基于Backstepping 方法的多变量系统自适应控制

基于Backstepping设计方法,对线性多变量系统设计出一种新的自适应控制器,使某些系统关于增益矩阵的假定条件便于检验,该控制器能保证系统的全局稳定性和跟踪误差趋于零。     

具有理想跟踪特性的多变量变结构模型参考自适应控制

考虑一个广义相对阶为1的多变量系统之变结构模型参考自适应控制器(VS-MRAC) 设计问题,提出了一种切换控制方案,解决了现有多变量VS-MRAC和多变量模型参考自适应 控制(MRAC)中两个尚未解决的问题:1)使系统跟踪误差的每个分量均满足预先给定的性能指 标;2)无须假定对象高频增益矩阵满足通常的正定性条件.     

时滞扩散性复杂网络同步保性能控制

针对节点扩张的时滞复杂网络系统, 在节点扩张的条件下, 讨论此类系统的同步保性能控制问题. 首先采用自适应控制方法, 利用Lyapunov-Krasovskii稳定性理论,结合矩阵不等式的凸优化问题处理方法, 得出了时 滞复杂网络系统保性能控制器存在的充分条件; 当系统节点的扩张后, 在原有自适应控制器不能使系统同步稳定的条件下, 设计脉冲控制器, 利用牵制控制原理使系统达到稳定同步. 所设计的自适应动态反馈控制器在保证系统的渐近稳定条件下使系 统性能指标满足一定的要求. 最后给出一个数值仿真说明其有效性.     

多变量系统的基于高频增益矩阵分解的直接型模型参考自适应控制

针对相对阶为2的多变量系统,利用高频增益矩阵分解,在适当的假设下,建立了新的参数模型,设计出具有未规范化自适应律的直接型模型参考自适应控制器,保证了闭环系统所有信号的有界性和跟踪误差的收敛性.仿真实例验证了控制方案的有效性.     

基于矩阵分解的多变量鲁棒自适应反推控制

针对含有输入未建模动态的一类MIMO系统,在高频增益矩阵的顺序主子式的符号已知的前提下,给出了多变量自适应反推控制器的设计.严格地证明了对一类未建模动态,闭环适应系统的所有信号都是全局一致有界的,且输出渐近收敛于零.     

基于特征结构配置和直接自适应的侧向飞行控制律设计

将特征结构配置与直接自适应理论相结合对飞机侧向飞行控制律进行设计.首先介绍了特征结构配置的原理和反馈增益矩阵的算法实现,在此基础上结合直接自适应方法对闭环系统进行鲁棒控制器设计.该控制策略以特征结构配置作为内环控制器,自适应控制器作为外环控制器,使系统获得较好的动态性能和较强的鲁棒性.某飞机的侧向飞行控制律设计仿真结果表明系统获得了良好的动态性能,并且有效抑制了内部参数摄动对系统的不良影响.     

一类非线性不确定时滞系统的混杂状态反馈H∞鲁棒控制

利用混杂状态反馈控制策略研究一类非线性不确定时滞系统的H∞鲁棒控制问题.假定在给定的控制器集合中有有限个备选的状态反馈控制器,并且每个单一的连续控制器都不能使系统具有鲁棒H∞性能.当控制器的增益矩阵已知时,基于单Lyapunov函数技术和凸组合条件给出控制器切换方案以确保非线性不确定时滞系统具有鲁棒H∞性能.当控制器的增益矩阵未知时,使用多Lyapunov函数技术得到了问题可解的另一个充分条件,同时还给出了混杂状态反馈H∞控制器的设计.     

一类时滞大系统的分散弹性控制器设计:自适应方法

研究了一类时滞大系统的分散弹性自适应控制器的设计问题.当控制器增益摄动范数 有界但上界未知时,采用自适应方法,给出了系统一致有界稳定的条件和控制器的设计方案,且 该控制器具有在线调整的功能.并给出了特例情况下,即控制器增益摄动范数有界且上界已知 时,闭环系统可镇定的充分条件和相应弹性控制器的设计方案.数值算例说明了设计的可行性 和有效性.     

