参数未知的离散系统Q-学习优化状态估计与控制

控制系统的应用中存在状态不能直接测量或测量成本高的实际问题,给模型参数未知的系统完全利用状态数据学习最优控制器带来挑战性难题.为解决这一问题,首先构建具有状态观测器且系统矩阵中存在未知参数的离散线性增广系统,定义性能优化指标;然后基于分离定理、动态规划以及Q-学习方法,给出一种具有未知模型参数的非策略Q-学习算法,并设计近似最优观测器,得到完全利用可测量的系统输出和控制输入数据的非策略Q-学习算法,实现基于观测器状态反馈的系统优化控制策略,该算法的优点在于不要求系统模型参数全部已知,不要求系统状态直接可测,利用可测量数据实现指定性能指标的优化;最后,通过仿真实验验证所提出方法的有效性.     

解析解耦设计在变风量空调系统中的应用

研究空调优化控制问题,变风量空调(VAV)具有多变量、强耦合和非线性系统.变量之间的耦合会直接影响空调系统的性能指标和稳定性.针对变风量空调系统进行建模,模型为三输入三输出的传递函数矩阵.在内模控制系统结构基础上,运用解耦控制的解析设计方法来设计解耦控制器.解耦设计方法可以达到标称系统变量间完全解耦,并且参数可以在线调整,使输出能够紧密跟随系统的动态变化.仿真结果表明,解耦控制器的设计提高了性能,应用效果较好.     

高超声速飞行器非线性鲁棒控制律设计

高超声速飞行器具有模型非线性程度高、耦合程度强、参数不确定性大、抗干扰能力弱等特点,其自主控制具有较大的挑战.论文提出了一种基于鲁棒补偿技术和反馈线性化方法的非线性鲁棒控制方法.文中首先采用反馈线性化的方法对纵向模型进行输入输出线性化,实现速度和高度通道的解耦和非线性模型的线性化.针对得到的线性模型,设计包括标称控制器和鲁棒补偿器的线性控制器.基于极点配置原理,设计标称控制器使标称线性系统具有期望的输入输出特性,利用鲁棒补偿器来抑制参数不确定性和外界扰动对于闭环控制系统的影响.基于小增益定理,证明了闭环控制系统的鲁棒稳定性和鲁棒跟踪性能.相比于非线性回路成形控制方法,仿真结果表明了所设计非线性鲁棒控制算法的有效性和优越性.     

具有有界控制输入线性多变量系统的渐近稳定性

基于连续时间系统向量比较定理,本文针对状态反馈控制器,带有观测器的状态反馈控制器和动态输出反馈控制器三种情况给出了控制输入有界时控制系统闭环渐近稳定性判据。     

基于神经网络的一类非线性系统自适应输出跟踪

针对一类未知非线性系统,提出了一种输出反馈控制方法.首先,在假设系统状态已 知情况下设计状态反馈控制器,实现跟踪性能;然后,在系统状态不完全可测的情况下,通过 设计高增益观测器对系统的状态进行估计,实现输出反馈控制器设计,证明了所设计的输出 反馈控制器可以获得状态反馈控制器的性能.     

输出重定义下的非线性非最小相位系统迭代学习控制

讨论非线性非最小相位系统实现完全跟踪的迭代学习控制方法, 适于在有限作业区间上重复运行的受控系统. 在控制器设计时, 通过输出重定义以使非最小相位系统的零动态变成渐近稳定特性. 分别采用部分限幅和完全限幅两种学习算法设计控制器, 理论分析表明两种算法能够保证学习系统中所有变量的有界性和跟踪误差在整个作业区间上渐近收敛于零. 数值仿真验证了两种迭代学习控制系统的跟踪性能.     

双输入双输出时滞过程解耦Smith 控制

针对实际工业过程控制中常见的双输入双输出时滞过程,提出基于Smith预估补偿控制的反向解耦控制器设计方法。通过对系统解耦后的对象采用内模控制思想设计Smith预估控制器,并讨论控制系统对象为一阶加纯滞后（FOPDT）模型下其控制器的实现方法和系统鲁棒稳定性分析。结合实际过程中的模型实例,该解耦控制方案使得系统各路输出响应完全解耦并具有较好的标称性能和鲁棒性。仿真实例验证了该方案的有效性和优越性。     

严格反馈非线性时变系统的迭代学习控制

针对一类含未知时变参数的严格反馈非线性系统, 提出一种实现有限作业区间轨迹跟踪控制的迭代学习算法. 基于Lyapunov-like方法设计控制器, 回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求. 以反推设计(Backstepping)方法设计控制器, 为使得虚拟控制项可导, 引入一级数收敛序列; 将时变参数展开为有限项多项式形式, 在控制器设计中采取双曲正切函数处理余项对于系统跟踪性能的影响. 理论分析表明, 闭环系统所有信号有界, 并能够实现系统输出完全收敛于理想轨迹.     

