Iterative Learning Fault-tolerant Control Research Articles

Iterative learning fault-tolerant control of networked systems with quantitative sampling

Iterative learning fault-tolerant control of networked systems with quantitative sampling

Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths.

This article presents an adaptive iterative learning fault-tolerant control algorithm for state constrained nonlinear systems with randomly varying iteration lengths subjected to actuator faults. First, the modified parameters updating laws are designed through a new defined tracking error to handle the randomly varying iteration lengths. Second, the radial basis function neural network method is used to deal with the time-iteration-dependent unknown nonlinearity, and a barrier Lyapunov function is given to cope with the state constraint. Finally, a new barrier composite energy function is used to achieve the tracking error convergence of the presented control algorithm along the iteration axis with the state constraint and then followed with the extension to the high-order case. A simulation for a single-link manipulator is given to illustrate the effectiveness of the theoretical studies.

Adaptive Iterative Learning Fault-Tolerant Consensus Control of Multiagent Systems Under Binary-Valued Communications.

In this article, the iterative learning averaging consensus problem is studied for multiagent systems with system uncertainties, actuator faults, and binary-valued communications. Considering only binary-valued measurement information with stochastic noise can be received from its neighbors for each agent, a new two-iteration-scale framework that alternates estimation and control is designed. Under the proposed framework, each agent estimates the neighbors' states based on the empirical measurement method during a dwell iteration interval, during which each agent's states will keep constant along the iteration axis. Further, in view of the impacts of system uncertainties and actuator faults, a novel adaptive iterative learning fault-tolerant averaging consensus control scheme is designed based on its own states and the estimated neighbors' states. Finally, the resulting closed-loop system is rigorously proved to be stable, and numerical simulations are conducted to demonstrate the effectiveness of the developed control strategy.

Iterative Learning Control for Actuator Fault Uncertain Systems

An iterative learning fault-tolerant control method is designed for an actuator fault intermittent process with simultaneous uncertainties for the system parameters. First, an intermittent fault tolerance controller is designed using 2D system theory, and the iterative learning control (ILC) intermittent process is transformed into a 2D Roesser model. Secondly, sufficient conditions for the controller’s existence are analyzed using the linear matrix inequality (LMI) technique, and the control gain matrices are obtained by convex optimization with LMI constraints. Under these conditions for all additive uncertainties for the system parameters and admissible failures, the controller can ensure closed-loop fault-tolerant performance in both the time and batch directions, and it can also meet the H∞ robust performance level against outside disturbances. Eventually, the algorithm’s computational complexity is analyzed, and the effectiveness of the algorithm is verified by simulation with respect to an injection molding machine model. Compared with traditional ILC laws, which do not consider actuator faults, the proposed algorithm has a better convergence speed and stability when the time-invariant and time-variant actuator faults occur during implementation.

Improved Networked Iterative Learning Fault-tolerant Control Algorithm for Systems with Time-delays, Random Packet Losses, Limited Communication and Actuator Failure

Improved Networked Iterative Learning Fault-tolerant Control Algorithm for Systems with Time-delays, Random Packet Losses, Limited Communication and Actuator Failure

Iterative learning fault-tolerant control for the networked control systems with initial state disturbance

Iterative learning fault-tolerant control for the networked control systems with initial state disturbance

Iterative learning fault-tolerant control for discrete-time nonlinear systems subject to stochastic actuator faults

This paper is concerned with an iterative learning fault-tolerant control strategy for discrete-time nonlinear systems where actuator faults arbitrarily occur. First, the stochastic faults occurring in multiplicative and additive manner are considered. Then, statistical behaviors of both faults-corrupted control signals from the actuator to the plant and faults-free ones from the iterative learning controller to the actuator are analyzed. Meanwhile, sufficient conditions of convergence for the proposed strategy are established by resorting to the time-weighted norm technique. Finally, two numerical examples are provided to illustrate the effectiveness and reliability of the proposed results. Both theoretical analysis and simulations indicate that the developed strategy is satisfactory in preserving decent tracking accuracy of the addressed systems subject to actuator faults.

Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains.

In this article, a cooperative adaptive iterative learning fault-tolerant control (CAILFTC) algorithm with the radial basis function neural network (RBFNN) is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults by using the multiple-point-mass dynamics model. First, an RBFNN is utilized to cope with the unknown nonlinearity of the subway train system. Next, a composite energy function (CEF) technique is applied to obtain the convergence property of the presented CAILFTC, which can guarantee that all train speed tracking errors are asymptotic convergence along the iteration axis; meanwhile, the headway distances of neighboring subway trains are kept in a safety range. Finally, the effectiveness of theoretical studies is verified through a subway train simulation.

