一类控制机构特性不确定非线性系统的镇定

运用非线性系统的精确线性化方法和Ｌｉｅ代数工具,将一类满足一定条件的。其中一个输入控制机构特性不确定的双输入－单输出偏向型非线性系统变换直接型的鲁里叶系统,再应用鲁里叶的绝对稳定性判据,通过选择恰当的状态反馈矩阵,使所得直接型鲁里叶闭环系统绝对稳定于坐标原点,从而使得原非线性系统在给定的控制机构不确定特性范围内镇定于其平衡点,并且求得了镇定控制律的解析形式。本文在将鱼时叶理论推广到非线性情形的同时     

一类不确定非线性系统的自适应模糊跟踪控制

针对一类不确定非线性连续系统，用模糊系统对未知函数进行逼近的基础上，利用Lyapunov稳定性理论，提出了一种新的自适应模糊跟踪控制算法，此算法的特点是，无论取多少条模糊系统的规则，自适应学习的参数只有一个，便于实现，而且还能确保闭环系统渐近稳定，并使系统的跟踪误差为零，仿真研究表明，所提出的算法是有效的。     

一类非线性不确定系统的分散保成本控制

针对一类不确定项具有有界约束特性的非线性组合系统,基于Lyapunov稳定性理论,运用线性矩阵不等式（LMI）处理方法,提出了系统存在非线性分散鲁棒保成本控制器的充分条件。该控制器能保证闭环系统渐近稳定并且能使所给定的线性二次性能指标具有确定的上界,并利用LMI方法给出了设计该控制器的一种算法。仿真实例表明,组合系统的所有状态均能很快收敛到零,该控制器是一个快速的保成本控制器,说明了所采用方法的有效性。     

一类不确定非线性系统的自适应模糊滑模控制

针对一类不确定非线性系统自适应模糊控制中,为了保证系统稳定性而附加监督控制问题,根据滑模控制原理并利用模糊系统的逼近能力,提出了一种Ⅰ型间接自适应模糊滑模控制方法。该方法取消了监督控制,用滑模控制器增加了逼近误差的自适应补偿,李雅普诺夫稳定性理论分析证明,控制系统全局稳定且跟踪误差收敛到零。将这种控制器应用到过程控制的典型对象液位控制中,仿真结果表明了该控制器的有效性和可行性。     

一类不确定系统的脉冲控制

关于线性不确定系统的鲁棒性能分析与控制，已有结果是基于连续系统，采用状态反馈的方法实现的，本文采用脉冲控制来镇定一类线性不确定系统，获得了脉冲控制下新的鲁棒稳定性判据，并通过实例进行了仿真，仿真结果表明，采用脉冲控制具有容易实现，能耗小的优点。     

一类非线性随机系统的自适应预测控制

根据线性滤波和谱分解定理及成型滤波器原理构成一非线性随机系统模型,结合在工作点处用线性的动态切平面逼近一般非线性系统的方法,基于准则函数和广义最优预测算法,采用NARMAX模型的非线性递推参数估计算法辨识未知参数,提出一种自适应预测控制算法,仿真研究验证了算法的有效性。     

一类不确定非线性系统的鲁棒镇定

针对一类含有不确定性的非线性系统,首先通过精确线性化方法,将其变为一种规范型形式,在一定的不确定性的假设条件下,构造了鲁棒控制器,仿真算例说明了方法的有效性。     

不确定滞后系统的鲁棒模型预测控制

对于输入受约束的离散不确定滞后系统，提出鲁棒模型预测控制方法，其中不确定性存在于系统的状态矩阵和输入矩阵当中，且满足范数有界条件。用LMI解决滞后系统的不确定性和约束，针对滞后系统给出了新的鲁棒性能指标上界和系统稳定的充分条件，通过求解LMI凸优化得到状态反馈控制律，对提出的方法进行了数字仿真研究，结果表明，基于LMI约束不确定滞后系统的鲁棒模型预测控制易于求解。适于实际应用。     

