Average dwell time-based optimal iterative learning control for multi-phase batch processes

Multi-phase batch process plays an important role in modern industry, especially for processes with different dimensional phases. As the different phases may interact with each others deeply, when and how to perform the transfer between adjacent phases highly affect the control performance and product quality. Meanwhile, the running time in different phases influence the production efficiency. Therefore it is of crucial importance to study the control of multi-phase batch processes with time constraints. Take the injection molding process as an example, a multi-phase batch process can be regarded as a switched system with different-dimensional subsystems in each batch. In this paper, the multi-phase batch process is converted to an equivalent two-dimensional (2D) switched system and the repetitive and 2D nature of batch processes is explored. Within the framework of 2D system theory, both the exponential stability and the shortest running time are considered. Meanwhile, a compound 2D controller with optimal performance is designed. The contributions of this paper are as follows: (1) the batch process studied is with different dimensions in each phase. (2) using the average dwell time method, a sufficient condition ensuring the exponential stability is obtained, meanwhile, the minimum running time of each subsystem, i.e., the running time of each phase can be calculated. Finally, the proposed method is illustrated with an injection molding process to show the effectiveness.     

Iterative learning model predictive control for multi-phase batch processes

Multi-phase batch process is common in industry, such as injection molding process, fermentation and sequencing batch reactor; however, it is still an open problem to control and analyze this kind of processes. Motivated by injection molding processes, the multi-phase batch process in each cycle is formulated as a switched system with internally forced switching instant. Controlling multi-phase batch processes can be decomposed into two subtasks: detecting the dynamics-switching-time; designing the control law for each phase with considering switching effect. In this paper, it is assumed that the dynamics-switching-time can be obtained in real-time and only the second subtask is studied. To exploit the repetitive nature of batch processes, iterative learning control scheme is used in batch direction. To deal with constraints, updating law is designed by using model predictive control scheme. An online iterative learning model predictive control (ILMPC) law is first proposed with a quadratic programming problem to be solved online. To reduce computation burden, an offline ILMPC is also proposed and compared. Applications on injection molding processes show that the proposed algorithms can control multi-phase batch processes well.     

Integrated design of fault reconstruction and fault-tolerant control against actuator faults using learning observers

This paper addresses the problem of integrated fault reconstruction and fault-tolerant control in linear systems subject to actuator faults via learning observers (LOs). A reconfigurable fault-tolerant controller is designed based on the constructed LO to compensate for the influence of actuator faults by stabilising the closed-loop system. An integrated design of the proposed LO and the fault-tolerant controller is explored such that their performance can be simultaneously considered and their coupling problem can be effectively solved. In addition, such an integrated design is formulated in terms of linear matrix inequalities (LMIs) that can be conveniently solved in a unified framework using LMI optimisation technique. At last, simulation studies on a micro-satellite attitude control system are provided to verify the effectiveness of the proposed approach.     

Iterative learning fault-tolerant control for differential time-delay batch processes in finite frequency domains

This paper develops a fault-tolerant iterative learning control law for a class of differential time-delay batch processes with actuator faults using the repetitive process setting. Once the dynamics are expressed in this setting, stability analysis and control law design makes use of the generalized Kalman–Yakubovich–Popov (KYP) lemma in the form of the corresponding linear matrix inequalities (LMIs). In particular, sufficient conditions for the existence of a fault-tolerant control law are developed together with design algorithms for the associated matrices. Under the action of this control law the ILC dynamics have a monotonicity property in terms of an error sequence formed from the difference between the supplied reference trajectory and the outputs produced. An extension to robust control against structured time-varying uncertainties is also developed. Finally, a simulation based case study on the model of a two-stage chemical reactor with delayed recycle is given to demonstrate the feasibility and effectiveness of the new designs.     

带有执行器故障的网络控制系统的自适应容错H∞控制

针对带有执行器故障的网络控制系统, 提出了一种自适应容错控制方法. 首先基于最近提出的一种新的网络诱导时滞模型, 设计了状态反馈形式的自适应容错控制器. 然后以线性矩阵不等式的形式给出了控制器存在的充分条件. 该条件不仅保证了系统在执行器故障和正常情形下均能达到稳定, 而且使得其H∞性能最优. 最后通过一个数值例子证明了所提方法的有效性.     

