基于即时学习的MIMO系统滑模预测控制方法

针对MIMO非线性系统的控制问题,采用数据驱动的控制策略,将具有本质自适应能力的即时学习算法与具有强鲁棒性的滑模预测控制相结合,设计了一种基于即时学习的滑模预测（LL-SMPC）控制方法．该方法在在线局部建模的基础上,采用滑模预测控制律求取最优控制量,具有较强的自适应和抗干扰能力,并避免TDiophantine方程的求解,有效减少了计算量．通过仿真研究,验证了算法的有效性．     

约束非线性系统的滑模预测控制方法

将预测控制和滑模控制结构起来,提出一种新的非线性模型预测控制方法,通过对系统状态预测得到切换函数预测值,求解约束开环优化求得预测控制律,并将当前时刻的控制作用于对象,下一时刻重复此过程,该方法具有预测控制在线处理约束及滑模控制滑动模态对干扰的不变性的优点,分析了零终端滑模约束模型预测控制的稳定性。     

一种基于预测函数的滑模控制方法

针对一类多输入多输出系统,为了削弱滑模控制引起的抖振问题,提出了一种基于预测函数的滑模控制方法。该方法根据系统实际输出值与参考轨迹的误差设计滑模面,引入预测函数,预测未来时刻的误差以得到未来时刻的滑模面,改进滑模趋近律来设计控制器,并加入约束条件来优化系统性能指标。该方法能保证系统稳定,削弱滑模控制给系统带来的抖振现象,且具有鲁棒性强的优点。最后以倒立摆为例进行了仿真,仿真结果验证了该方法的有效性。     

基于即时学习的高炉炼铁过程数据驱动自适应预测控制

针对高炉炼铁过程,本文提出一种基于即时学习的高炉铁水质量自适应预测控制方法(JITL–APC).该方法的特点是控制器通过k向量近邻(k–VNN)方法搜索数据库中的输入输出(I/O)数据信息,对非线性系统进行局部建模,并在此基础上计算控制律.而且,该方法中引入了工业异常数据处理机制,利用JITL学习子集中的平均数据项,对异常数据项进行填补或替换,从而消除异常数据对控制系统的影响.此外,本文提出一种JITL模型保留策略(MRS),避免由于数据库中相似数据样本不足导致的局部模型严重失配,并通过实时收集I/O数据更新数据库,使控制器自适应不同的工况条件, MRS还可以有效抑制噪声干扰的影响,从而提高控制系统的稳定性.最后,基于某大型钢铁厂2#高炉的数值仿真实验,充分验证了该方法的有效性.     

即时学习算法在非线性系统迭代学习控制中的应用

运用即时学习算法来解决一类非线性系统的迭代学习控制初值问题。对于任何类型的迭代学习控制算法，即时学习算法都能有效地估计初始控制量，减小了初始输出误差，加快了算法的收敛速度，使得经过有限次迭代后系统输出能严格跟踪理想信号。对机器人系统的仿真结果表明了该方法的有效性。     

基于即时学习的非线性系统优化控制

基于数据驱动机制的逆控制是一种非线性系统控制方法,关键问题在于局部逆控制模型的准确性,但尚无校验机制来保证其能否产生期望的输出．为此,提出一种k-VNN即时学习算法,提高了逆控制模型的建模精度．将该算法与性能指标优化策略相结合,在线修正逆控制模型顶估的系统控制量。可得到系统的一步最优控制量。实现非线性系统的跟踪控制,为提高控制系统的泛化能力,提出一种数据库数据更新策略．仿真结果表明了所提出方法的有效性．     

实现滑模变结构控制直流调速系统微机系统

本文提出一种用单片机实现直流电机调速系统滑模变结构控制实现方案。     

Pendubot的一种分层滑模控制方法

针对Pendubot这类二阶欠驱动系统提出了一种分层滑模控制方法.该方法将系统状态分成两个子系统,分别构造滑动平面,采用Lyapunov方法求取总控制量,该控制量可以实现Pendubot的摆起控制,当系统接近平衡位置附近时,双层滑模控制器退化成单层控制器,这样又保证了Pendubot能够稳定在最终的平衡位置上.从理论上证明了各层滑动平面的渐近稳定性,并且通过仿真实验验证了该方法的有效性以及该控制器对各类扰动的自适应性.     

一种调整多面体描述系统的非线性预测控制方法

常用的非线性预测控制算法先构造合适的多面体描述系统包裹原非线性系统,通过研究多面体描述系统的各个顶点系统的稳定性来保证原非线性系统的稳定性。在以前研究中,多面体描述系统保持不变,但事实上多面体描述系统与终端约束集密切相关。设计了一种根据终端约束椭圆集调整多面体描述系统的非线性预测控制方法,该方法能够有效地减小系统的控制性能指标,同时控制过程中的终端不变椭圆集及其内的反馈控制律具有离线指导作用。     

Sliding mode predictive control: A survey

This paper reviews the development and application of sliding mode predictive control (SMPC) in a tutorial manner. Two core design paradigms are revealed in the combination of sliding mode control (SMC) and model predictive control (MPC). In the first case, MPC is used in the reaching phase to ensure a sliding mode is attained. In the second case, MPC is used to solve the existence problem and define the required performance in the sliding mode. The two approaches are discussed in detail from the perspectives of both theory and application. Finally, some future challenges and opportunities in the area of SMPC are summarized.     