一种混合神经网络飞行控制方法研究

讨论了一种基于神经网络控制的飞行控制方法。针对复杂非线性系统难以建立精确模型的特点,利用神经网络的任意非线性逼近能力进行控制器设计,首先应用神经网络在线辨识对象逆模型,进行控制系统反馈线性化;接着利用circle theorem（圆定理）设计线性PID鲁棒控制器,控制系统输出跟随系统输入,然后应用神经网路自适应逆方法设计混合控制器,最后以F-8飞机纵向飞行控制模态为研究对象进行仿真。仿真结果表明,该控制方法具有较强的自适应和抗干扰能力。     

压电加筋壁板结构的多模态自抗扰振动控制

针对压电加筋壁板结构多模态主动控制时存在振动模型和外界干扰难以确定等问题, 提出一种不依赖结构数学模型的多模态自抗扰振动控制方法. 首先,采用多回路的扩张状态观测器实时估计其他模态的输出叠加、输入耦合、高次谐波以及外界激励等组成的集总干扰, 并将估计值通过前馈补偿的方式消除干扰对整个控制系统的影响. 然后, 针对每个控制模态设计独立的PD反馈控制器. 为了提高整个控制系统的振动抑制性能, 结合多模态振动控制的特点, 引入一种具有实际意义的性能指标函数. 并基于此性能函数, 提出基于logistic映射的自抗扰振动控制器参数自动优化方法. 最后, 利用dSPACE半实物仿真平台, 搭建了四面固支壁板结构的压电振动控制实验系统.最后, 多模态干扰激励的实验结果表明了所提的多模态自抗扰振动主动控制方法的有效性.     

无线网络环境下数据驱动混合选别浓密过程双率控制方法

无线网络环境下赤铁矿混合选别浓密过程控制问题是以底流矿浆泵频率为内环输入,以底流矿浆流量为内环输出外环输入,以底流矿浆浓度为外环输出的非线性串级工业过程控制问题.其外环反馈回路存在丢包,且模型参数难以辨识,故本文利用工业运行过程的在线数据,设计不依赖模型参数的跟踪控制器.首先,利用浓密过程运行在工作点附近的特点进行线性化,对流量过程设计Q-学习控制器,保证流量过程能够跟踪给定的流量设定值;然后采用提升技术,得到统一时间尺度的以底流矿浆流量设定值为输入,以底流矿浆浓度为输出的被控对象;最后,考虑到在无线网络环境下浓度过程存在反馈丢包,当前的状态可能无法获得,故采用史密斯预估器的思想,利用历史的数据估计系统当前的状态,设计丢包Q-学习设定值控制器为流量过程提供最优设定值.通过仿真实验验证所提算法的有效性.     

Robust Feedback Control for a Class of Uncertain MIMO Nonlinear Systems

In this paper, a continuous feedback tracking controller is developed for a class of high-order multi-input multi-output (MIMO) nonlinear systems with an input gain matrix that has nonzero leading principal minors but can be nonsymmetric. Under the mild assumption that the signs of the leading minors of the control input gain matrix are known, the controller yields locally uniformly ultimately bounded (UUB) tracking while compensating for unstructured uncertainty in both the drift vector and the input matrix. First, a full-state feedback controller is designed based on limited assumptions on the structure of the system nonlinearities, and the singularity-free controller is proven to yield locally UUB tracking through a Lyapunov-based analysis. Then, it is shown that an output feedback control can be designed based on a high-gain observer. Simulation results are provided to illustrate the performance of the proposed control algorithm.     

