On the stability of two-step predictive controller based on state observer

For input saturated Hammerstein systems, the two-step predictive control strategy is adopted. The first step calculates the desired intermediate variable applying unconstrained linear modal and predictive control. The second step obtains the real-time control action by solving algebraic equation and desaturation. The case of immeasurable state is considered where the observer gain matrix is solved by Sylvester equation. The sufficient closed-loop stability condition is given and the designing and tuning algorithm for the domain of attraction is proposed. The theoretical results are validated by an example.

Nonlinear model predictive control with guaranteed stability based on pseudolinear neural networks

A nonlinear model predictive control problem based on pseudo-linear neural network (PNN) is discussed, in which the second order on-line optimization method is adopted. The recursive computation of Jacobian matrix is investigated. The stability of the closed loop model predictive control system is analyzed based on Lyapunov theory to obtain the sufficient condition for the asymptotical stability of the neural predictive control system. A simulation was carried out for an exothermic first-order reaction in a continuous stirred tank reactor.It is demonstrated that the proposed control strategy is applicable to some of nonlinear systems.

Model predictive control synthesis algorithm based on polytopic terminal region for Hammerstein-Wiener nonlinear systems

An improved model predictive control algorithm is proposed for Hammerstein-Wiener nonlinear systems.The proposed synthesis algorithm contains two parts:offline design the polytopic invariant sets,and online solve the min-max optimization problem.The polytopic invariant set is adopted to replace the traditional ellipsoid invariant set.And the parameter-correlation nonlinear control law is designed to replace the traditional linear control law.Consequently,the terminal region is enlarged and the control effect is improved.Simulation and experiment are used to verify the validity of the wind tunnel flow field control algorithm.

An improved constrained model predictive control approach for Hammerstein-Wiener nonlinear systems

Many industry processes can be described as Hammerstein-Wiener nonlinear systems. In this work, an improved constrained model predictive control algorithm is presented for Hammerstein-Wiener systems. In the new approach, the maximum and minimum of partial derivative for input and output nonlinearities are solved in the neighbourhood of the equilibrium. And several parameter-dependent Lyapunov functions, each one corresponding to a different vertex of polytopic descriptions models, are introduced to analyze the stability of Hammerstein-Wiener systems, but only one Lyapunov function is utilized to analyze system stability like the traditional method. Consequently, the conservation of the traditional quadratic stability is removed, and the terminal regions are enlarged. Simulation and field trial results show that the proposed algorithm is valid. It has higher control precision and shorter blowing time than the traditional approach.

Real-Time nonlinear predictive controller design for drive-by-wire vehicle lateral stability with dynamic boundary conditions

Due to flexible drive-by-wire technology,vehicle stability control can improve handling and lateral stability under extreme conditions.However,this technology can also increase the probability of random transmission delay.This paper proposes a nonlinear model predictive control(NMPC)strategy to improve vehicle stability and compensate for the random time delay.First,by combining the nonlinear dynamic characteristics and driver behavior,we obtain a stable region of the yaw rate and the sideslip angle under complex driving conditions.Second,an NMPC controller is designed to track the reference values in the identified stable region to improve the handling and lateral stability.Finally,the actuator receives the optimized control sequence and compensates for the random time delay of the transmission channel.CarSim/Simulink simulation and hardware-in-the-loop experiment results show that the proposed controller with dynamic boundary conditions can better track the expected value of the yaw rate and suppress the sideslip angle under low adhesion road conditions.

Real‑Time Predictive Control of Path Following to Stabilize Autonomous Electric Vehicles Under Extreme Drive Conditions