一类多指标约束下模糊非线性系统的满意容错控制

针对一般执行器故障模式的T-S模糊非线性系统,分别研究了同时具有稳定度指标、输入指标、输出指标约束下和同时具有闭环极点指标、方差指标、H∞指标约束下的满意容错控制器设计问题,给出了各相容指标的取值范围.仿真实例验证了所设计的满意容错控制策略的有效性,它不仅保证了执行器故障闭环系统的稳定性,而且保证系统具有良好的动态和稳态性能.     

Robust parameter-dependent fault-tolerant control for actuator and sensor faults

In this article, we study a robust fault-tolerant control (FTC) problem for linear systems subject to time-varying actuator and sensor faults. The faults under consideration are loss of effectiveness in actuators and sensors. Based on the estimated faults from a fault detection and isolation scheme, robust parameter-dependent FTC will be designed to stabilise the faulty system under all possible fault scenarios. The synthesis condition of such an FTC control law will be formulated in terms of linear matrix inequalities (LMIs) and can be solved efficiently by semi-definite programming. The proposed FTC approach will be demonstrated on a simple faulty system with different fault levels and fault estimation error bounds.     

一种带参考批次的线性时变系统迭代学习控制算法

针对线性时变系统的轨迹跟踪控制问题,提出一种带参考批次的迭代学习控制算法,并给出了算法的收敛性分析.该迭代学习控制算法不需要事先了解线性时变对象的太多知识,而是将当前批次输入轨迹的较小变化所引起的输出轨迹作为参考批次,并以当前批次与参考批次的输入变化与对应的输出变化之比作为学习律,从而实现目标轨迹的跟踪.以一个典型的线性时变系统为例进行仿真分析,验证了所提出算法的有效性.     

执行器故障多率采样间歇过程的鲁棒 耗散迭代学习容错控制

针对一类具有干扰和执行器故障的多率采样间歇过程,提出一种具有鲁棒耗散性能的迭代学习容错控制算法.通过提升技术将多采样率过程用慢速率采样的状态空间模型来描述,并基于二维系统理论,把迭代学习控制过程转化为等价2D Roesser故障系统,再沿时间和迭代方向设计具有耗散性能的反馈容错控制器,并以线性矩阵不等式形式给出容错控制器存在的充分条件,同时确保多率采样间歇过程在正常和故障条件下的耗散性能.注塑过程的注射速度控制仿真验证了方法的有效性和可行性.     

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。     

Passive fault-tolerant control of discrete time piecewise affine systems against actuator faults

In this article, we propose a new method for passive fault-tolerant control of discrete time piecewise affine systems. Actuator faults are considered. A reliable piecewise linear quadratic regulator state feedback is designed such that it can tolerate actuator faults. A sufficient condition for the existence of a passive fault-tolerant controller is derived and formulated as the feasibility of a set of linear matrix inequalities (LMIs). The upper bound on the performance cost can be minimised using a convex optimisation problem with LMI constraints which can be solved efficiently. The approach is illustrated on a numerical example and a two degree of freedom helicopter.     

Iterative learning control of heat-up phase for a batch polymerization reactor

This paper describes a novel method for heat-up phase control of an industrial batch polymerization reactor where heat transfer characteristics change with batches due to fouling of the polymer products on the reactor wall. The main objective of the control is to settle the reactor temperature on a target value within ± 0.1°C in a minimum possible time. To achieve this goal utilizing the repetitive nature of batch operation, the control problem was defined as a tracking problem and feedback-assisted iterative learning control (FBALC) was employed as the underlying control technique. The proposed control method was applied to an industrial batch reactor polymerizing ABS resin. After on-site evaluation for an extended period of time, it was found that the proposed method gives a pronounced improvement in heat-up phase operation. Consistent heat-up profiles with a minimized settling time are obtained.     

Iterative learning control with Smith time delay compensator for batch processes

How to improve the control of batch processes is not an easy task because of modeling errors and time delays. In this work, novel iterative learning control (ILC) strategies, which can fully use previous batch control information and are attached to the existing control systems to improve tracking performance through repetition, are proposed for SISO processes which have uncertainties in modeling and time delays. The dynamics of the process are represented by transfer function plus pure time delay. The stability properties of the proposed strategies for batch processes in the presence of uncertainties in modeling and/or time delays are analyzed in the frequency domain. Sufficient conditions guaranteeing convergence of tracking error are stated and proven. Simulation and experimental examples demonstrating these methods are presented.     