一类复杂非线性系统的特征模型自适应控制

传统的自适应控制理论不能胜任复杂的非线性系统,本文针对一类复杂的带滞后环节的非线性系统,采用特征模型黄金分割自适应控制方法进行研究。该控制方案是一种无模型控制策略,即控制器构造时不用系统精确模型,只凭借系统输入输出信息。分别将特征模型和PID控制方法用在系统上实施控制,考虑了不加扰动和加扰动两类情况下进行分析,结果表明特征模型黄金分割方法具有良好的控制性能,响应迅速,超调量小,鲁棒性更强。     

基于最优控制方法的一类不确定非线性系统的鲁棒控制

研究了一类不确定非线性系统的鲁棒控制问题,目的是在未满足不确定性匹配条件的情况下设计稳定系统的状态反馈。通过将设计鲁棒控制器问题转化为设计最优控制器问题以补偿系统中的不确定性,说明了最优控制的解即为鲁棒控制的解,并通过仿真说明这种设计方法的效果。     

Robust economic model predictive control of nonlinear networked control systems with communication delays

In this work, we consider economic model predictive control of nonlinear networked control systems subject to external disturbances and communication delays in both sensor-to-controller and controller-to-actuator channels. The problem is addressed in the framework of the min-max model predictive control. First, a delay compensation strategy is proposed to minimize the impact of communication delays on the control performance. In the compensation strategy, once the receiver at the controller node receives a new state measurement, the controller generates a control sequence and sends the sequence to the actuator to compensate for delayed control inputs. Subsequently, the presence of disturbance is explicitly considered for robustness and the semi-feedback min-max optimization algorithm is used to design the control law based on the estimate of the current state reconstructed by the estimator. Furthermore, the input-to-state practical stability of the proposed approach is established by constructing a modified Lyapunov function. Simulation results of a numerical example and a chemical process example demonstrate the applicability and effectiveness of our approach.     

输入受限的非线性系统模型预测控制

基于模糊T—S模型对输入受限的非线性离散系统,提出了模型预测控制,导出了预测控制性能指标上界,将稳定性约束、输入约束变换成容易求解的线性矩阵不等式(LMIs)形式。采用了状态反馈控制器和并行补偿分布控制器(PDC),基于李雅普诺夫函数和线性矩阵不等式方法给出滚动时域优化的充分条件,证明了闭环系统的稳定性。仿真结果验证了所提方法的有效性。     

An adaptive model predictive control strategy for a class of discrete-time linear systems with parametric uncertainty

In this study, an adaptive model predictive control (MPC) strategy is proposed for a class of discrete-time linear systems with parametric uncertainty. In the presented adaptive MPC, an updating law is firstly designed to update the estimated parameters online. By utilizing the estimated parameters, a standard MPC optimization problem for unconstrained systems is established. Then, to deal with constrained systems, the min–max MPC technique is developed under the set-based approach for the estimated parameters. Furthermore, it is shown theoretically that the recursive feasibility and closed-loop stability can be rigorously proved, respectively. Finally, numerical simulations and comparative analysis are presented to illustrate the superiority of the proposed algorithms in control performance.     

Microsecond nonlinear model predictive control for DC-DC converters

In this paper, we present a novel nonlinear model predictive control (NMPC) formulation for the transient control of a DC-DC converter. We demonstrate that a real-time implementation of the proposed NMPC scheme using the PANOC solver can be efficiently applied to control DC-DC converters in the microsecond range. Moreover, an embedded, code-generated version of PANOC can be implemented using microcontrollers or digital signal processors. The algorithm is incorporated in the transient simulator of PWM DC-DC converters, and the operation of the simulator on the boost converter's example is presented, comparing the performance of our NMPC-controller with that of a classical PID controller. The operation of the boost converter controlled using the proposed NMPC algorithm is validated experimentally.     

一类结构不确定性非线性系统动态反馈控制器的设计

研究带有结构不确定性的一般非线性系统的镇定问题.首先给出它的自治动态是Lyapunov稳定的充要条件,由此条件获得它可全局渐近稳定的充分条件,并构造了动态状态反馈控制器,可全局渐近稳定闭环系统的平衡点.仿真实例说明了所采用方法的有效性.     

