使用低阶参考模型的离散多变量MRACS

本文提出了一种高阶被控对象跟随低阶参考模型的离散多变量模型参考自适应控制系统(MRACS)的设计方法,它利用数字滤波器将系统降阶,通过求解p个一次代数方程而确定的自适应控制输入信号,能够使多变量被控对象的每个多输入单输出(MISO)子系统的输出渐近收敛到对应的单输入单输出(SISO)低阶参考模型的输出,提出的自适应控制器不仅避免了引进正反馈和辅助信号,而且实现了自适应解耦控制,仿真结果表明该控制器具有较好的控制性能。     

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。     

先进控制在化工装置的最新应用

Profit Loop是霍尼韦尔公司PKS系统中的模型预测控制器,它用一个单输入/单输出的过程模型预测过去、现在和将来控制动作对被控变量的影响,以实现期望的控制目标。Profit Loop是控制技术的新突破,能够超越取代传统的PID控制,能够显著提升过程控制的质量指标,应用前景广阔。     

飞行器动力系统多变量控制结构设计研究

针对飞行器动力系统多变量控制系统结构设计问题,提出了以相关增益矩阵方法确定发动机多变量控制方案中被控参数与控制量的一一对应关系,阐明了相关增益矩阵的意义.以二输入二输出系统为例推导了相关增益矩阵的比值计算方法.并以某型三输入三输出的变循环发动机为仿真算例,计算了其各被控参数和控制量的相关关系.根据其相关关系,确定了该变循环发动机多变量控制系统结构,并分析了其相关关系对飞行器动力系统多变量控制方案确定的意义.仿真算例表明,利用相关增益矩阵方法可以定量地确定系统各控制量和被控参数的相关关系,从而可以避免传统方法仅通过经验定性确定其相关关系的缺点.     

时变对象模糊控制稳态性能的提高

本文提出了两种提高模糊控制稳态精度的方法，一种方法是分档控制，暂态时用“粗调”控制器以加快响应速度，稳定时用“细调”控制器以提高稳态精度，另一种方法是通过推知被控对象的慢变规律，然后根据这个规律补偿对象增益的慢变以得到恒定的输出，对一个参数慢时变的二阶被控对象的仿真实验验证了上述方法有效性，稳态精度大为提高，抗干扰性增强。     

一种新型控制器的应用

Profit Loop是霍尼韦尔公司PKS系统中的模型预测控制器,它用一个单输入/单输出的过程模型预测过去、现在和将来控制动作对被控变量的影响,以实现期望的控制目标。Profit Loop是控制技术的新突破,能够超越取代传统的PID控制,能够显著提升过程控制的质量指标,应用前景广阔。本文说明Profit Loop在精馏塔控制中的应用。     

多变量解耦控制系统设计与仿真

工业生产过程中,被控系统越来越复杂,需要控制的变量通常不止一对,而且相互耦合,较成熟的线性多变量控制理论与设计方法已难以满足实际的多变量生产过程控制。本文针对多变量强耦合带有延迟环节的被控系统,研究了一种简化的串联前馈补偿解耦控制器设计方法。根据被控对象的辨识模型,设计串联前馈补偿解耦器,使系统解耦成单输入单输出模型,进而分别对解耦后输入输出模型设计PID控制器,整定PID控制器参数。仿真结果表明该方法具有较好的解耦能力和鲁棒性,算法简单,易于实现。     

多变量解耦控制系统设计与仿真北大核心CSCD

工业生产过程中,被控系统越来越复杂,需要控制的变量通常不止一对,而且相互耦合,较成熟的线性多变量控制理论与设计方法已难以满足实际的多变量生产过程控制。本文针对多变量强耦合带有延迟环节的被控系统,研究了一种简化的串联前馈补偿解耦控制器设计方法。根据被控对象的辨识模型,设计串联前馈补偿解耦器,使系统解耦成单输入单输出模型,进而分别对解耦后输入输出模型设计PID控制器,整定PID控制器参数。仿真结果表明该方法具有较好的解耦能力和鲁棒性,算法简单,易于实现。     

具有减少辨识参数的随机多变量极点配置自适应控制器

本文提出了一种新的限制输出个数减少随机多变量自适应控制中辨识参数的方法，并给出了减少辨识参数的极点配置自适应算法。虽然采用ｎ个输入１个输出的减少辨识参数的模型来设计控制器，但所提出的控制器能够保证被控系统的几个输出跟踪参考输入信号，仿真结果表明，所提出的方法是成功的。     

一种新型的对角型模糊变结构轨迹跟踪控制器设计

文章试图结合模糊控制和滑模变结构控制的优点,设计一种新的对角型模糊变结构轨迹跟踪控制器。用SISO的对角型模糊逻辑控制取代一种一般的变结构轨迹跟踪控制器的切换控制项,并且增加了动态调整输入和输出空间的功能去增强系统的快速性和灵活性。仿真表明了该控制器具有快速性、较小稳态误差及较强抗干扰能力;所设计的控制器性能良好,具有一定实用价值。     