Generalized PI control design for a class of unknown nonaffine systems with sensor and actuator faults

This work deals with the tracking control problem of a class of unknown nonaffine dynamic systems that involve unpredictable sensor and actuation failures. As the control inputs enter into and influence the dynamic behavior of the nonaffine system through a nonlinear and implicit way, control design for such system becomes quite challenging. The underlying problem becomes even more complex if the system dynamics are unavailable for control design yet involving unanticipated sensor and/or actuator faults. In this work, a structurally simple and computationally inexpensive control scheme is proposed to achieve uniformly ultimately bounded (UUB) stable tracking control of a class of nonaffine systems. The proposed control is of a generalized PI form and is able to accommodate both sensor and actuator faults. The effectiveness of the proposed control strategy is confirmed by theoretical analysis and numerical simulations.     

Adaptive iterative learning control for switched nonlinear continuous-time systems

In this paper, an adaptive iterative learning control (ILC) method is proposed for switched nonlinear continuous-time systems with time-varying parametric uncertainties. First, an iterative learning controller is constructed with a state feedback term in the time domain and an adaptive learning term in the iteration domain. Then a switched nonlinear continuous-discrete two-dimensional (2D) system is built to describe the adaptive ILC system. Multiple 2D Lyapunov functions-based analysis ensures that the 2D system is exponentially stable, and the tracking error will converge to zero in the iteration domain. The design method of the iterative learning controller is obtained by solving a linear matrix inequality. Finally, the efficacy of the proposed controller is demonstrated by the simulation results.     

Optimal fault-tolerant control for Markov jump power systems with asynchronous actuator faults

Communication problems in the sensor-to-controller and controller-to-actuator channels can cause both the controller and actuator to run asynchronously with the original system in different operating modes. This paper investigates an optimal fault-tolerant control approach for Markov jump power systems (MJPSs) with asynchronous controller and actuator. Firstly, an asynchronous controller is proposed to deal with incomplete information (i.e., system modes) transmission in the sensor-to-controller channel. Secondly, a new asynchronous actuator faults model is constructed to simultaneously represent the two partial losses, of modes information in the controller-to-actuator channel, and of control effectiveness (LoCE) caused by actuator faults. Under this framework, two related hidden Markov models (HMMs) are formed, which reveal that both the controller and actuator are asynchronous with the controlled system in different modes. By using Lyapunov and optimal approaches, sufficient conditions are derived to ensure that MJPSs are mean square stable with an optimal guaranteed cost. Finally, Monte Carlo simulations are used to verify the effectiveness of the proposed control method.     

Distributed adaptive fault-tolerant control against actuator faults and lossy interconnection links

This paper presents an adaptive method to solve the robust fault-tolerant control （FTC） problem for a class of large scale systems against actuator failures and lossy interconnection links. In terms of the special distributed architectures, the adaptation laws are proposed to estimate the unknown eventual faults of actuators and interconnections, constant external disturbances, and controller parameters on-line. Then a class of distributed state feedback controllers are constructed for automatically compensating the fault and disturbance effects on systems based on the information from adaptive schemes. On the basis of Lyapunov stability theory, it shows that the resulting adaptive closed-loop large-scale system can be guaranteed to be asymptotically stable in the presence of uncertain faults of actuators and interconnections, and constant disturbances. The proposed design technique is finally evaluated in the light of a simulation example.     

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.     

基于传感器与执行器同时失误的鲁棒可靠 H∞控制

讨论线性不确定系统的鲁棒可靠H∞控制问题。对于执行器和传感器同时失误的情况，基于线性矩阵不等式方法，给出了经估计状态反馈可靠H∞控制的设计方案。采用该方案设计的可靠控制系统，不仅在系统运行良好的条件下，而且在系统的传感器和执行器元件均出现失误的情况下，仍能确保系统内部状态的稳定性，并同时满足给定的H∞性能指标。最后，以一个数值调子说明了所给出结论的有效性。     

初态学习下的迭代学习控制

提出一种新的初态学习律,以放宽常规迭代学习控制方法的初始定位条件．它允许一定的定位误差,在迭代中不需要定位在某一具体位置上,使得学习控制系统具有鲁棒收敛性．针对二阶LTI系统,给出了输入学习律及初态学习律的收敛性充分条件．依据收敛性条件,学习增益的选取需系统矩阵的估计值,但在一定建模误差下,仍能保证算法的收敛性．所提出的初态学习律本身及其收敛性条件均与输入矩阵无关．