RBFNN-Based Adaptive Iterative Learning Fault-Tolerant Control for Subway Trains With Actuator Faults and Speed Constraint

In this article, a radial basis function neural network-based adaptive iterative learning fault-tolerant control (RBFNN-AILFTC) algorithm is developed for subway trains subject to the time-iteration-dependent actuator faults and speed constraint by using the multiple-point-mass dynamic model. First, the RBFNN is utilized to approximate the time-iteration-dependent unknown nonlinearity of the subway train system; then, the iterative learning mechanism is used to tackle the outstanding repetitive operational pattern of a subway train which runs from one station to the next strictly according to the operation timetable schedule every day within the finite time interval, and the adaptive mechanism is designed for dealing with the time and the iteration-varying factors of the subway train. Second, a barrier composite energy function technique is exploited to obtain the convergence property of the proposed RBFNN-AILFTC scheme for subway train system, which can guarantee that the tracking error is asymptotic convergence along the iteration axis, meanwhile keep the speed profile of the subway train system satisfies the constraint. Finally, a subway train simulation is shown to verify the effectiveness of the theoretical studies.

Finite frequency range iterative learning fault-tolerant control for discrete time-delay uncertain systems with actuator faults

The subject area considered is discrete linear time delay systems operating repetitively on a finite time interval with actuator faults, where the system resets at the end of each operation. Regulation of the dynamics is by iterative learning control and performance goals imposed over finite frequency intervals for the case of uncertainty in the dynamic model. To derive the results, the generalized Kalman–Yakubovich–Popov lemma is used. A simulation based case study is also given to demonstrate the applicability of the new results.

2D Fuzzy Constrained Fault-Tolerant Predictive Control of Nonlinear Batch Processes

Concerning actuator gain faults and strong system nonlinearity in batch processes, a design method of a kind of 2D fuzzy constrained model fault-tolerant predictive controller is proposed. Firstly, introduce two errors: state error and output error, after that the original system model is converted into a 2D Roesser fault model. Meanwhile, the design of iterative learning fault tolerant control under constraints has been transformed into the determination of the constrained update law. Subsequently, real-time on-line design of the fuzzy fault-tolerant update law that ensures the closed-loop system robustly asymptotic stability is presented by taking the appearance of linear matrix inequalities (LMIs) with constraint subject to the designed infinite optimization performance index and Lyapunov stability theory. Finally, taking a three-tank case as an example to compare with the 1D of the method proposed in this paper, the comparison results illustrated the 2D method of this paper has better control effects.

Iterative learning fault-tolerant control for networked batch processes with event-triggered transmission strategy and data dropouts

ABSTRACTThis paper studies the iterative learning fault-tolerant control (ILFTC) problem for networked batch processes with event-triggered transmission strategy and data dropouts. During the transmission of input signal, the event-triggered mechanism is adopted to reduce the number of updated data items. The data dropouts are assumed to obey the Bernoulli random binary distribution. The objective of this paper is to design a state feedback controller such that the system is fault-tolerant and satisfies the robust performance requirement. By combining 2D stochastic system theory and linear matrix inequality (LMI) technique, some sufficient conditions are given to ensure the existence of the designed controller. Finally, an example of nozzle pressure control is utilized to verify the availability of the proposed method.

Delay-Range-Dependent-Based Hybrid Iterative Learning Fault-Tolerant Guaranteed Cost Control for Multiphase Batch Processes

Concerning multiphase batch processes with delays, disturbances, and actuator faults, the design of 2D robust hybrid composite iterative learning fault-tolerant guaranteed cost controller is put forward. First, a hybrid iterative learning control law is introduced and the multiphase batch process with interval time-varying delays is converted to an equivalent 2D-FM switched system with actuator faults by the introduction of system output tracking errors and state errors and consideration of fault effects. Next, a sufficient condition for satisfying asymptotical stability that the closed-loop switched system has the upper bound of minimum performance index is established by application of the theoretical framework of 2D system and selection of 2D Lyapunov–Krasovskii function on the basis of an average dwell time method. Then, the optimal design algorithm of the 2D hybrid robust iterative learning fault-tolerant guaranteed cost control is presented. In the meantime, in view of the effects of delay terms on ...

