一种实用的模型自适应MPC策略

在丙烯精馏塔的控制中,由于被控对象的非线性特性,采用线性模型的模型预测控制器难以保持良好的控制性能。本文提出基于系统稳态模型的模型自适应MPC策略,利用稳态模型在不同操作点上被控变量对操纵变量及扰动变量的相对变化率的变化,来刷新RMPCT控制器中各通道的模型增益。在模型输出对输入的相对变化率的计算中,使用主操作区间内的变化率以替代实际操作点的变化率,并采用设定模型变化域和控制模型变化频度的方法,以解决模型变化过大,与模型变换周期和RMPCT控制周期不协调而引起的系统不稳定等问题。实际投运效果表明:采用该控制策略,塔顶、塔底温度控制偏差与传统RMPCT比下降了一个数量级,有利于稳定与提高产品质量。     

An adaptive multi-parameter based dispatching strategy for single-loop interbay material handling systems

The automation of interbay systems in 300 mm semiconductor wafer fabrication systems (SWFSs) is complex due to the dynamic, stochastic and mass transportation demands, transportation deadlocks, and vehicle blockages. An adaptive multi-parameter based (AMP) dispatching policy is proposed to obtain better performance of the interbay material handling systems and SWFSs. The system parameters, including vehicle's distance, lot's due date, lot's waiting time, and lot's origin-destination buffer status parameters are simultaneously considered, and the multi-parameter's weight coefficients are adjusted adaptively by Takagi-Sugeno fuzzy logic method. With experimental data from an interbay system of 300 mm SWFSs and running simulation experiments, it is demonstrated that the proposed approach has better performance in terms of cycle time, throughput, due-date satisfaction rate, and vehicle utilization compared to conventional single- and multi-attribute dispatching methodologies.     

A practical approach for hybrid distributed MPC

This paper presents a framework to deal with distributed optimization problems composed by binary and continuous variables. Instead of using a mixed integer quadratic programming (MIQP), the approach proposed here transforms the MIQP into a set of quadratic programming's (QP) that are easier to solve. In this way an instance of the controller related to each feasible combination of binary variables is created. The distributed controller performs an iterative process where the set of agents must agree on the value of continuous interconnection variables, while each agent must decide the values of local binary variables. During the iteration procedure the instances are rated according to a performance index and the instances with best performance are selected until the best one is obtained. The proposed methodology is applied to economic optimization of networked microgrids.     

An adaptive hybrid single-loop method for reliability-based design optimization using iterative control strategy

Single-loop approach (SLA) is one of the most promising methods for solving linear and weakly nonlinear reliability-based design optimization (RBDO) problems. However, since SLA locates the current approximate most probable point (MPP) by using the gradient information of the previous one to reduce the computational cost, it may lead to inaccuracy when the nonlinearity of probabilistic constraints becomes relatively high. To overcome this limitation, a new adaptive hybrid single-loop method (AH-SLM) that can automatically choose to search for the approximate MPP or accurate MPP is proposed in this paper. Moreover, to get the accurate MPP more efficiently and alleviate the oscillation in the search process, an iterative control strategy (ICS) with two iterative control criteria is developed. In each iterative step, the KKT-condition of performance measure approach (PMA) is introduced to check the validity of the approximate MPP. If the approximate MPP is infeasible, ICS will be further carried out to search for the accurate MPP. The two iterative control criteria are used to update the oscillation control step length, then ICS can converge fast for both weakly and highly nonlinear performance functions. Besides, numerical examples are presented to verify the efficiency and robustness of our proposed method.     

A multi-iteration pseudo-linear regression method and an adaptive disturbance model for MPC

This paper proposes an MPC method that uses an adaptive disturbance model to improve the accuracy of prediction. In unmeasured disturbance model identification, a novel multi-iteration pseudo-linear regression (MIPLR) method is used which is more accurate and has faster convergence than traditional recursive identification methods. The adaptive disturbance model is used in an MPC scheme for improved performance in disturbance rejection. The method is demonstrated by the simulation of a distillation column and also tested on the real process. The test results show that the proposed MPC scheme can not only increase control performance, but also increase robustness.     

A describing function technique for multiple nonlinearities in a single-loop feedback system

A graphical technique for determining the existence of limit cycles, their amplitude, frequency, and stability when they exist, and the stability of a single-loop feedback system withn - 1memoryless nonlinear elements and one nonlinear element with memory is considered. The approach here is to assume an input to a nonlinear element and then apply the Nyquist stability condition to the linear system resulting after the nonlinear elements have been approximated by their describing functions. The method requires no trial and error procedure, is noniterative in nature, and is especially easy to apply. The method is subject to the usual errors and restrictions of the describing function method. An extension of the method to includennon-linear elements with memory andnnonlinear elements in parallel is also included. Three numerical examples are included to illustrate the method.     

