Robust Output Feedback Model Predictive Control: A Stochastic Approach

This paper addresses the robust explicit model predictive control scheme for linear systems with input and output constraint in the presence of disturbances and noise. Conditions for disturbance rejection are established by incorporating a full state/disturbance observer. The separation principle is applied to design an optimal observer in the unconstrained problem. Then, an efficient algorithm is developed to explicitly design observer gains by minimizing a quadratic performance criterion. It is shown that the solution includes a set of regions with piecewise affine functions of the state and reference vectors and a set of regions with optimal observers. In the proposed method, two sets of partitions associated with the control law and the observer gains are obtained. Therefore, the online computation includes finding the active regions of both observer and control law partitions in which the current state is located. The proposed technique is particularly attractive for a wide range of practical problems where the exact model of the actual system is not available.     

电液伺服系统多项式非线性建模与控制一体化设计

常规电液伺服系统PID控制无法克服非线性因素影响,存在跟踪准确性和鲁棒性问题.因此,本文提出电液伺服系统多项式非线性H_∞控制律设计方法,改进电液伺服系统的控制性能与鲁棒性.首先利用多项式非线性模型对电液伺服系统进行系统辨识,得到以误差作为状态变量的多项式非线性模型;然后设计多项式非线性控制律,证明所提出控制律可以保证系统从干扰至控制输出L₂增益小于等于设定值,并且在系统干扰为零时保证误差全局渐进收敛,同时给出了控制律的求解方法.最后对提出的控制律进行实验验证.实验结果表明:相较于常规PID控制,多项式非线性控制律能够改善实验台伺服缸控制过程的瞬态响应,具有更好的抗干扰能力.本文提出的设计方法为非线性H_∞控制在电液伺服系统控制领域的实际应用提供了可行方案.     

一类具有非线性扰动的多重时滞不确定系统鲁棒预测控制

针对一类具有非线性扰动且同时存在多重状态和输入时滞的不确定系统, 提出 一种鲁棒预测控制器设计方法. 基于预测控制滚动优化原理, 运用Lyapunov稳定性 理论和线性矩阵不等式 (Linear matrix inequalities, LMIs)方法, 首先近似求解无限时域二次性能指标优化问题, 然后优化非 线性扰动项所应满足的最大上界, 定量地研究鲁棒预测控制在范数有界意义下的扰动抑制 问题, 并给出了鲁棒预测控制器存在的充分条件. 最后通过仿真验证了所提方法的有效性.     

Semi-global output regulation for linear systems with input saturation by composite nonlinear feedback control

To improve transient performance of output response, this paper applies composite nonlinear feedback (CNF) control technique to investigate semi-global output regulation problems for linear systems with input saturation. Based on a linear state feedback control law for a semi-global output regulation problem, a state feedback CNF control law is constructed by adding a nonlinear feedback part. The extra nonlinear feedback part can be applied to improve the transient performance of the closed-loop system. Moreover, an observer is designed to construct an output feedback CNF control law that also solves the semi-global output regulation problem. The sufficient solvability condition of the semi-global output regulation problem by CNF control is the same as that by linear control, but the CNF control technique can improve the transient performance. The effectiveness of the proposed method is illustrated by a disturbance rejection problem of a translational oscillator with rotational actuator system.     

Support vector machine based predictive functional control design for output temperature of coking furnace

A new support vector machine based nonlinear predictive functional control design method has been developed and applied to an industrial coking furnace, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection compared with traditional PFC and PID control strategies. The nonlinear process is first treated into a linear part plus a nonlinear part, then a convergent overall linear predictive functional control law is designed. The method gives a direct and effective multi-step predicting method and uses linear methods to get the control law which avoids the complicated nonlinear optimization. Comparison results and application to the temperature control of the industrial heavy oil coking furnace are presented in the article showing the efficiency of the method.     

Identification and predictive control of a multistage evaporator

A recurrent neural network-based nonlinear model predictive control (NMPC) scheme in parallel with PI control loops is developed for a simulation model of an industrial-scale five-stage evaporator. Input–output data from system identification experiments are used in training the network using the Levenberg–Marquardt algorithm with automatic differentiation. The same optimization algorithm is used in predictive control of the plant. The scheme is tested with set-point tracking and disturbance rejection problems on the plant while control performance is compared with that of PI controllers, a simplified mechanistic model-based NMPC developed in previous work and a linear model predictive controller (LMPC). Results show significant improvements in control performance by the new parallel NMPC–PI control scheme.     

Improved control using PFC and modified linear quadratic regulator for a kind of nonlinear systems

This paper describes the combination design of predictive functional control (PFC) and optimal linear quadratic (LQ) method for a kind of nonlinear process with output feedback coupling. In many existing control methods for this kind of nonlinear systems, the nonlinear part is either ignored or represented as a rough linear one when corresponding predictive control methods are designed. However, by assuming that the nonlinearity can be ignored or simplified to a linear time-varying part may not lead to the good control performance of subsequent linear control designs. The paper is a further investigation on this kind of systems, in which a procedure of PFC plus a modified optimal LQ control is developed. With respect to the proposed control strategy and the corresponding processes, the closed-loop performance is improved concerning tracking ability and disturbance rejection compared with previous predictive control methods. In addition, the proposed control is easy to implement as it selects a simple structure and a modification of the classical control scheme.     

