步系统的多模型预测控制

针对多电机同步系统具有强烈的非线性和耦合性,难以建立精确数学模型并施加有效控制的缺陷,提出一种多电机多模型动态矩阵预测控制算法．在辨识不同工况子模型的基础上,设计各自的子控制器,并利用加权和形式构造全局控制器,在有约束的条件下,通过滚动优化的策略获得合适的控制增量．控制过程中,无需进行磁链观测．仿真结果验证了该算法的有效性．

     

二维直线电机的多入多出无模型自适应轮廓控制

针对传统的单入单出控制器无法解决二维直线电机存在的非线性,不确定性以及强耦合作用等问题,依据无模型自适应控制不依赖于被控系统精确数学模型,仅需受控系统输入输出数据便能实现自适应控制这一特点,采用多入多出的紧格式动态线性化无模型自适应控制算法对二维直线电机XY轴进行整体控制器设计.同时,针对二维直线电机这种含有纯二阶积分环节的非自平衡系统,提出了多入多出无模型自适应控制改良方法,并进行严格的稳定性和收敛性证明.为了提高二维直线电机的轮廓精度,在多入多出无模型自适应控制改良方法的基础上,加入交叉耦合控制器,与传统的交叉耦合控制方法相比较,提高了跟踪精度和轮廓精度.最后通过仿真和实物实验证明了所提方案的有效性.     

一种感应电机多模型解耦控制方法研究

提出一种基于新型广义逆系统的感应电机多模型解耦控制方法.通过对电机输入空间进行划分,利用神经网络辨识得到其广义逆系统模型,并针对每个子逆模型设计相应的控制器,使得闭环控制系统的传递函数实现任意的零极点配置.通过对电机输入空间的训练数据进行聚类,计算参考信号对每个类的相异度以实现模型之间的切换.仿真实验表明,该方法可对电机的转速和磁链实现有效的解耦控制,整个控制系统具有良好的鲁棒性.     

基于多模型自适应控制器的感应电机变频调速系统

针对变频调速矢量控制系统存在的参数鲁棒性差这一难点问题,首次将多模型自适应控制理论应用于感应电机变频调速系统中。提出了基于多模型自适应控制器的变频调速系统的新型控制结构与控制策略;在转子磁场定向的同步旋转系M_T中,分别建立了转速子系统、磁链子系统模型集并设计了相应的多模型控制器集,由此实现了感应电机变频调速系统多模型自适应控制。     

机械手臂的多模型预测控制

针对机械手臂的非线性特点,提出了基于隶属度函数的多模型预测控制方法。该方法首先根据机械手臂的特点,选择合适的调度变量,将机械手臂的工作空间划分为若干个工作子空间,在每个子空间内的平衡点处对机械手臂进行线性化处理,得到相应的线性子模型,从而得到机械手臂的多模型表示;其次针对每个线性子模型设计局部预测控制器,使其在相应的子空间内达到控制要求;最后选择梯形隶属度函数与局部预测控制器进行加权求和,获得全局多模型预测控制器,以对机械手臂进行控制。仿真结果表明,当机械手臂的工作条件在大范围内变化时,全局多模型预测控制器的控制性能远优于常规PD控制器,达到了预期的控制目的。     

直接多模型自适应控制器设计——LMI方法

本文将线性矩阵不等式(LMI)方法引入直接多模型自适应控制, 将直接多模型控制器的 设计过程转化为求解线性矩阵不等式的可行解问题,同时给出在不同不确定参数范围内的多 个状态反馈控制器,并由此构成直接多模型自适应控制器．同时将直接多模型自适应控制推 广到多输入多输出被控对象的设定值跟踪问题,并给出稳定性分析结果．     

多模型混合H∞型鲁棒控制

基于多模型控制器设计方法中需要建立大量离线子模型,导致计算负担过大的问题,本文提出基于H_∞理论的多模型混合鲁棒控制器设计方法.该方法首先对参数变化区间进行分解,根据得到的子区间建立子模型,从而获得多模型集.然后依据理论设计子模型鲁棒控制器,具有良好的抗干扰性,并能有效地减少子模型数量;其次,针对各鲁棒控制器,设计混合信号实现混合输出,可以很好地解决切换多模型控制中存在的持续切换问题.最后,对本文所提方法进行仿真和聚合釜温度系统的应用研究,结果表明当被控对象参数存在不确定性和存在输入扰动的时候,该方法可以在较少子模型数量的前提下保证系统具有良好的鲁棒性.     

未知多变量非线性系统自适应模糊预测控制

对一类未知多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接用模糊逻辑系统组成的向量来设计预测控制器,并基于时变死区函数对控制器中的未知向量和广义误差估计值中的未知矩阵进行自适应调整．文中证明了此方法可使广义误差向量估计值收敛到原点的一个邻域内．     

基于动态模型库的多模型切换控制

针对含有有界扰动和模型参数跳变的离散时间系统,提出基于动态模型库的多模型切换控制方法.在模型参数范围未知情况下,利用在线学习的多模型自适应控制算法自动建立多模型,并对模型库中的子模型进行优化.采用具有积分特性的指标函数作为切换准则.在每一采样时刻根据其最小值来选择与实际系统最接近的模型,并将基于此模型的控制器切换为当前控制器.文中证明了该算法能够保证闭环系统的稳定性和跟踪误差的渐近收敛性.计算机仿真结果表明该算法的有效性.     