非线性系统模糊间接自适应控制的后推设计

采用模糊神经网络作为非线性逼近器，针对一类一阶非线性多入多出系统，提出了一种具有扰动抑制的鲁棒自适应控制方法，给出了高阶多入多出系统具有扰动抑制的自适应后推(backstepping)设计方法。在鲁棒项合理简化的情况下，给出了系统Lyapunov意义下的稳定性证明，简略分析了各设计参数的物理意义及其对系统性能的影响。理论分析和仿真实验均显示，本方法可以保证系统的全局渐近稳定性，且若选取恰当的设计参数可保证系统对输入信号的跟踪达到任意精度；由于鲁棒项的引入可使系统的设计更具灵活性。     

基于T-S模糊模型的多变量非线性预测控制

针对多变量非线性系统预测控制的实时性问题,提出一种基于T-S模糊模型的预测控制快速算法.通过辨识建立非线性系统的T-S模糊模型,依据系统的运动特性和实际中控制输入增量的变化趋势选取特定基函数,将广义预测控制和预测函数控制相结合设计多变量预测控制律.该算法相比已有基于T-S模糊模型的多变量非线性广义预测控制算法大幅度减少了计算量,显著提高了控制的实时性.仿真结果验证了该算法的有效性.     

一种基于T-S云模型的非线性系统控制

将云模型与T-S模糊系统结合,利用隶属云代替模糊系统的前件,提出一种T-S云模型.T-S云模型综合考虑模型的精确性和可解释性,不但可以利用专家的知识和经验建立系统模型,而且还可以通过输入/输出数据设计云模型系统.详细分析T-S云模型的系统结构.基于云模型和模糊系统对模糊概念表述的一致性,在不考虑超熵的情况下,使用梯度下降法辨识T-S云模型前件参数.利用递推最小二乘法辨识T-S云模型后件参数.设计了基于T-S云模型的控制器,实现了将卡车后倒至指定的卸车位置,体现了T-S云模型的不确定处理能力.仿真研究验证了算法的有效性.     

Quasi‐ARX neural network based adaptive predictive control for nonlinear systems

In this paper, a new switching mechanism is proposed based on the state of dynamic tracking error so that more information will be provided –not only the error but also a one up to pth differential error will be available as the switching variable. The switching index is based on the Lyapunov stability theory. Thus the switching mechanism can work more effectively and efficiently. A simplified quasi‐ARX neural‐network (QARXNN) model presented by a state‐dependent parameter estimation (SDPE) is used to derive the controller formulation to deal with its computational complexity. The switching works inside the model by utilizing the linear and nonlinear parts of an SDPE. First, a QARXNN is used as an estimator to estimate an SDPE. Second, by using SDPE, the state of dynamic tracking error is calculated to derive the switching index. Additionally, the switching formula can use an SDPE as the switching variable more easily. Finally, numerical simulations reveal that the proposed control gives satisfactory tracking and disturbance‐rejection performances. Experimental results demonstrate its effectiveness. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Robust fuzzy control and evolutionary fuzzy identification of singularly perturbed nonlinear systems with parameter uncertainty

This paper presents an H _∞ robust fuzzy control strategy which stabilizes singularly perturbed (SP) nonlinear systems with bounded uncertainties and guarantees disturbance attenuation bounds for all admissible uncertainties. The modified Takagi–Sugeno (T–S) fuzzy linear models are used to describe the SP nonlinear systems. By the proposed fuzzy control strategy, the number and type of membership functions in the fuzzy control rules are not necessarily the same as those in the fuzzy models. A sufficient condition for the existence of the H _∞ robust fuzzy controllers is then presented in terms of a novel linear matrix inequalities (LMIs) form which takes full consideration of modeling error and uncertainties in system parameters. This condition provides extra design parameters with more flexibility in control gain selection. Furthermore, we propose a compound search strategy composed of island genetic algorithms concatenated with the simplex method to identify the uncertain SP nonlinear systems for the fuzzy control design, and to solve the LMI problem. Finally, design example of the proposed H _∞ robust fuzzy controller for an uncertain SP nonlinear system is presented.