A novel real-time predictive control strategy is proposed for path following(PF)and vehicle stability of autonomous electric vehicles under extreme drive conditions.The investigated vehicle configuration is a distributed drive electric vehicle,which allows to independently control the torques of each in-wheel motor(IWM)for superior stability,but bringing control com-plexities.The control-oriented model is established by the Magic Formula tire function and the single-track vehicle model.For PF and direct yaw moment control,the nonlinear model predictive control(NMPC)strategy is developed to minimize PF tracking error and stabilize vehicle,outputting front tires’lateral force and external yaw moment.To mitigate the calcu-lation burdens,the continuation/general minimal residual algorithm is proposed for real-time optimization in NMPC.The relaxation function method is adopted to handle the inequality constraints.To prevent vehicle instability and improve steering capacity,the lateral velocity differential of the vehicle is considered in phase plane analysis,and the novel stable bounds of lateral forces are developed and online applied in the proposed NMPC controller.Additionally,the Lyapunov-based constraint is proposed to guarantee the closed-loop stability for the PF issue,and sufficient conditions regarding recursive feasibility and closed-loop stability are provided analytically.The target lateral force is transformed as front steering angle command by the inversive tire model,and the external yaw moment and total traction torque are distributed as the torque commands of IWMs by optimization.The validations prove the effectiveness of the proposed strategy in improved steering capacity,desirable PF effects,vehicle stabilization,and real-time applicability.

Predication-based adaptive control of a class of discrete-time nonlinear systems with nonlinear growth rate

Adaptive control of discrete-time parametric-strict-feedback nonlinear systems will be studied in this paper. To the best knowledge of the authors, almost all of the existing results for such systems need Lipschitz condition. The main purpose of this paper is to take a step towards nonlinear growth rate for second-order systems. The control law is designed based on least squares （LS） algorithms and on recursive adaptive predictors. Global stability and tracking performance are established for the closed-loop systems.

约束非线性系统理想点多目标安全预测控制

考虑具有状态和控制约束的仿射非线性系统多目标安全控制问题,本文提出一种保证安全和稳定的多目标安全模型预测控制(MOSMPC)策略.首先通过理想点逼近方法解决多个控制目标的冲突问题.其次,利用控制李雅普诺夫障碍函数(CLBF)参数化局部控制律,并确定系统不安全域.在此基础上,构造非线性系统的参数化双模控制器,减少在线求解模型预测控制(MPC)优化问题的计算量.进一步,应用双模控制原理和CLBF约束,建立MOSMPC策略的递推可行性和闭环系统的渐近稳定性,并保证闭环系统状态避开不安全域.最后,以加热系统的多目标控制为例,验证了本文策略的有效性.

基于模型预测的主动转向智能无人车辆横向稳态控制

在复杂路况以及智能无人车辆转向过程中,车辆处于一种时变非稳定性状态,影响车辆操纵安全性和行驶稳定性.针对此问题,提出了一种应用于前轮主动转向无人车辆的优化模型预测控制策略,该控制器采用分层结构对车辆前轮转角进行优化控制,以改善车身在非稳态下的特征.考虑到无人车辆实际行驶时存在的内部和外部扰动因素,建立了非线性扰动量平台模型和非线性轮胎模型,并通过模块化方法,构建了无人车辆非线性动力学模型,利用非线性扰动观测器对干扰进行抑制,提高了车辆模型精确性与控制器控制效果.经仿真实验对比和硬件在环实验测试,结果表明:与无横向稳态控制车辆相比,稳定性提高了55%,验证了该策略能显著改善车辆的横向稳定性及有效性.

面向越野车辆通过性安全的转矩协调分配研究

为了实现在通过越野地形和通过平整路面上大功率驱动时飞转车轮滑转率的快速平滑收敛,以及在执行器外特性与稳定性约束下实时优化各轮毂电机驱动功率利用率,采用前馈-反馈的闭环架构设计了纵向运动控制策略,其中前馈环节是基于垂向轮荷的实时估计与力矩矢量的预分配,优化驱动响应性并使各轮滑转率的方差最小。引入自适应滑模控制器动态跟踪四轮滑转率控制误差,构建基于鲁棒性、稳定性优化的反馈回路。仿真结果表明:该转矩协调控制系统可以有效地改善车辆的越野通过性,同时将轮胎滑移率控制在稳定范围内,提高车辆安全性。