基于PID神经网络的多轮轮毂电机协调控制研究

多轮轮毂电机运行参数具有不确定性,因此难以精准计算电机协调控制量,所以设计基于PID神经网络的多轮轮毂电机协调控制方法。搭建多轮轮毂电机数学模型,在该模型下实时采集多轮轮毂电机转速、位置等运行参数,并计算电机协调控制量。装设PID神经网络协调控制器,利用PID神经网络算法生成协调控制指令,从转速、转矩等方面实现多轮轮毂电机的协调控制。测试结果表明,在所提方法的协调控制下,多轮轮毂电机在空载和负载工况下的转矩控制误差降低了21.25r/min和25.7r/min,转矩控制误差降低了6.55N·m和17.45N·m,电机运行平衡度有所提升,多轮轮毂电机协调控制效果更好。     

Fault-tolerant decentralized H-infinity control for multi-channel systems using homotopy method

This paper considers a fault-tolerant decentralized H-infinity control problem for multi-channel linear time-invariant systems. The purpose is to design a decentralized H-infinity output feedback controller to.stabilize the given system and achieve a certain H-infinity performance requirement both in the normal situation and in the situation where any one of the local controllers fails. The designed problem is reduced to a feasibility problem of a set of bilinear matrix inequalities （BMIs）. An algorithm is proposed to solve the BMIs. First, the normal situation is considered where all the local controllers are functioning. The local controllers are obtained from a standard centralized H-infinity controller by using a homotopy method imposing a structural constraint progressively. Secondly, the above case is extended to the one where any one of the local controllers fails. We again use a homotopy method where the coefficient matrices of the failed controller are decreased progressively to zero. The efficiency of the proposed algorithm is demonstrated by an example.     

Fault-tolerant decentralized H-infinity control for multi-channel systems using homotopy method

This paper considers a fault-tolerant decentralized H-infinity control problem for multi-channel linear time-invariant systems. The purpose is to design a decentralized H-infinity output feedback controller to stabilize the given system and achieve a certain H-infinity performance requirement both in the normal situation and in the situation where any one of the local controllers fails. The designed problem is reduced to a feasibility problem of a set of bilinear matrix inequalities (BMIs). An algorithm is proposed to solve the BMIs. First, the normal situation is considered where all the local controllers are functioning. The local controllers are obtained from a standard centralized H-infinity controller by using a homotopy method imposing a structural constraint progressively. Secondly, the above case is extended to the one where any one of the local controllers fails. We again use a homotopy method where the coefficient matrices of the failed controller are decreased rogressively to zero. The efficiency of the proposed algorithm is demonstrated by an example.     

Neurofuzzy network based self-tuning control with offset eliminating

The design of nonlinear controllers involves first selecting the input and then determining the nonlinear functions for the controllers. Since systems described by smooth nonlinear functions can be approximated by linear models in the neighbourhood of the selected operating points, the input of the nonlinear controller at these operating points can be chosen to be identical to those of the local linear controllers. Following this approach, it is proposed that the input of the nonlinear controller are similarly chosen, and that the local linear controllers are designed based on the integrating and k-incremental suboptimal control laws for their ability to remove offsets. Neurofuzzy networks are used to implement the nonlinear controllers for their ability to approximate nonlinear functions with arbitrary accuracy, and to be trained from experimental data. These nonlinear controllers are referred to as neurofuzzy controllers for convenience. As the integrating and k-incremental control laws have also been applied to implement self-tuning controllers, the proposed neurofuzzy controllers can also be interpreted as self-tuning nonlinear controllers. The training target for the neurofuzzy controllers is derived, and online training of the neurofuzzy controllers using a simplified recursive least squares (SRLS) method is presented. It is shown that using the SRLS method, computing time to train the neurofuzzy controllers can be drastically reduced and the ability to track varying dynamics improved. The performance of the neurofuzzy controllers and their ability to remove offsets are demonstrated by two simulation examples involving a linear and a nonlinear system, and a case study involving the control of the drum water level in the boiler of a power generation system.     

Distributed Secondary Control and Optimal Power Sharing in Microgrids

We address the control problem of microgrids and present a fully distributed control system which consists of primary controller, secondary controller, and optimal active power sharing controller. Different from the existing control structure in microgrids, all these controllers are implemented as local controllers at each distributed generator. Thus, the requirement for a central controller is obviated. The performance analysis of the proposed control systems is provided, and the finite-time convergence properties for distributed secondary frequency and voltage controllers are achieved. Moreover, the distributed control system possesses the optimal active power sharing property. In the end, a microgrid test system is investigated to validate the effectiveness of the proposed control strategies.      