一类时滞线性系统的鲁棒非脆弱控制器设计

基于线性矩阵不等式（LMI）方法,研究一类时滞线性系统的鲁棒非脆弱控制器的设计问题.在假定控制器增益扰动范数有界的前提下,对不确定时滞线性系统设计了鲁棒非脆弱状态反馈控制器,同时以一个LMI的形式给出了状态反馈控制器存在的充分条件,而且该LMI是与时滞相关的,因而具有较小的保守性.数值例子证明了该设计方法的有效性和可行性.     

模糊非脆弱动态输出反馈H∞控制器设计

本文考虑受有限字长影响的离散时间模糊系统的非脆弱H∞控制问题. 假定所设计的控制器具有加性区间型增益变量, 该增益变量反映了控制器数字执行过程中有限字长的影响. 区间型增益变量导致控制器设计具有数值计算问题, 而模糊性质的引入进一步增加了控制器设计的复杂性, 使问题变得更具有挑战性. 本文采用结构的顶点分离器方法来解决数值计算问题, 从而给出一个基于线性矩阵不等式的模糊非脆弱H∞控制器设计的两步算法. 该设计结果保证闭环系统渐进稳定并具有指定的H∞性能指标. 最后给出一个数值例子验证所提出方法的有效性.     

Adaptive control of linear multivariable systems with high frequency gain matrix hurwitz

A new adaptive control scheme is proposed for multivariable model reference adaptive control （MRAC） systems based on the nonlinear backstepplng approach with vector form. The assumption on a priori knowledge of the high frequency gain matrix in existing results is relaxed and the new required condition for the high frequency gain matrix can be easily checked for certain plants so that the proposed method is widely applicable. This control scheme guarantees the global stability of the closed-loop systems and the tracking error can be arbitrary small. The simulation result for an application example shows the validity of the proposed nonlinear adaptive scheme.     

Adaptive control of linear multivariable systems with high frequency gain matrix hurwitz

A new adaptive control scheme is proposed for multivariable model reference adaptive control(MRAC) systems based on the nonlinear backstepping approach with vector form.The assumption on a priori knowledge of the high frequency gain matrix in existing results is relaxed and the new required condition for the high frequency gain matrix can be easily checked for certain plants so that the proposed method is widely applicable.This control scheme guarantees the global stability of the closed-loop systems and the tracking error can be arbitrary small.The simulation result for an application example shows the validity of the proposed nonlinear adaptive scheme.     

A learning approach to tracking in mechanical systems with friction

This paper describes a novel learning control scheme for tracking periodic trajectories in mechanical systems with friction. It is based on the fact that the solution of the closed-loop system tends to be periodic in steady state. When the closed-loop system reaches the steady state, the proposed learning control scheme updates the control input. By doing this iteratively, the proposed learning control scheme eventually can drive the tracking error to zero. Neither the information of the system mass nor the parametric model for friction is required for successful tracking. In particular the proposed learning control scheme can be implemented at cheap cost on a commercially available microprocessor. Furthermore, its generality is well supported through rigorous convergence analysis     

On the practice of artificial intelligence based predictive control scheme: a case study

This paper describes a novel artificial intelligence based predictive control scheme for the purpose of dealing with so many complicated systems. In the control scheme proposed here, the system has to be first represented through a multi-Takagi-Sugeno-Kang (TSK) fuzzy-based model approach to make an appropriate prediction of the system behavior. Subsequently, a multi-generalized predictive control (GPC) scheme, which is organized based on a number of GPC schemes, is realized in line with the investigated model outcomes, at chosen operating points of the system. In case of the proposed control strategy realization, the investigated multi-GPC scheme is instantly updated to handle the system by activating the best control scheme through a new GPC identifier, while the system output is suddenly varied with respect to time. To present the applicability of the proposed control scheme, an industrial tubular heat exchanger system and also a drum-type boiler-turbine system have been chosen to drive through the proposed strategy. In such a case, the simulations are carried out and the corresponding results are compared with those obtained using traditional GPC scheme in addition to nonlinear GPC (NLGPC) scheme, as benchmark approaches, where the acquired results of the proposed control scheme are desirably verified.     

