时滞系统的状态预测观测器及预测控制器设计

研究基于状态空间模型的时滞控制系统的状态预测观测器及最优预测控制器的设计问题。针对控制项含有时滞的系统，设计一种全维状态预测观测器，并将其用于时滞控制系统的最优状态预测反馈控制。通过该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外，从而使其优化控制规律完全可按无时滞系统进行设计。所给出的性能指标计算公式表明，该预测控制器关于二次型性能指标是次优的。     

基于观测器的不确定广义时滞系统鲁棒预测控制

针对一类不确定广义时滞系统,讨论了其基于观测器的鲁棒预测控制问题,给出了系统观测器型预测控制器的设计方法.通过构造带有误差项的Lyapunov函数,应用线性矩阵不等式,将无穷时域二次性能指标"min-max"优化问题转化为凸优化问题,得到了鲁棒预测控制器存在的充分条件和显式表达式.证明了优化问题在初始时刻的可行解能保证广义闭环系统渐近稳定且正则无脉冲.仿真实例验证了所提出方法的有效性.     

具有执行器饱和特性的不确定系统预测控制器设计

针对具有执行器饱和特征的不确定系统,提出了一种带有状态观测器的新型预测控制器设计方法.该方法在滚动优化的每一步,采用带有饱和特性的反馈控制结构得到一个最优控制律.使无穷时域性能指标最小.考虑在状态不完全已知的情况下,设计了带有状态观测器的预测控制器,并通过观测器参数调整使闭环系统渐近稳定.通过仿真实验验证了所设计控制器的有效性.     

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.     

基于观测器的不确定广义时滞系统鲁棒预测控制

针对一类不确定广义时滞系统,讨论了其基于观测器的鲁棒预测控制问题,给出了系统观测器型预测控制器的设计方法.通过构造带有误差项的Lyapunov函数,应用线性矩阵不等式,将无穷时域二次性能指标"min-max"优化问题转化为凸优化问题,得到了鲁棒预测控制器存在的充分条件和显式表达式.证明了优化问题在初始时刻的可行解能保证广义闭环系统渐近稳定且正则无脉冲.仿真实例验证了所提出方法的有效性.

     

时滞纳米运动系统的多速率采样预测自抗扰控制

电容型纳米位置传感器在纳米伺服系统中得到了越来越多的应用.这类超高精度模拟传感器用于反馈信号时因较长的模数转换时间将带来明显的测量时滞.而这类数字纳米伺服系统也因硬件使其采样频率在处理高频干扰时受到带宽限制.本文针对测量时滞和高频干扰的挑战,提出一种带采样预测功能的多速率自抗扰控制器设计方法.首先建立了电容式位移传感器的纳米运动平台带有时滞的动力学模型.其次,基于该模型设计多率采样预测线性扩张状态观测器和多率反馈控制器.通过设计预测型观测器,适当选取观测器增益,消除时滞对状态观测的影响.另外,将输出预估器加入预测扩张状态观测器中重构采样点间系统输出值,从而在时滞系统中更好地估计和消除高频干扰,并给出了系统的稳定性分析.最后通过压电驱动纳米运动平台的实时控制实验验证本文提出控制器的有效性.     

短时延网络控制系统的鲁棒H 2 öH ∞状态观测器设计

针对受白噪声干扰的短时延网络控制系统,将随机时延对系统的影响转化为未知有界不确定项,在不改变闭环系统结构的前提下,利用鲁棒控制理论提出了系统的H2/H∞状态观测器设计方法;证明了所设计状态观测器估计输出对随机时延引起的不确定项的H∞鲁棒性,给出了估计输出对白噪声干扰的鲁棒H2性能指标的求解方法和观测器增益阵的确定方法.最后通过仿真实例证实了所设计状态观测器的有效性和鲁棒性.     

具有执行器饱和特性的不确定系统预测控制器设计

针对具有执行器饱和特征的不确定系统,提出了一种带有状态观测器的新型预测控制器设计方法.该方法在滚动优化的每一步,采用带有饱和特性的反馈控制结构得到一个最优控制律,使无穷时域性能指标最小.考虑在状态不完全已知的情况下,设计了带有状态观测器的预测控制器,并通过观测器参数调整使闭环系统渐近稳定.通过仿真实验验证了所设计控制器的有效性.