非线性水轮机调节系统的鲁棒预测控制

随着“双碳”目标进程的加快,水电机组向高水头、大容量和长输水管道方向发展,对调节系统控制品质提出了更高要求。考虑水轮机调节系统是一个集水力、机械、电气为一体的非线性系统,而传统水轮机调节系统的PID控制方法为线性或线性化控制,因此研究水轮机调节系统新的先进性控制方法对提高机组运行质量具有重要意义。为此,本文采用鲁棒预测控制理论,设计了基于LMI鲁棒预测控制器,并通过理论分析证明了该方法的可靠性,通过数值试验验证新控制方法的控制效果,进一步讨论了该控制方法的抗干扰能力、跟踪性能等控制品质,并与传统PID控制方法作对比,分析了两种控制方法的优劣,结果表明提出的新控制方法提高了六维非线性水轮机调节系统的速动性和稳定性,具有良好的控制品质。

预测控制进展及其应用研究

本文探讨了预测控制理论的发展和现状 ,主要包括预测控制系统的稳定性、鲁棒性等性能以及对非线性系统的研究。分析了当前预测控制在工业控制领域中的发展、应用的现状和今后的发展方向。

预测控制性能研究的新进展

随着预测控制在工业过程中的广泛应用 ,近年来 ,预测控制理论研究也取得很大进展 .本文系统地综述和评析了在预测控制性能方面理论研究的新进展 ,主要内容包括预测控制系统的稳定性、鲁棒性、可行性等性能以及对非线性系统的研究 .

非线性系统神经网络预测控制研究进展

神经网络由于其在非线性系统建模与优化求解方面的优势,被广泛应用于预测控制中,形成了各种各样的神经网络预测控制算法.本文系统地评述了非线性系统神经网络预测控制系统中的模型选取、控制器优化、控制系统结构设计以及收敛性理论等研究现状,分析了非线性系统神经网络预测控制算法存在的问题和今后的研究方向.

非线性系统模型预测控制若干基本特点与主题回顾

随着线性系统模型预测控制的大量成功应用,基于非线性模型的预测控制简称非线性预测控制(NMPC)在近20年来引起了广泛关注,并取得了丰富的研究成果.本文在阐述NMPC基本原理和特点的基础上,分别从优化可行性、稳定性、鲁棒性、优化求解、吸引域等主题出发,分析并总结NMPC的研究现状.最后,在指出NMPC有待进一步深入研究问题的同时,展望当前NMPC的一些研究方向.

非线性系统参数自适应直接广义预测控制

针对广义预测控制(Generalized predictive control,GPC)计算量大的缺陷,本文对参数未知非线性系统提出一种直接广义预测控制(Direct generalized predictive control,DGPC)方法.该方法直接辨识广义预测控制器参数,即基于广义误差估计值对控制器参数θ_u和广义误差估计值中的未知向量θ_e进行自适应辨识.理论证明了该方法可使广义误差估值收敛到原点的一个小邻域内.

广义预测控制:理论、算法与应用

本文简要介绍了广义预测控制基本方法及其近年来提出的改进广义预测控制算法，并对非线性系统广义预测控制、多变量系统广义预测控制、广义预测控制的稳定性与鲁棒性分析作了进一步讨论．最后，概述了广义预测控制在工业中的应用和广义预测控制的进一步研究方向．

持续有界扰动下的非线性H_∞鲁棒预测控制

针对未知但有界的持续扰动,提出了一种约束非线性H_∞鲁棒预测控制策略.首先,引入离散系统的输入状态稳定性概念;其次,采用仿射输入定义预测控制的控制律,并给出相应终端约束集的估计解法.进一步,得到预测控制闭环系统的鲁棒稳定性结论.最后,数值仿真验证了上述策略的有效性.

约束非线性系统构造性模型预测控制

研究了连续时间约束非线性系统模型预测控制设计.利用控制Lyapunov函数离线构造单变量可调预测控制器,再根据性能指标在线优化可调参数,其中该参数近似于闭环系统的"衰减率".同时,控制Lyapunov函数保证了算法的可行性和闭环系统的稳定性.最后通过数值仿真验证了该算法的有效性.

汽轮发电机主汽门开度的非线性最优预测控制

基于非线性最优预测控制理论,提出了一种汽轮发电机主汽门开度非线性预测控制器的设计方法.通过单机无穷大系统的建模分析,导出具有闭型解析解的控制律,避免了在线优化带来的大量计算负担,从而满足实时控制的要求.控制器的输入信号均选为发电机组的当地测量量,便于工程实现.仿真结果表明,该控制器能有效地提高电力系统的暂态稳定及改善其动态品质.