Partially decoupled approach of extended non-minimal state space predictive functional control for MIMO processes

This paper presents a partially decoupled design of the state space predictive functional control for MIMO processes. The multivariable process is first treated into MISO process by a simple Cramer's rule solution to linear equations which provides a balance between model complexity and control system design, and then the derived MISO process based extended state space predictive functional control is presented. The overall design of the controller enables the controller to consider both the process state dynamics and the output dynamics, thus improved control performance for tracking set-points and disturbance rejection is resulted. The proposed controller is tested on both model match and model mismatch cases to demonstrate its superiority. In addition, a closed-form of transfer function representation that facilitates frequency analysis of the control system is provided to give further insight into the proposed method.     

Active Disturbance Rejection in Affine Nonlinear Systems Based on Equivalent‐Input‐Disturbance Approach

This paper presents a disturbance rejection method for an affine nonlinear system. The control system is constructed based on the equivalent‐input‐disturbance (EID) approach. An affine nonlinear state observer is used to reconstruct the state of the affine nonlinear system and to estimate an EID. The well‐known differential mean value theorem enables us to describe the closed‐loop system in the state space as a linear‐parameter‐varying system. This makes it easy to derive sufficient conditions of global uniform ultimate boundedness in term of linear matrix inequalities (LMIs) by using a Lyapunov function and convexity theory. Controllers are designed based on the LMIs. A numerical example is used to illustrate the design of the control system. And a comparison between the EID‐based control and the sliding‐mode control demonstrates the effectiveness and advantages of the EID‐based control method.     

A Novel Robust Flight Controller Design for an Air-Breathing Hypersonic Vehicle

A novel robust flight control design method is proposed for a generic air-breathing hypersonic vehicle based on a state-dependent Riccati equation technique and a nonlinear disturbance observer. The highly nonlinear dynamics of the hypersonic vehicle are firstly brought to a linear structure having a state-dependent coefficient form. And then a state-dependent Riccati equation is solved at each sampling moment to obtain a nonlinear feedback optimal control law. In order to enhance robustness of the closed-loop system, a nonlinear disturbance observer is introduced to estimate the uncertainty caused by parametric variations and external disturbances. The resulting composite controller achieves not only promising robustness and disturbance rejection performance but also flexible adjustment in the response time. Compared to a Kriging controller, the proposed controller has great advantages in the system response time and robustness. The feasibility of the proposed method is validated by simulation results.     

OPTIMAL REJECTION WITH ZERO STEADY‐STATE ERROR OF SINUSOIDAL DISTURBANCES FOR TIME‐DELAY SYSTEMS

This paper studies the problem of optimal rejection with zero steady‐state error of sinusoidal disturbances for linear systems with time‐delay. Based on the internal model principle, a disturbance compensator is constructed to counterbalance the external sinusoidal disturbances, so that the original system can be transformed into an augmented system without disturbances. Then, with the introduction of a sensitivity parameter and expanding power series around it, the optimal disturbance rejection problem can be simplified to the problem of solving an infinite sum of a linear optimal control series without time‐delay or disturbance. The optimal control law for disturbance rejection with zero steady‐state error consists of accurate linear state feedback terms and a time‐delay compensating term, which is an infinite sum of an adjoint vector series. In the presented approach, iteration is required only for the time‐delay compensation series. By intercepting a finite sum of the compensation series, we obtain an approximate physically realizable optimal control law that avoids complex calculation. A numerical simulation shows that the algorithm is effective and easy to implement.     

An improved structure for model predictive control using non-minimal state space realisation

This paper describes a new method for the design of model predictive control (MPC) using non-minimal state space models, in which the state variables are chosen as the set of measured input and output variables and their past values. It shows that the proposed design approach avoids the use of an observer to access the state information and, as a result, the disturbance rejection, particularly the system input disturbance rejection, is significantly improved when constraints become activated. In addition, when there is no model/plant mismatch, the paper shows that the system output constraints can be realised in the proposed approach. Furthermore, closed-form transfer function representation of the model predictive control system enables the application of frequency response analysis tools to the nominal performance of the system.     

一类不确定系统基于滑模干扰补偿的广义预测控制

广义预测控制(GPC)是基于非线性机理模型的一种优化控制策略, 但当系统存在内部不确定性、建模动态误差和外部干扰的情况下, 采用基于标称系统模型的GPC方法的系统性能将显著下降. 为此, 针对一类不确定非线性系统, 首先分析设计了一种基于不确定模型的理想GPC控制律; 同时设计了一种滑模干扰补偿器(SMDC)对系统的复合干扰进行估计, 将其输出作为补偿控制与标称GPC控制律结合以消除不确定性和外干扰的影响, 并利用Lyapunov理论分析了闭环复合系统的性能; 最后将其应用于一种高超声速飞行器(HSV)姿态控制系统, 仿真结果表明该方法具有很好的鲁棒特性和干扰衰减特性.     