     

化工不稳定时滞过程鲁棒控制的解析设计

针对化工不稳定时滞过程,提出一种两自由度控制结构.首先基于鲁棒H2最优性能指标,设计给定值跟踪控制器;然后根据系统稳态运行时的抗负载干扰要求,在过程输入与输出端之间设计控制闭环,利用其反馈通道上的扰动观测器抑制负载干扰,通过提出期望的闭环余灵敏度函数的方法,反向确定扰动观测器,同时给出了该控制闭环保证鲁棒稳定性的充要条件.仿真实例验证了该控制结构的优越性.     

不确定输入时滞系统的滑模输出反馈控制

研究了在状态不可测,并且状态参数和控制输入矩阵同时存在不确定性的情况下,不确定输入时滞系统的滑模控制问题.为了有效克服不确定性和时滞对系统稳定性的影响,在状态估计空间选择了一种积分型的切换函数,设计了一种基于状态观测器的滑模控制器,并证明了所设计切换面的可达性.通过李亚普诺夫理论,给出了闭环系统渐近稳定的充分条件.数值仿真验证了算法的有效性.     

Adaptive neural network predictive control for nonlinear pure feedback systems with input delay

This paper presents an adaptive neural control design for nonlinear pure-feedback systems with an input time-delay. Novel state variables and the corresponding transform are introduced, such that the state-feedback control of a pure-feedback system can be viewed as the output-feedback control of a canonical system. An adaptive predictor incorporated with a high-order neural network (HONN) observer is proposed to obtain the future system states predictions, which are used in the control design to circumvent the input delay and nonlinearities. The proposed predictor, observer and controller are all online implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed. The conventional backstepping design and analysis for pure-feedback systems are avoided, which renders the developed scheme simpler in its synthesis and application. Practical guidelines on the control implementation and the parameter design are provided. Simulation on a continuous stirred tank reactor (CSTR) and practical experiments on a three-tank liquid level process control system are included to verify the reliability and effectiveness.     

带有状态和输入时滞的不确定广义系统的鲁棒预测控制

针对一类同时具有状态和输入时滞的不确定广义系统, 研究其鲁棒预测控制问题, 给出了鲁棒预测控制器的综合方法. 运用Lyapunov稳定性理论和线性矩阵不等式方法, 近似求解无穷时域二次性能指标优化问题, 从而得到鲁棒预测控制器存在的充分条件和显式表达式. 证明了优化问题在初始时刻的可行解, 可以保证广义闭环时滞系统是渐近稳定且正则无脉冲的. 仿真算例验证了所提出方法的有效性.     

Observer-based tracking controller design for networked predictive control systems with uncertain Markov delays

This paper is concerned with a tracking controller design problem for discrete-time networked predictive control systems. The control law used here is a combined state-feedback control and integral control. Since not all the states are available in practice, a local Luenberger observer is utilised to estimate the state vector. The measured output and estimated state vector are packed together and transmitted to the tracking controller via a communication channel with a limited capacity. Meanwhile, the control signal is also transmitted through a communication network.Network-induced delays on both links are considered for the signal transmission and modelled by Markov chains. Moreover, it is assumed that the elements in Markov transition matrices are subject to uncertainties. In order to fully compensate for network-induced delays, the controller generates a sequence of control signals which are dependent on each possible delay in the feedforward channel. By taking the augmentation twice, we obtain delay-free stochastic closed-loop systems and the controlled output is chosen as the tracking error. Sufficient conditions are provided for the energy-to-peak performance of the closed-loop systems. The feedback gains of the controller can be derived by solving a minimisation problem. Two examples are illustrated to demonstrate the effectiveness of the proposed design method.     

DoS攻击下网络化控制系统基于观测器的记忆型事件触发预测控制

本文研究了DoS攻击下网络化控制系统记忆型事件触发预测补偿控制问题. 首先, 由于网络带宽资源有限 和系统状态不完全可观测性, 引入了记忆型事件触发函数, 为观测器提供离散事件触发传输方案. 然后, 分析了网络 传输通道上发生的DoS攻击. 结合上述记忆型事件触发方案, 在控制节点设计一类新颖的预测控制算法, 节省网络 带宽资源并主动补偿DoS攻击. 同时, 建立了基于观测器的记忆型事件触发预测控制的闭环系统, 并且分析稳定性. 通过线性矩阵不等式(LMI)和Lyapunov稳定性理论, 建立了控制器、观测器和记忆型事件触发矩阵的联合设计方案, 并验证了该方案的可行性. 仿真结果表明, 该方案结合记忆型事件触发机制可以有效补偿DoS攻击, 节约网络带宽 资源.     