Novel MPC‐Based Fault Tolerant Tracking Control Against Sensor Faults

The problem of active fault‐tolerant tracking control with control input and system output constraints is studied for a class of discrete‐time systems subject to sensor faults. A time‐varying fault‐tolerant observer is first developed to estimate the real system state from the faulty sensor output and control input signals. Then by using the estimated state at each time step, a model predictive control (MPC)‐based fault‐tolerant tracking control scheme is presented to guarantee the desired tracking performance and the given input and output constraints on the faulty system. In comparison with many existing fault‐tolerant MPC methods, its main contribution is that the proposed state estimator is designed by the simple and online numerical computation to tolerate the possible sensor faults, so that the regular MPC algorithm without fault information can be adopted for the online calculation of fault‐tolerant control signal. The potential recursive infeasibility and computational complexity due to the faults are avoided in the scheme. Additionally, the closed‐loop stability of the post‐fault system is discussed. Simulative results of an electric throttle control system verify the effectiveness of the proposed method.     

Disturbance modeling for offset-free linear model predictive control

An offset-free controller is one that drives controlled outputs to their desired targets at steady state. In the linear model predictive control (MPC) framework, offset-free control is usually achieved by adding step disturbances to the process model. The most widely-used industrial MPC implementations assume a constant output disturbance that can lead to sluggish rejection of disturbances that enter the process elsewhere. This paper presents a general disturbance model that accommodates unmeasured disturbances entering through the process input, state, or output. Conditions that guarantee detectability of the augmented system model are provided, and a steady-state target calculation is constructed to remove the effects of estimated disturbances. Conditions for which offset-free control is possible are stated for the combined estimator, steady-state target calculation, and dynamic controller. Simulation examples are provided to illustrate trade-offs in disturbance model design.     

Reconfigurable predictive control for redundantly actuated systems with parameterised input constraints

A method is proposed for on-line reconfiguration of the terminal constraint used to provide theoretical nominal stability guarantees in linear model predictive control (MPC). By parameterising the terminal constraint, its complete reconstruction is avoided when input constraints are modified to accommodate faults. To enlarge the region of feasibility of the terminal control law for a certain class of input faults with redundantly actuated plants, the linear terminal controller is defined in terms of virtual commands. A suitable terminal cost weighting for the reconfigurable MPC is obtained by means of an upper bound on the cost for all feasible realisations of the virtual commands from the terminal controller. Conditions are proposed that guarantee feasibility recovery for a defined subset of faults. The proposed method is demonstrated by means of a numerical example.     

Reference trajectory tuning of model predictive control

An approach to minimize tuning effort of nominal Model Predictive Control algorithms is proposed. The algorithm dynamically calculates output set points to accommodate user-defined output importance, which is more intuitive than selecting values for the MPC weighing matrices. Instead of tuning the weights on the outputs deviations from their set points, weights on the input values and input increments, which are the usual tuning parameters of MPC, the desired output control performance of the MPC can be specified by performance factors. The proposed method extends the existing methods that consider a reference trajectory for the output tracking to the case of zone control and input targets. The proposed method also assumes that, as in most commercial MPC packages, the controller has two layers: a static layer and an extended dynamic layer. The method is illustrated by three case studies, contemplating both SISO and MIMO systems. It is observed that: the output set point tracking performance can be changed without modifying the MPC tuning weights, the approach is capable of achieving similar performance to conventional MPC tuned by multiobjective optimization techniques from the literature, with a fraction of computer effort, and it can be integrated with Real Time Optimization algorithms to control complex systems, always respecting output constraints.     

Multi-input single-output control for extending the operating range: Generalized split range control using the baton strategy

Split range control is used to extend the steady-state operating range for a single output (controlled variable) by using multiple inputs (manipulated variables). The standard implementation of split range control uses a single controller with a split range block, but this approach has limitations when it comes to tuning. In this paper, we introduce a generalized split range control structure that overcomes these limitations by using multiple independent controllers with the same setpoint. Undesired switching between the controllers is avoided by using a baton strategy where only one controller is active at a time. As an alternative solution we consider model predictive control (MPC), but it requires a detailed dynamic model and does not allow for using only one input at a time.     