A mixed PI/VI design method for nonlinear H∞ Control

This paper proposes a new mixed policy iteration and value iteration (PI/VI) design method for nonlinear H_∞ control based on the theories of polynomial optimization and Lasserre's hierarchy. The design of a mixed PI/VI controller can be carried out in four steps: firstly, initialize design parameters and expand nonlinear system matrices; secondly, obtain a polynomial matrix inequality for policy improvement; thirdly, obtain the Lasserre's hierarchy of a global polynomial optimization problem for value improvement; fourthly, perform the mixed PI/VI algorithm to approximate the optimal nonlinear H_∞ control law. The novelty of this work lies in that the problem of designing a nonlinear H_∞ controller is translated into a polynomial global optimization problem, which can be solved by Lasserre's hierarchy directly, and then, the mixed PI/VI algorithm is presented to approximate the optimal nonlinear H_∞ control law by updating global optimizers iteratively. The main results of this paper consist of the mixed PI/VI algorithm and the related three theorems, which guarantee robust stability and performance of the closed‐loop nonlinear system. Numerical simulations show that the mixed PI/VI algorithm converges very fast and achieves good robust stability and performance in transient behavior, disturbance rejection, and enlarging the domain of attraction of the close‐loop system.     

基于U模型的模糊免疫自抗扰控制方法研究

针对一类非线性系统在持续扰动下的控制问题,设计基于U模型的模糊免疫自抗扰控制方法。首先,引入U模型方法进行被控对象建模,提高处理非线性系统的能力,结合自抗扰控制方法,设计基于U模型的改进自抗扰控制器。在非线性反馈环节引入模糊免疫方法实现非线性智能反馈,设计基于U模型的模糊免疫自抗扰控制系统。最后仿真实验表明：基于U模型的模糊免疫自抗扰控制方法在保持了基于U模型的自抗扰控制的简洁性和良好抗扰性能的基础上,简化了控制器参数调节过程,在持续未知扰动下的跟踪速度、精度都更优。     

凹印机套色系统的自抗扰解耦控制

无轴凹印机套色系统的运行过程中存在各种形式的扰动,且各色组的套色误差通过张力传递相互耦合,严重影响系统的套准精度,必须进行解耦和抗扰控制.本文在建立了凹印机套色系统的近似数学模型的基础上,将扩张状态观测器与前馈相结合,提出了一种新的自抗扰控制策略.把一个强耦合、大扰动、模型不确定的复杂非线性系统动态补偿为近似的二阶线性系统,降低了系统的控制难度,改善了系统的动态性能,并使系统具有自抗扰的能力.仿真结果表明,与传统的前馈控制和非线性控制相比,该算法使系统控制误差的收敛速度加快,动态响应性能更优越,实现了色组间的解耦并使系统具有良好的抗扰性能.     

Design of explicit model predictive control for constrained linear systems with disturbances

On-line model predictive control approaches require the online solution of an optimization problem. In contrast, the explicit model predictive control moves major part of computation offline. Therefore, eMPC enables one to implement a MPC in real time for wide range of fast systems. The eMPC approach requires the exact system model and results a piecewise affine control law defined on a polyhedral partition in the state space. As an important limitation, disturbances may reduce performance of the explicit model predictive control. This paper presents efficient approach for handling the problem of using eMPC for constrained systems with disturbances. It proposes an approach to improve performance of the closed loop system by designing a suitable state and disturbance estimator. Conditions for observability of the disturbances are considered and it is depicted that applying the disturbance’s estimation leads to rejection of the response error. It is also shown that the proposed approach prevents the reduction of feasible space. Simulation results illustrate the advantages of this approach.     

Semi-global sampled-data output feedback disturbance rejection control for a class of uncertain nonlinear systems

This paper investigates the semi-global output feedback disturbance rejection control problem for a class of uncertain nonlinear systems with additive disturbances using linear sampled-data control. Aiming to reject the adverse effects caused by the uncertainties and unknown nonlinear perturbations which may not satisfy the strict feedback or feedforward structure, a new generalised discrete-time extended state observer is proposed to estimate the disturbance at sampling points. An output feedback disturbance rejection control law is then constructed in a sampled-data form which facilitates digital implementations. By selecting adequate control gains and a sufficiently small sampling period to restrain the state growth under a zero-order-hold input, the semi-global asymptotic stability of the hybrid closed-loop system and the disturbance rejection ability are proved. Both numerical example and an application of a single-link robot arm system demonstrate the feasibility and efficacy of the proposed method.     

基于支持向量机的一类非线性系统预测控制

针对一类具有输出反馈耦合的离散非线性系统,将过程的非线性部分通过支持向量机转化为全局线性状态空间模型,并在目标函数中引入系统状态的变化,给出一种类似于离散PI最优调节器的新型预测控制器.该方法不需要在线辨识系统参数,因为系统的内模已转换成全局离线模型.由于引入了新的优化目标函数,该控制器的控制效果和鲁棒性优于仅考虑预测输出误差的传统预测控制器.仿真结果表明,它也优于经典离散PI最优调节器.