A distributed model predictive control (MPC) fault reconfiguration strategy for formation flying satellites

In this paper, an active distributed (also referred to as semi-decentralised) fault recovery control scheme is proposed that employs inaccurate and unreliable fault information into a model-predictive-control-based design. The objective is to compensate for the identified actuator faults that are subject to uncertainties and detection time delays, in the attitude control subsystems of formation flying satellites. The proposed distributed fault recovery scheme is developed through a two-level hierarchical framework. In the first level, or the agent level, the fault is recovered locally to maintain as much as possible the design specifications, feasibility, and tracking performance of all the agents. In the second level, or the formation level, the recovery is carried out by enhancing the entire team performance. The fault recovery performance of our proposed distributed (semi-decentralised) scheme is compared with two other alternative schemes, namely the centralised and the decentralised fault recovery schemes. It is shown that the distributed (semi-decentralised) fault recovery scheme satisfies the recovery design specifications and also imposes lower fault compensation control effort cost and communication bandwidth requirements as compared to the centralised scheme. Our proposed distributed (semi-decentralised) scheme also outperforms the achievable performance capabilities of the decentralised scheme. Simulation results corresponding to a network of four precision formation flight satellites are also provided to demonstrate and illustrate the advantages of our proposed distributed (semi-decentralised) fault recovery strategy.     

A framework for multiple robust explicit MPC controllers for linear systems

This article presents a multi-mode explicit controller for constrained linear systems with bounded disturbances using a switching strategy based on Model Predictive Control (MPC). In the proposed approach, the system switches among several MPC controllers having different performance levels. The switching is done so as to achieve increasing levels of performance as time evolves, reaching the desired controller in finite time steps. The conditions needed for switching and robust convergence of the multi-mode MPC controllers are provided. Compared with standard robust explicit MPC implementations, the proposed approach has the flexibility of having a large domain of attraction, a good asymptotic behaviour and a small number of partitions.     

Convergence of adaptive MPC for linear stochastic systems

The convergence of an adaptive model predictive control(MPC) algorithm for discrete-time linear stochastic systems with unknown parameters is investigated in this paper. The proposed adaptive MPC is designed by solving a finite horizon constrained linear-quadratic optimal control problem of online estimated models, which are built on a recursive weighted least-squares(WLS) algorithm together with a random regularization method. By incorporating an attenuating excitation signal into adaptive MPC,...     

A dual Newton strategy for scenario decomposition in robust multistage MPC

This paper considers the solution of tree‐structured quadratic programs as they may arise in multistage model predictive control. In this context, sampling the uncertainty on prescribed decision points gives rise to different scenarios that are linked to each other via the so‐called nonanticipativity constraints. Previous work suggests to dualize these constraints and apply Newton's method on the dual problem to achieve a parallelizable scheme. However, it has been observed that the globalization strategy in such an approach can be expensive. To alleviate this problem, we propose to dualize both the nonanticipativity constraints and the dynamics to obtain a computationally cheap globalization. The dual Newton system is then reformulated into small highly structured linear systems that can be solved in parallel to a large extent. The algorithm is complemented by an open‐source software implementation that targets embedded optimal control applications.     

A practical strategy for certifying GKS implementations

The question of how to validate GKS implementations is crucial to the success of GKS as an international standard for computer graphics. This problem has been addressed by a series of certification workshops sponsored by the EEC. A basic strategy for testing GKS implementations is outlined and progress towards the development of a test suite is reported.     

Collaborative software infrastructure for adaptive multiple model simulation

This paper presents a software infrastructure being developed to support the implementation of adaptive multiple model simulations. The paper first describes an abstraction of single and multiple model simulations into the individual operational components with a focus on the relationships and transformations that relate them. Building on that abstraction, consideration is then given to how adaptively controlled multiple model simulations can be constructed using existing simulation components interacting through functional interfaces. This includes addressing how experts would provide the infrastructure with the needed components and define the relations and transformations needed to interact with other components, and for users to define the simulations they wish to be executed. Next, a discussion of the software environment used to implement the multiple model simulation infrastructure is given. Finally, there is discussion of the implementation, using this infrastructure, of two multiscale and one multiple fidelity model simulation applications.     