Separated design of robust model predictive control for LPV systems with periodic disturbance

To eliminate the steady-state error of systems with periodic disturbance, the repetitive control (RC) is a useful approach. For practical applications, the controller is designed to both steer system output to a given set-point (or track a given reference signal) and reject periodic disturbance. The learning procedure of RC and the control action to steer system output to a set-point may influence each other and prolong the convergence time RC. In order to reduce this interaction, this paper proposes a separated design approach. A linear parameter varying (LPV) system is considered. A repetitive predictive control (RPC) and a robust model predictive control (RMPC) are separately designed, respectively, corresponding to reject the periodic disturbance and steer system output to the set-point. The convergence of the proposed RPC sub-controller is derived. The numerical examples show that the proposed design is effective.     

Sliding mode learning control of non‐minimum phase nonlinear systems

In this paper, a novel robust sliding mode learning control scheme is developed for a class of non‐minimum phase nonlinear systems with uncertain dynamics. It is shown that the proposed sliding mode learning controller, designed based on the most recent information of the stability status of the closed‐loop system, is capable of adjusting the control signal to drive the sliding variable to reach the sliding surface in finite time and remain on it thereafter. The closed‐loop dynamics including both observable and non‐observable ones are then guaranteed to asymptotically converge to zero in the sliding mode. The developed learning control method possesses many appealing features including chattering‐free characteristic, strong robustness with respect to uncertainties. More importantly, the prior information of the bounds of uncertainties is no longer required in designing the controller. Numerical examples are presented in comparison with the conventional sliding mode control and backstepping control approaches to illustrate the effectiveness of the proposed control methodology. Copyright © 2015 John Wiley & Sons, Ltd.     

Output feedback model predictive control for LPV systems based on quasi-min–max algorithm

This paper proposes a robust output feedback model predictive control (MPC) scheme for linear parameter varying (LPV) systems based on a quasi-min–max algorithm. This approach involves an off-line design of a robust state observer for LPV systems using linear matrix inequality (LMI) and an on-line robust output feedback MPC algorithm using the estimated state. The proposed MPC method for LPV systems is applicable for a variety of systems with constraints and guarantees the robust stability of the output feedback systems. A numerical example for an LPV system subject to input constraints is given to demonstrate its effectiveness.     

基于分段Lyapunov 函数的Hammerstein-Wiener 非线性预测控制

针对输入和输出受约束的Hammerstein-Wiener型非线性系统,建立T-S模糊模型,并提出一种基于分段Lyapunov函数的非线性预测控制算法.通过构造分段二次Lyapunov函数,分析非线性系统的稳定性,降低普通二次Lyapunov函数的保守性;通过离线设计分段反馈控制律,在线实施符合条件的反馈控制律,极大程度地提高了在线计算效率.仿真结果验证了该方法的有效性.     

基于即时学习的非线性系统自适应PID控制

当使用先进策略整定PID控制器参数时,往往要依赖于系统所辨识的模型,而模型的精度与优化算法的计算效率直接影响到系统的控制效果.本文利用即时学习算法的本质自适应特点(建模数据在时间与空间上相邻性),来提高辨识模型的精度,并基于广义最小方差的性能指标,用等价多项式的方法,推导出PID形式的控制律,从而避免其他优化算法带来的计算量,提高了控制精度与计算效率.仿真结果验证了该方法的有效性.     

Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems

A design of adaptive model predictive control (MPC) based on adaptive control Lyapunov function (aCLF) is proposed in this article for nonlinear continuous systems with part of its dynamics being unknown at the starting time. Specifically, to guarantee the convergence of the closed-loop system with online predictive model updating, a stability constraint is designed. It limits the aCLF of the system under the MPC to be less than that under an online updated auxiliary adaptive control. The auxiliary adaptive control which implements in a sampling-hold fashion can guarantee the convergence of the controlled system. The sufficient conditions that guarantee the states to be steered to a small region near the equilibrium by the proposed MPC are provided. The calculation of the proposed algorithm does not depend on the model mismatch at the starting time. And it does not require the Lyapunov function of the state of the real system always to be reduced at each time. These provide the potential to improve the performance of the closed-loop system. The effectiveness of the proposed method is illustrated through a chemical process example.     

Sliding mode observers for a class of linear parameter varying systems

In this paper, a new framework for the synthesis of a class of sliding mode observers for affine linear parameter varying (LPV) systems is proposed. The sliding mode observer is synthesized by selecting the design freedom via linear matrix inequalities ( LMIs ). Posing the problem from a small gain perspective allows existing numerical techniques from the literature to be used for the purpose of synthesizing the observer gains. In particular, the framework allows affine parameter‐dependent Lyapunov functions to be considered for analyzing the stability of the state estimation error dynamics, to help reduce design conservatism. Initially a variable structure observer formulation is proposed, but by imposing further constraints on the LMIs, a stable sliding mode is introduced, which can force and maintain the output estimation error to be zero in finite time. The efficacy of the scheme is demonstrated using an LPV model of the short period dynamics of an aircraft and demonstrates simultaneous asymptotic estimation of the states and disturbances.     

网络控制系统的滑模多步预估控制

针对网络控制系统中出现的长时滞、网络诱导噪声和数据包丢失,提出了的滑模多步预估控制器的设计方法.首先对提出的控制器进行了描述,它利用滑模控制的强鲁棒性来克服网络诱导噪声,采用多步预估的办法来处理网络中的时滞和数据包丢失.进而对导出的闭环网络控制系统的稳定性进行了分析.最后对通过网络控制的直流电机,采用所提出的方法设计了控制器,仿真结果验证了方法的有效性.