Robust control of a family of uncertain nonminimum‐phase systems via continuous‐time and sampled‐data output feedback

The problem of global robust stabilization is studied by both continuous‐time and sampled‐data output feedback for a family of nonminimum‐phase nonlinear systems with uncertainty. The uncertain nonlinear system considered in this paper has an interconnect structure consisting of a driving system and a possibly unstable zero dynamics with uncertainty, ie, the uncertain driven system. Under a linear growth condition on the uncertain zero dynamics and a Lipschitz condition on the driving system, we show that it is possible to globally robustly stabilize the family of uncertain nonminimum‐phase systems by a single continuous‐time or a sampled‐data output feedback controller. The sampled‐data output feedback controller is designed by using the emulated versions of a continuous‐time observer and a state feedback controller, ie, by holding the input/output signals constant over each sampling interval. The design of either continuous‐time or sampled‐data output compensator uses only the information of the nominal system of the uncertain controlled plant. In the case of sampled‐data control, global robust stability of the hybrid closed‐loop system with uncertainty is established by means of a feedback domination method together with the robustness of the nominal closed‐loop system if the sampling time is small enough.     

基于状态观测器的直接约束鲁棒预测控制

针对多包描述线性离散不确定系统,提出一种在系统状态不可测时的直接约束鲁棒预测控制算法.将控制器与观测器综合设计,利用观测状态直接构造性能指标,通过求解无穷时域性能指标的最小最大优化问题,得到系统的最优状态反馈控制律.采用参数依赖Lyapunov函数,在满足输入和状态约束的情况下保证闭环系统稳定.仿真结果验证了算法的有效性.     

Robust MPC for systems with output feedback and input saturation

In this work, it is proposed an MPC control algorithm with proved robust stability for systems with model uncertainty and output feedback. It is assumed that the operating strategy is such that system inputs may become saturated at transient or steady state. The developed strategy aims at the case in which the controller performs in the output-tracking scheme following an optimal set point that is provided by an upper optimization layer of the plant control structure. In this case, the optimal operating point usually lies at the boundary of the region where the input is defined. Assuming that the system remains stabilizable in the presence of input saturation, the design of the robust controller is performed off-line and an on-line implementation strategy is proposed. At each sampling step, a sub optimal control law is obtained by combining control configurations that correspond to particular subsets of available manipulated inputs. Stability of the closed-loop system is forced by considering in the off-line step of the controller design, a state contracting restriction for the closed-loop system. To produce an offset free controller and to attend the case of unknown steady state, the method is developed for a state-space model in the incremental form. The method is illustrated with simulation examples extracted from the process industry.     

离散时间部分状态反馈模型参考自适应控制

本文针对线性不确定性系统, 给出了部分状态反馈直接模型参考自适应控制设计方案以及详细的系统稳 定性、输出跟踪性能分析. 控制器设计基于降维观测器和参数化方法. 此方案采用反馈控制, 反馈信号不仅仅依赖 全状态信息或者输出信号, 而是任意不超过系统维数的可测信号. 因此, 部分状态反馈控制是包含状态反馈、输出 反馈控制的新的控制方案, 缓解了状态反馈对状态信息的限制, 降低了输出反馈控制结构的复杂性. 通过引入辅助 信号, 本文证明了输出匹配条件的存在性、所有闭环系统信号的有界性以及渐近输出跟踪性能. 仿真结果验证了该 方案的有效性.     

数据驱动的保证收敛速率最优输出调节

针对具有外部系统扰动的线性离散时间系统的输出调节问题,提出了可保证收敛速率的数据驱动最优输出调节方法,包括状态可在线测量系统的基于状态反馈的算法,与状态不可在线测量系统的基于输出反馈的算法.首先,该问题被分解为输出调节方程求解问题与反馈控制律设计问题,基于输出调节方程的解,通过引入收敛速率参数,建立了可保证收敛速率的最...     

Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

Integral sliding mode based optimal composite nonlinear feedback control for a class of systems

This paper presents an optimization method of designing the integral sliding mode （ISM） based composite nonlinear feedback （CNF） controller for a class of low order linear systems with input saturation. The optimal CNF control is first designed as a nominal control to yield high tracking speed and low overshoot. The selection of all the tuning parameters for the CNF control law is turned into a minimization problem and solved automatically by particle swarm optimization （PSO） algorithm. Subsequently, the discontinuous control law is introduced to reject matched disturbances. Then, the optimal ISM-CNF control law is achieved as the sum of the optimal CNF control law and the discontinuous control law. The effectiveness of the optimal ISM-CNF controller is verified by comparing with a step by step designed one. High tracking performance is achieved by applying the optimal ISM-CNF controller to the tracking control of the micromirror.