Observer-based approximate optimal tracking control for time-delay systems with external disturbances

This paper proposes a successive approximation design approach of observer-based optimal tracking controllers for time-delay systems with external disturbances. To solve a two-point boundary value problem with time-delay and time-advance terms and obtain the optimal tracking control law, two sequences of vector differential equations are constructed first. Second, the convergence of the sequences of the vector differential equations is proved to guarantee the existence and uniqueness of the control law. Third, a design algorithm of the optimal tracking control law is presented and the physically realisable problem is addressed by designing a disturbance state observer and a reference input state observer. An example of an industrial electric heater is given to demonstrate the efficiency of the proposed approach.     

An improved model predictive control approach based on extended non-minimal state space formulation

This paper presents a new design method of model predictive control (MPC) based on extended non-minimal state space models, in which the measured input and output variables, their past values together with the defined output errors are chosen as the state variables. It shows that this approach does not need the design of an observer to access the state information any more and by augmenting the process model and its objective function to include the changes of the system state variables, the control performances are superior to those of the controller that does not bear this feature. Furthermore, closed-loop transfer function representation of the model predictive control system facilitates the use of frequency response analysis methods for the nominal control performances of the system.     

严格反馈非线性系统的自适应神经网络输出反馈控制

基于非线性反馈函数,文章设计神经网络状态观测器,解决一类非线性系统的输出反馈控制问题.非线性反馈神经网络观测器在系统存在不确定性函数的情况下实时估计系统状态.利用所获得的状态信号,设计了自适应神经网络动态面控制器,同时保证了闭环系统的稳定性和所有信号的有界性.通过调节设计参数的取值能够达到期望的闭环跟踪性能.数值仿真表明,所设计的状态观测器不需要对原系统做状态变换,能够克服输出反馈滑模控制器带来的抖震问题.     

Robust moving horizon estimation based output feedback economic model predictive control

In this work, we develop an economic model predictive control scheme for a class of nonlinear systems with bounded process and measurement noise. In order to achieve fast convergence of the state estimates to the actual system state as well as the robustness of the observer to measurement and process noise, a deterministic (high-gain) observer is first applied for a small time period with continuous output measurements to drive the estimation error to a small value; after this initial small time period, a robust moving horizon estimation scheme is used on-line to provide more accurate and smoother state estimates. In the design of the robust moving horizon estimation scheme, the deterministic observer is used to calculate reference estimates and confidence regions that contain the actual system state. Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates. The output feedback economic model predictive controller is designed via Lyapunov techniques based on state estimates provided by the deterministic observer and the moving horizon estimation scheme. The stability of the closed-loop system is analyzed rigorously and conditions that ensure the closed-loop stability are derived. Extensive simulations based on a chemical process example illustrate the effectiveness of the proposed approach.     

An improved state-space model structure and a corresponding predictive functional control design with improved control performance

Conventional state-space model predictive control requires a state estimator/observer to access the state information for feedback controller design. Its drawbacks are the numerical convergence stability of the observer and closed-loop control performance deterioration with activated plant input/output constraints. The recent direct use of measured input and output variables to formulate a non-minimal state-space (NMSS) model overcomes these problems, but the subsequent controller is too sensitive to model mismatch. In this article, an improved structure of NMSS model that incorporates the output-tracking error is first formulated and then a subsequent predictive functional control design is proposed. The proposed controller is tested on both model match and model mismatch cases for comparison with previous controllers. Results show that control performance is improved. In addition, a linear programming method for constraints dealing and a closed form of transfer function representation of the control system are provided for further insight into the proposed method.     

具有多传输通道系统的网络化预测控制

本文研究了一类具有多传输通道网络化系统的控制问题,基于网络化预测控制方法,提出了一种改进型的分布式预测补偿方式,从而更有效地利用反馈数据来提高控制系统的性能.对闭环网络化预测控制系统进行分析,得到其稳定性条件,特别地,在模型精确已知和多传输通道的时延为定常的情况下,该条件将会退化为本地控制的闭环系统稳定性条件.上述结论的好处是网络化预测控制系统中状态观测器和控制器的设计可以参考本地控制.通过球杆系统算例验证本文所提方法的正确性和有效性.