非线性主动悬架系统自适应最优控制

针对非线性主动悬架系统多性能指标综合优化问题,提出一类自适应最优控制方法.首先,通过引入一阶低通滤波操作,利用系统输入输出构建结构简单且调节参数少的一类未知非线性动态估计器,在线估计系统未知非线性动态;其次,构建包含乘驾舒适度、悬架行程空间及输入能耗的性能指标函数,采用单层神经网络对最优性能指标函数进行在线逼近,并得到新的哈密尔顿函数;为实现在线求解,构建一类新的基于参数估计误差信息的自适应律,在线更新神经网络权值并计算最优控制律;最后,理论分析闭环系统稳定性和收敛性,并通过专业软件Carsim与Matlab/Simulink搭建的联合仿真平台给出的对比仿真结果,验证所提出方法可有效解决主动悬架系统多目标性能优化控制问题,提升主动悬架系统综合性能.     

一类非自衡对象的最优控制

对模型不确定性和控制能量约束并存时一类非自衡对象的最优控制问题作了探讨．首先基于一类随机模型误差的描述，定义了一个平均意义上的包含跟踪误差和控制能量在内的性能指标；然后通过谱分解极小化该性能指标，导出一个最优的控制律，从而为模型不确定性和控制能量约束并存条件下一类非自衡对象的最优控制问题提供了一种实用的控制方法．仿真结果证明了该方法的有效性．     

网络控制系统的指数稳定最优控制

利用网络控制系统的增广矩阵模型,结合系统的指数稳定性能指标,构建了新的等价性能指标,导出了在该性能指标下有限时间和无限时间状态反馈最优控制律,并分析了系统的均方指数稳定性与几乎处处指数稳定性,最后给出无限时间最优控制的仿真结果,验证了本文结论的正确性。     

δ算子下的网络控制系统最优控制方法

研究网络控制系统的随机最优控制问题,提出了针对随机时延的网络控制系统最优控制律和二次型性能指标极小的控制律设计方案．在δ算子域内应用动态规划理论．设计网络控制系统的最优状态反馈和输出反馈控制律,得到的线性二次型高斯控制器可对系统中的随机长时延进行动态补偿．最后通过实例仿真验证了上述最优控制方案的可行性和有效性．     

基于数据的网络化系统最优控制

针对网络介入导致的系统动态复杂性增加及建模困难等问题,提出了基于数据驱动控制的思想,独立于系统模型设计控制器。利用在线获取的系统输入/输出当前数据和历史数据,分别构造系统的输入数据矩阵和输出数据矩阵,建立两者之间的线性关系,获得当前采样时刻系统的Markov参数,将该参数代入到Markov参数形式表示的Riccati方程解中,获取最优控制增益。同时利用输入与输出之间的线性关系,构造一个控制器状态观测器,利用估计的控制器状态参与计算最优控制律。最后,在Truetime1.5和Matlab仿真平台上验证了提出的控制器是有效的。     

非线性相似组合大系统最优控制的逐次逼近过程

研究一类仿射非线性相似组合大系统关于二次型性能指标的最优控制问题．首先通过模型简化,将非线性相似组合大系统化为若干个准解耦的子系统;然后利用非线性系统最优控制的逐次逼近设计方法,将求解高阶强耦合的非线性两点边值问题简化为求解一族解耦的线性两点边值问题序列．该线性两点边值问题序列的解一致收敛于非线性相似组合大系统的最优控制,得到的最优控制律由线性最优控制的解析项与非线性补偿序列的极限项组成．通过截取最优控制非线性补偿序列的有限次逼近值．得到了非线性组合大系统的次优控制律．     

输入饱和的双积分系统的复合时间最优控制

针对典型的有输入饱和的双积分环节或系统的时间最优控制问题,建立了双积分环节的传递函数和状态空间方程两种数学模型,设计双积分环节的闭环时间最优控制律;对时间最优控制在系统存在干扰和不确定性存在条件下出现的振颤现象进行分析;基于对振颤问题的分析,提出一种对时间最优控制的改进,即一种复合控制方法,当输入作用时,系统先由时间最优控制律控制,当误差达到预定值限,控制律由时间最优控制律切换到另一种线性控制律。采用了比例微分控制律,来解决时间最优控制的振颤问题,响应时间达到最优,并解决振颤问题。     