Decentralized variable structure control of a two-arm robotic system

The control problem for a two-arm robotic system in coordinated motion is addressed. A hierarchical framework, employing two levels of control hierarchy, is utilized, and the decentralized model reference adaptive control approach using variable structure controllers (DMRA-VSC) is applied. Within the control hierarchy, the DMRA-VSC strategy is accomplished at the lower level, where control is responsible for the servoing of each joint. These local controllers are coordinated by the high-level, central controller, whose task is to provide the local controllers with the upper bound on the dynamical interactions with other subsystems. The local controllers are responsible for making each link follow the prescribed local reference subsystem in moving from one position to another. This is done using the local measurements and the information provided by the central controller. Advantages of the DMRA-VSC approach for multiple manipulator control include the inherent robustness properties to nonlinearities and interaction effects, the decentralization structure facilitating ease in multiple manipulator system programming and implementation, and the general structure of the controller which allows further extensions such as force feedback.     

挠性航天器的非线性PID和PI姿态控制器设计

针对存在模型不确性和常值干扰的挠性航天器, 提出一种不依赖于模型参数的非线性PID姿态控制器. 该控制器在小姿态偏差的情况下近似经典的线性PID控制器. 另外, 考虑到航天器上陀螺失效情况, 设计了一种仅需姿态测量信息的非线性PI控制器. 这两种控制器在局部均对常值干扰有抑制作用, 并能使无干扰作用的姿态控制系统半全局渐近稳定. 闭环系统的稳定性证明采用了奇异扰动理论, 以解决积分项的存在带来的稳定性分析问题. 文章最后用数学仿真验证了控制器的性能.     

基于多LPV 模型的调度离线鲁棒预测控制

针对一类具有大工作区域和快时变特性的约束非线性系统, 采用多个线性参数时变(LPV) 模型近似描述原非线性系统. 对于各LPV 模型, 设计基于参数独立Lyapunov 函数的局部离线预测控制器. 构造各局部控制器间的切换策略, 在保证切换稳定性的同时, 使相互重叠的稳定域覆盖期望的工作区域. 仿真结果表明, 相比于已有的调度预测控制方法, 所提出的方法不仅能够保证控制输入在给定的约束范围内, 而且在局部控制器切换次数少的情况下, 获得良好的控制性能.

     

Decentralized robust PI controller design for an industrial boiler

This paper presents a new scheme to design decentralized robust PI controllers for uncertain LTI multivariable systems. Sufficient conditions for closed-loop stability and closed-loop diagonal dominance (almost decoupling) of a multivariable system are obtained. Satisfying these conditions and robust performance of the overall system are modeled as local robust performance problems. Then, by appropriately selecting the time constants of the closed-loop isolated subsystems in the IMC (Internal Model Control) strategy, the defined local robust performance problems are solved. To design a decentralized robust PI controller for a real industrial utility boiler, a control oriented nonlinear model for the boiler is identified. The nonlinearity of the system is modeled as uncertainty for a nominal LTI multivariable system. Using the new proposed method, a decentralized PI controller for the uncertain LTI model is designed. The designed controller is applied to the real system. The simulation results show the effectiveness of the proposed methodology.     

Nonlinear system stabilization via hierarchical switching control

A nonlinear control system design framework predicated on a hierarchical switching controller architecture parameterized over a set of moving system equilibria is developed. Specifically, using equilibria-dependent Lyapunov functions, a hierarchical nonlinear control strategy is developed that stabilizes a given nonlinear system by stabilizing a collection of nonlinear controlled subsystems. The switching nonlinear controller architecture is designed based on a generalized lower semicontinuous Lyapunov function obtained by minimizing a potential function over a given switching set induced by the parameterized system equilibria. The proposed framework provides a rigorous alternative to designing gain-scheduled feedback controllers and guarantees local and global closed-loop system stability for general nonlinear systems     

Force Reflecting Bilateral Control of Master–Slave Systems in Teleoperation

In this paper, a simple structure design with arbitrary motion/force scaling to control teleoperation systems, with model mismatches is presented. The goal of this paper is to achieve transparency in presence of uncertainties. The master–slave systems are approximated by linear dynamic models with perturbed parameters, which is called the model mismatch. Moreover, the time delay in communication channel with uncertainties is considered. The stability analysis will be considered for two cases: (1) stability under time delay uncertainties and (2) stability under model mismatches. For the first case, two local controllers are designed. The first controller is responsible for tracking the master commands, while the second controller is in charge of force tracking as well as guaranteeing stability of the overall closed-loop system. In the second case, an additional term will be added to the control law to provide robustness to the closed-loop system. Moreover, in this case, the local slave controller guarantees the position tracking and the local master controller guarantees stability of the inner closed-loop system. The advantages of the proposed method are two folds: (1) robust stability of the system against model mismatches is guaranteed and (2) structured system uncertainties are well compensated by applying independent controllers to the master and the slave sites. Simulation results show good performance of the proposed method in motion tracking as well force tracking in presence of model mismatches and time delay uncertainties.