Sensor‐fault tolerance using robust MPC with set‐based state estimation and active fault isolation

In this paper, a sensor fault‐tolerant control scheme using robust model predictive control (MPC) and set‐theoretic fault detection and isolation (FDI) is proposed. The robust MPC controller is used to control the plant in the presence of process disturbances and measurement noises while implementing a mechanism to tolerate faults. In the proposed scheme, fault detection (FD) is passive based on interval observers, while fault isolation (FI) is active by means of MPC and set manipulations. The basic idea is that for a healthy or faulty mode, one can construct the corresponding output set. The size and location of the output set can be manipulated by adjusting the size and center of the set of plant inputs. Furthermore, the inputs can be adjusted on‐line by changing the input‐constraint set of the MPC controller. In this way, one can design an input set able to separate all output sets corresponding to all considered healthy and faulty modes from each other. Consequently, all the considered healthy and faulty modes can be isolated after detecting a mode changing while preserving feasibility of MPC controller. As a case study, an electric circuit is used to illustrate the effectiveness of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.     

Experimental quadrotor flight performance using computationally efficient and recursively feasible linear model predictive control

A new linear model predictive control (MPC) algorithm in a state-space framework is presented based on the fusion of two past MPC control laws: steady-state optimal MPC (SSOMPC) and Laguerre optimal MPC (LOMPC). The new controller, SSLOMPC, is demonstrated to have improved feasibility, tracking performance and computation time than its predecessors. This is verified in both simulation and practical experimentation on a quadrotor unmanned air vehicle in an indoor motion-capture testbed. The performance of the control law is experimentally compared with proportional-integral-derivative (PID) and linear quadratic regulator (LQR) controllers in an unconstrained square manoeuvre. The use of soft control output and hard control input constraints is also examined in single and dual constrained manoeuvres.     

输入变量关联约束对约束优化控制的影响特性分析

过程工业控制中除了存在常见的输入变量和输出变量幅值高低限约束, 由于工艺或者控制的需要也可能具有关于输入变量线性函数的关联约束. 不同约束条件之间的矛盾可能会造成约束条件无法全部满足, 失去了实施预测控制的基础. 从凸体顶点角度, 将具有输入关联约束的约束优化控制的可行性判定转化为凸多面集是否非空的问题. 为保证具有输入关联约束预测控制的有效实施, 本文将输入关联约束纳入到预测控制控制律的求解当中. 基于Newton控制框架, 考虑具有输入关联约束条件下, 得到基于区间控制思想的预测控制律的解析表达式, 从而分析输入关联约束条件对控制的影响. 通过典型系统模型的控制仿真实验, 验证以上方法的有效性.     

Predictive metamorphic control

In spite of its easy implementation, ability to handle constraints and nonlinearities, etc., model predictive control (MPC) does have drawbacks including tuning difficulties. In this paper, we propose a refinement to the basic MPC strategy by incorporating a tuning parameter such that one can move smoothly from an existing controller to a new MPC strategy. Each change of this tuning parameter leads to a new stabilising control law, therefore, allowing one to gradually move from an existing control law to a new and better one. For the infinite horizon case without constraints and for the general case with state and input constraints, stability results are established. We also examine the practical applicability of the proposed approach by employing it in the nominal prediction model of the tube-based output feedback robust MPC method. The merits of the proposed method are illustrated by examples.     

From data to diagnosis and control using generalized orthonormal basis filters. Part II: Model predictive and fault tolerant control

Given a state space model together with the state noise and measurement noise characteristics, there are well established procedures to design a Kalman filter based model predictive control (MPC) and fault diagnosis scheme. In practice, however, such disturbance models relating the true root cause of the unmeasured disturbances with the states/outputs are difficult to develop. To alleviate this difficulty, we reformulate the MPC scheme proposed by K.R. Muske and J.B. Rawlings [Model predictive control with linear models, AIChE J. 39 (1993) 262–287] and the fault tolerant control scheme (FTCS) proposed by J. Prakash, S.C. Patwardhan, and S. Narasimhan [A supervisory approach to fault tolerant control of linear multivariable systems, Ind. Eng. Chem. Res. 41 (2002) 2270–2281] starting from the innovations form of state space model identified using generalized orthonormal basis function (GOBF) parameterization. The efficacy of the proposed MPC scheme and the on-line FTCS is demonstrated by conducting simulation studies on the benchmark shell control problem (SCP) and experimental studies on a laboratory scale continuous stirred tank heater (CSTH) system. The analysis of the simulation and experimental results reveals that the MPC scheme formulated using the identified observers produces superior regulatory performance when compared to the regulatory performance of conventional MPC controller even in the presence of significant plant model mismatch. The FTCS reformulated using the innovations form of state space model is able to isolate sensor as well as actuator faults occurring sequentially in time. In particular, the proposed FTCS is able to eliminate offset between the true value of the measured variable and the setpoint in the presence of sensor biases. Thus, the simulation and experimental study clearly demonstrate the advantages of formulating MPC and generalized likelihood ratio (GLR) based fault diagnosis schemes using the innovations form of state space model identified from input output data.