基于动态补偿的矩形广义系统线性二次最优控制

考虑了基于动态补偿的矩形广义系统线性二次最优控制问题. 首先给出具有适当动态阶的补偿器,使得闭环系统正则、稳定、无脉冲(称为容许), 而且相关的矩阵不等式和Lyapunov方程解存在. 进一步二次性能指标可写成一个与该解和系统初值相关的表达式. 为了求解系统的最优控制问题, 将该Lyapunov方程转化为一个双线性矩阵不等式, 并给出了相应的路径跟踪算法以最小化二次性能指标, 进而得到最优补偿器. 最后, 通过数值算例说明本文方法的有效性和可行性.     

基于ADP算法的带时滞及饱和的非线性系统最优控制

针对控制时滞及带饱和的一类离散时间非线性系统的最优控制问题,通过重构性能指标函数和对应的系统变换,处理了性能指标函数中的控制耦合项;继而引入一个合适的泛函,解决了控制带饱和问题.给出了一个新的性能指标函数,利用迭代自适应动态规划(ADP)算法获得最优控制.为实现该算法,采用神经网络逼近函数来求解最优控制问题.仿真结果验证了方法的有效性.     

受扰线性离散系统的前馈2反馈最优控制

研究具有已知动态特性但未知初始条件的持续外界扰动的线性离散系统最优控制问题。给出了前馈一反馈最优控制律的存在唯一性条件，并提出了最优控制律的设计算法．通过降维扰动观测器解决了前馈一反馈最优控制律的物理不可实现问题．对近海结构物振动控制的实例仿真表明，该设计算法易于实现，在抑制外部持续扰动和鲁棒性方面优于经典的状态反馈最优控制。     

Robust and optimal attitude control of spacecraft with disturbances

In this paper, a robust and optimal attitude control design that uses the Euler angles and angular velocities feedback is presented for regulation of spacecraft with disturbances. In the control design, it is assumed that the disturbance signal has the information of the system state. In addition, it is assumed that the disturbance signal tries to maximise the same performance index that the control input tries to minimise. After proposing a robust attitude control law that can stabilise the complete attitude motion of spacecraft with disturbances, the optimal attitude control problem of spacecraft is formulated as the optimal game-theoretic problem. Then it is shown that the proposed robust attitude control law is the optimal solution of the optimal game-theoretic problem. The stability of the closed-loop system for the proposed robust and optimal control law is proven by the LaSalle invariance principle. The theoretical results presented in this paper are illustrated by a numerical example.     

Constrained data-driven optimal iterative learning control

A constrained optimal ILC for a class of nonlinear and non-affine systems, without requiring any explicit model information except for the input and output data, is proposed in this work. In order to address the nonlinearities, an iterative dynamic linearization method without omitting any information of the original plant is introduced in the iteration direction. The derived linearized data model is equivalent to the original nonlinear system and reflects the real-time dynamics of the controlled plant, rather than a static approximate model. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear matrix inequality, a novel constrained data-driven optimal ILC is developed by minimizing a predesigned objective function. The optimal learning gain is unfixed and updated iteratively according to the input and output measurements, which enhances the flexibility regarding modifications and expansions of the controlled plant. The results are further extended to the point-to-point control tasks where the exact tracking performance is required only at certain points and a constrained data-driven optimal point-to-point ILC is proposed by only utilizing the error measurements at the specified points only.     

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.     

非线性系统的输入多采样率模糊优化控制

基于多采样率数字控制理论,讨论了非线性连续被控对象和输入多采样率模糊控制器的设计问题.提出用线性矩阵不等式凸优化技术构建非线性系统的输入多采样率T-S模糊模型,并相应地研究了基于优化区域极点配置的PDC状态反馈控制.通过解代数Riccati方程得到控制器的参数,给出了优化数字控制器的设计算法和闭环系统的稳定性条件.计算机仿真表明了所提出方法的有效性.     