Iterative learning fault-tolerant control for injection molding processes against actuator faults

The injection molding process is a typical multi-phase batch process. As the filling and packing-holding phases share the same actuator, faults occurring in the actuators may cause serious impact on the performance and running time. Because these two phases are of crucial importance in relation to the final quality of the product, to solve this problem is essentially meaningful. This paper proposes iterative learning fault-tolerant control (ILTFC) in terms of common multi-phase batch processes and then applies it to the injection molding processes. To develop the ILFTC design, the multi-phase batch process is treated as a switched system composed of different dimensional subsystems and then converted to an equivalent two-dimensional (2D) switched fault-tolerant Rosser model. A hybrid fault-tolerant law is then designed based on an average dwell time method. Sufficient conditions and minimum running time guaranteeing the exponential stability under both normal and fault conditions are obtained. Under the proposed control law, the control performance and running time will restore to the previous level before actuator faults occur. The efficiency and merits of the proposed scheme is illustrated by an injection molding process, and results show that it can guarantee the stability and minimum running time whether the process is in normal operation or in case of actuator faults.

Robust Iterative Learning Fault-Tolerant Control for Multiphase Batch Processes with Uncertainties

In this paper, the iterative learning fault-tolerant control problem for multiphase batch processes with uncertainty and actuator faults is studied. First, making full use of the characteristics of the two-time dimension (2D) feature and repetitiveness in batch processes and introducing the state error and output error between the adjacent batches, the established model is transformed into an equivalent 2D-Roesser switched system with different dimensions. Under the framework of the 2D system theory and by means of the average dwell time method, sufficient conditions ensuring the system to be 2D robustly stable along the time and batch directions and the minimum running time lower bound in each phase are given. Simultaneously, the designed updating law is derived. In order to examine the control performance of the proposed method, the traditional reliable control method is also investigated in this paper. The batch process is regarded as a continuous system, in which only the fault-tolerant control along ...

Iterative Learning Fault-Tolerant Control for Networked Batch Processes with Multirate Sampling and Quantization Effects

The fault-tolerant control problem is investigated for a class of networked batch processes with actuator faults and external disturbances. A two-dimensional Fornasini-Marchesini (2D-FM) system with multirate sampling and quantization effects is introduced to model the networked batch processes, which may reflect the reality more closely. The aim of this paper is to design a dynamic output feedback controller such that the closed-loop system can achieve fault tolerance with the effect of actuator faults and satisfy the H∞ performance constraint for unknown external disturbances. By employing a combination of the Lyapunov stability analysis theory, lifting technique, and logarithmic quantization method, a networked iterative learning fault-tolerant control (NILFTC) scheme is first proposed, and some sufficient conditions are established for the existence of the desired dynamic output feedback controller. Finally, an example is exploited to illustrate the effectiveness of the developed method.

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.

Iterative learning and adaptive fault‐tolerant control with application to high‐speed trains under unknown speed delays and control input saturations

This study investigates the speed trajectory tracking problem of high-speed trains with actuator failures and unknown speed delays as well as control input saturations. New adaptive iterative learning fault-tolerant control (AILFTC) strategy is derived without the need for precise system parameters or analytically estimating bound on actuator failures variables. It is shown that with the proposed method, both actuator failures can be accommodated and the unknown time-varying speed delays and control input saturations can be analysed by means of Lyapunov–Krasovskii function. As such, the resultant control algorithms are able to achieve the L [0,T] 2 convergence of the train speed to desired profile during operations repeatedly in the presence of non-linearities and parametric uncertainties, as validated by the theoretical analysis and numerical simulations.

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller (ILRGCC). A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞ performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities (LMIs), and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.

Delay-Range-Dependent Method for Iterative Learning Fault-Tolerant Guaranteed Cost Control for Batch Processes

This paper addresses the problem of delay-range-dependent iterative learning reliable guaranteed cost control of a class of batch processes with uncertainties, state delay and actuator failures, where the main contribution is in the relevant concept of H∞ iterative learning fault-tolerant guaranteed cost control based on an equivalent two-dimensional system description of these processes. The H∞ iterative learning reliable guaranteed cost controller (ILRGCC) is formulated to include a robust extended feedback control for ensuring the performances over time and an iterative learning control (ILC) for improving the tracking performance from cycle to cycle, such that it can not only guarantee the closed-loop convergence along both the time and the cycle directions but also satisfy both the H∞ performance index and a cost function preserving upper bounds for all admissible uncertainties and any actuator failures. To achieve the least guaranteed cost for the closed-loop system, a convex optimization problem wi...