Application of intelligence-based predictive scheme to load-frequency control in a two-area interconnected power system

This paper describes an application of intelligence-based predictive scheme to load-frequency control (LFC) in a two-area interconnected power system. In this investigation, at first, a dynamic model of the present system has to be considered and subsequently an efficient control scheme which is organized based on Takagi-Sugeno-Kang (TSK) fuzzy-based scheme and linear generalized predictive control (LGPC) scheme needs to be developed. In the control scheme proposed, frequency deviation versus load electrical power variation could efficiently be dealt with, at each instant of time. In conclusion, in order to validate the effectiveness of the proposed control scheme, the whole of outcomes are simulated and compared with those obtained using a nonlinear GPC (NLGPC), as a benchmark approach, which is implemented based on the Wiener model of this power system. The validity of the proposed control scheme is tangibly verified in comparison with the previous one.     

IMC-based iterative learning control for batch processes with uncertain time delay

Based on the internal model control (IMC) structure, an iterative learning control (ILC) scheme is proposed for batch processes with model uncertainties including time delay mismatch. An important merit is that the IMC design for the initial run of the proposed control scheme is independent of the subsequent ILC for realization of perfect tracking. Sufficient conditions to guarantee the convergence of ILC are derived. To facilitate the controller design, a unified controller form is proposed for implementation of both IMC and ILC in the proposed control scheme. Robust tuning constraints of the unified controller are derived in terms of the process uncertainties described in a multiplicative form. To deal with process uncertainties, the unified controller can be monotonically tuned to meet the compromise between tracking performance and control system robust stability. Illustrative examples from the recent literature are performed to demonstrate the effectiveness and merits of the proposed control scheme.     

严格反馈系统的事件触发学习控制

本文针对一类严格反馈非线性系统,提出了基于确定学习的事件触发控制方案.首先,在本地控制测试端设计自适应神经网络控制,并在控制过程中实现系统未知动态的知识获取和存储.随后,基于常值权值,设计了新颖的事件触发控制器和事件触发条件.结合李雅普诺夫稳定性分析和非线性脉冲动态系统原理,验证了所提方案能够保证跟踪误差收敛到零的小邻域内以及所有闭环信号是最终一致有界的.此外,本文所提方案采用常值权值代替了估计权值,使得所提方案易于实现,暂态性能好和网络资源占用少.最后,通过对比仿真结果证明了所提方案的有效性.     

轮式移动机器人的数据驱动轨迹跟踪滑模约束控制

针对有输入饱和约束的轮式移动机器人(WMR)的轨迹跟踪问题,提出一种抗饱和无模型自适应积分终端滑模控制方案.该方案基于紧格式动态线性化技术,构建WMR系统的在线数据驱动模型.在积分终端滑模控制器设计过程中,引入动态抗饱和补偿器,以解决WMR系统轨迹跟踪过程中执行器饱和问题.控制器设计仅利用控制系统的输入输出数据,与WMR系统模型信息无关.因此,针对不同类型的WMR系统,该方案均可实现.最后,通过仿真实验将所提出的方法与PID方法的控制效果进行对比,仿真结果表明,所提出的控制算法的跟踪误差更小且响应速度更快.     

考虑驱动系统动态的机械手神经网络控制及应用

针对结构和参数均未知的机械手控制问题, 提出了考虑驱动系统动态的机械手神经网络控制方法, 采用稳定的径向基(Radial basis function, RBF)神经网络辨识机械手未知动态, 而附加的鲁棒控制可以保证存在神经网络的建模误差和外部干扰时系统的稳定性和性能, 并且该方法使机械手闭环系统一致最终有界. 同时开发了基于半实物仿真技术的机械手控制系统, 最后, 将本文方法与经典的PD控制器和自适应控制器在同一机械手平台上进行了实验验证与分析, 实验结果表明该方法具有良好的控制性能.     

Predictive energy management of a 4QT series-parallel hybrid electric bus

This paper presents a novel predictive control scheme for a series-parallel hybrid bus. The proposed scheme uses information from GPS together with a data record of the driving along the bus route to schedule the charging and discharging of the energy storage system. Switching between hybrid and pure electric mode is optimized in a receding horizon scheme based on a prediction model that reflects the uncertainty of the future driving.The benefits of the proposed predictive control scheme are shown by a simulation study on measured driving data along a bus route. The simulations show that the predictive control scheme achieves both lower fuel consumption and better control of the energy storage system than can be achieved with a non-predictive controller.