MPC for tracking with optimal closed-loop performance

In the recent paper [Limon, D., Alvarado, I., Alamo, T., & Camacho, E.F. (2008). MPC for tracking of piece-wise constant references for constrained linear systems. Automatica, 44, 2382-2387], a novel predictive control technique for tracking changing target operating points has been proposed. Asymptotic stability of any admissible equilibrium point is achieved by adding an artificial steady state and input as decision variables, specializing the terminal conditions and adding an offset cost function to the functional.In this paper, the closed-loop performance of this controller is studied and it is demonstrated that the offset cost function plays an important role in the performance of the model predictive control (MPC) for tracking. Firstly, the controller formulation has been enhanced by considering a convex, positive definite and subdifferential function as the offset cost function. Then it is demonstrated that this formulation ensures convergence to an equilibrium point which minimizes the offset cost function. Thus, in case of target operation points which are not reachable steady states or inputs for the constrained system, the proposed control law steers the system to an admissible steady state (different to the target) which is optimal with relation to the offset cost function. Therefore, the offset cost function plays the role of a steady-state target optimizer which is built into the controller. On the other hand, optimal performance of the MPC for tracking is studied and it is demonstrated that under some conditions on both the offset and the terminal cost functions optimal closed-loop performance is locally achieved.     

Performance deterioration of optimal regulators incorporating state estimators

In this paper, the performance deterioration of optimal regulators caused by the introduction of deterministic state-estimators such as Kalman estimators and Luenberger observers is investigated. It is shown that, in the case of Kalman estimators, the performance deterioration cannot be made arbitrarily small unless the optimal control law can be implemented by direct output feedback. In the case of Luenberger observers with minimum order, a necessary and sufficient condition for the performance deterioration to be made arbitrarily small is derived.     

Application of stochastic optimal control to a hydrofoil boat problem

In this paper the solution of a stochastic optimal control problem described by linear equations of motion and a nonquadratic performance index is presented. The theory is then applied to the dynamics of a single-foil and a hydrofoil boat flying on rough water. The random disturbances caused by sea waves are represented as the response of an auxiliary system to a white noise input. The control objective is formulated as an integral performance index containing a quadratic acceleration term and a nonquadratic term of the submergence deviation of the foil from calm water submergence. The stochastic version of the maximum principle is used in the formulation of a feedback control law. The Riccati equations and the feedback gains associated with a nonquadratic performance index are non-linear functions of the state and auxiliary state variables. These equations are integrated forward with the state equations for the steady-state solution of the problem. The controller for a nonquadratic performance index contains computing elements which perform the integration of the Riccati equations to generate the instantaneous values of the feedback gains. The effect of a nonquadratic penalty on the submergence deviation and the effect of a nonquadratic control penalty on the response of the system are investigated. A comparison between an optimal nonlinear control law and a suboptimal linear control law is presented.     

Minimum-energy control for time-varying systems with multiple state and input delays

This paper considers the minimum-energy control problem for a class of time-varying systems with multiple state and input delays. First, a state transform matrix is presented. By using the transform matrix, a system with multiple state and input delays is converted to a formal equivalent one without delay. Then, the optimal problem of the novel system is solved by using the maximum principle. Analytical expressions of the optimal control law and optimal performance are given by two formulas with respect to the state transform matrix. At last, an algorithm is given to solve the analytical expression of the state transform matrix. Simulation results show that the design algorithm is efficient and easy to implement.     

基于模糊控制的线性最优励磁控制系统权矩阵的优化选取

传统线性最优励磁控制系统的加权矩阵,通常是根据正常运行工况下系统对状态变量的约束程度选取的,因此在平衡点附近控制系统具有较好的动态品质,而当系统受到的扰动过大或者过小时,调节性能会变差.模糊控制器可以跟踪系统运行工况,依据在线插值法所细化的规则库输出目标反馈增益矩阵,进而利用灵敏度分析综合考虑目标反馈增益的物理可实现性与权值对动态性能的影响,通过迭代求出优化权矩阵.仿真结果表明基于模糊控制的权矩阵优化选取,使线性最优励磁控制系统表现出更加优良的动态品质.