An interpolation strategy for discrete-time bilinear MPC problems

Input-output (I-O) feedback linearization suffers from a number of restrictions which have limited its use in model-based predictive control. Some of these restrictions do not apply to the case of bilinear systems, but problems with input constraints and unstable zero dynamics persist. This paper overcomes these difficulties by means of an interpolation strategy. Involved in this interpolation is a feasible and stabilizing trajectory, which is computed through the use of invariant feasible sets, and a more aggressive trajectory, which can be chosen to be either the unconstrained optimal trajectory or any alternative one     

A gradient-based strategy for the one-layer RTO + MPC controller

In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economic objective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimal setpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachability of those setpoints, due to the different models employed by the RTO and the MPC, as well as their different time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamic control layer is presented. To overcome the complexity of this one-layer formulation a gradient-based approximation is proposed, which provides a low-computational-cost suboptimal solution.     

A practical control strategy for servo-pneumatic actuator systems

A practical control strategy with a simple controller structure is proposed for servo-pneumatic cylinder actuator systems. Theoretical analysis reveals that the acceleration of the piston indirectly represents the cylinder chamber pressure difference so it is possible to employ acceleration feedback instead of pressure feedback in the construction of servo-pneumatic actuator control systems. The main features of the control strategy developed in the paper are (1) using acceleration feedback to improve the stability of the system; and (2) introducing time-delay minimisation and optimised null offset compensation to address the problem of time delay and dead zone, which are mainly caused by the compressibility of air and friction. The experimental studies have been conducted using an asymmetric pneumatic cylinder system and the results show that the system performance has been much improved when compared with a conventional PID controller.     

A multiple model adaptive dual control algorithm for stochastic systems with unknown parameters

An adaptive dual control algorithm is presented for linear stochastic systems with constant but unknown parameters. The system parameters are assumed to belong to a finite set on which a prior probability distribution is available. The tool used to derive the algorithm is preposterior analysis: a probabilistic characterization of the future adaptation process allows the controller to take advantage of the dual effect. The resulting actively adaptive control called model adaptive dual (MAD) control is compared to two passively adaptive control algorithms-the heuristic certainty equivalence (HCE) and the Deshpande-Upadhyay-Lainiotis (DUL) model-weighted controllers. An analysis technique developed for the comparison of different controllers is used to show statistically significant improvement in the performance of the MAD algorithm over those of the HCE and DUL.     

A method of MPC model error detection

This work introduces a method of multivariable model error detection in model prediction control (MPC). The idea is to use non-disturbing small sinusoidal test signals to obtain accurate estimates of process frequency responses at several frequency points. Then, the differences between estimated frequency responses and the frequency responses of current MPC model are used to form the model error index matrix which is used to access the model error of the MPC controller. An upper error bound is developed for quantifying the error of frequency response estimation. The method works in closed-loop operation with the MPC controller online. Simulation studies are used to demonstrate the use of the method.     

A multiple model predictive control strategy in the PLS framework

For chemical processes with a wide range of operating conditions, a switched multiple model predictive control (MMPC) strategy in the partial least squares (PLS) framework is proposed. Interactive MIMO systems can be automatically decoupled with inputs and outputs paired in their dynamic PLS models. Based on the identified PLS models, companion controllers are designed to form the MMPC strategy. A novel switching criterion based on output statistics is proposed to assure each model/control pair works in its operating region spanned by the identification data sets. The control results of disturbance rejection and setpoint tracking in a two-phase chemical reactor process are presented to demonstrate the capability and effectiveness of the proposed MMPC strategy.     

多模型分层递阶自适应前馈解耦控制器

针对参数跳变系统,提出一种基于分层递阶结构的多模型自适应前馈解耦控制器.该控制器采用多模型方法来提高系统的暂态性能;采用自适应方法消除系统的稳态误差,采用分层递阶结构减少系统模型集的数量和计算时间.为了在分布式计算机集散控制系统(DCS)中得到应用,该控制器根据耦合的形成机理和DCS的结构特点,将系统变量之间的耦合作用视为可测干扰,采用前馈结构予以消除.通过加权多项式的选取,不仅实现了极点配置,而且可以动态解耦.最后给出了全局收敛性分析.仿真结果表明,与常规多模型控制方法相比,大大减少了固定模型的数量;而当模型数目相同时,系统的暂态响应、解耦效果都大为改善.     

加权多模型自适应控制的稳定性

加权多模型自适应控制的稳定性证明是一个未解决的问题.本文采用基于模型输出误差的递推加权算法,在模型输出误差可分的情况下,可以保证其收敛性;然后在加权收敛的前提下,借助虚拟等价系统的概念和方法证明了加权多模型自适应控制系统的稳定性和收敛性.本文的分析方法和结论不依赖于具体的局部控制策略和具体的权值算法,而只取决于它们的某些属性.