Transactions of the Institute of Measurement and Control

Yang, Pu; Shen, Ziwei; Feng, Kejia; Ding, Yu

doi: 10.1177/01423312241248258pmid: N/A

This paper develops a novel fault-tolerant control based on an observer for underactuated robot manipulators with unknown external disturbances, uncertainties, and actuator faults. First, unlike the traditional approach of treating the robot system as a parameter time-varying system, the underactuated robot manipulator with different drive modes can be considered as one of the classical Markov jump nonlinear systems (MJNSs). Second, an adaptive disturbance observer is designed to estimate the state and disturbances of the system. Finally, based on the observation results, a nonsingular fast integral terminal sliding mode controller (NFITSMC) is utilized to implement fault-tolerant control of the system. Compared with traditional observer and terminal sliding mode controller, the adoption of the novel controller and observer can improve response time and reduce chattering. Especially, in order to eliminate chattering, the integral term is introduced into the nonsingular fast terminal sliding mode controller. Structuring Lyapunov–Krasovskii functional (LKF) and based on linear matrix inequalities (LMIs) techniques, the convergence of control strategy and tracking errors is proved. The simulation results show that the actuator faults can be observed successfully and the error system is finite-time stable.

Guan, Shiwen; Pang, Hongbo; Li, Chensong

doi: 10.1177/01423312241248723pmid: N/A

The paper investigates the finite-time adaptive consensus control of nonlinear multi-agent systems (MASs) by output-feedback. During the process of control design, the fuzzy logic system (FLS) is employed to approximate nonlinear function, as well as constructing a fuzzy state observer to estimate unmeasured states. Combining the dynamic surface control (DSC) with adaptive backstepping method conquers the complexity explosion issue. Then, semi-global practical finite-time stability (SGPFS) for closed-loop system is obtained under arbitrary switching communication topologies. All the signals of the overall system are uniformly ultimately bounded (UUB). Finally, the availability of developed control approach will be demonstrated through a simulation.

Zhou, Xin; Wang, Heng; Li, Qing; Wu, Libing

doi: 10.1177/01423312241249207pmid: N/A

This paper presents an adaptive path-tracking control of autonomous ground vehicles with sideslip effects and time-varying input delays. To handle the problem of unknown time-varying input delays, the vehicle kinematic model is transformed into a novel chain model, and a delay-based auxiliary integral term is introduced for the estimation of the unknown terms caused by time-varying delays. To avoid modeling errors caused by existing linear approximation methods, a new adaptive path-tracking control strategy is proposed, such that the optimal tracking performance is achieved by appropriately adjusting certain design parameters. In addition, the inherent complexity explosion issue is avoided with the aid of a boundary estimation strategy. All signals of the closed-loop systems are guaranteed to be bounded. Simulation results illustrate the effectiveness of the method proposed.

Xie, Yufang; Gao, Lijun; Yang, Suo

doi: 10.1177/01423312241248489pmid: N/A

This paper aims to investigate input-to-state stability (ISS) of nonlinear impulsive switched systems under mode-dependent event-triggered impulsive control (MDETIC) method. A novel mode-dependent event-triggered mechanism (MDETM) is devised, where triggering impulsive instants are determined by some predesigned event conditions. Different from existing MDETM, there are two types of impulsive behavior involved, including the impulsive behavior of system itself and event-triggered one. The novel MDETM not only reduces communication cost and energy consumption but also excludes “Zeno behavior.” Then, the ISS criteria of the closed-loop system are established based on MDETIC strategy, admissible edge-dependent average dwell time (AED-ADT), and admissible edge-dependent average impulsive interval (AED-AII) methods. Finally, two examples are given to verify the effectiveness of the main results.

Wang, Ruihua; Sun, Wenxu

doi: 10.1177/01423312241249830pmid: N/A

This paper mainly investigates the finite-time rate anti-bump switching (RABS) control problem for switched systems. A novel multiple convex Lyapunov function is first proposed by constructing a convex combination of positive definite matrices for the RABS control problem of switched systems. By imposing a prespecified dwell time on state-dependent switching, a combined switching law is devised based on the new Lyapunov function to ensure that Zeno phenomenon is prevented. Then, a bumpless control scheme is proposed to reduce the big and undesired jump in the rate of system at switching instants while achieving the disturbance suppression in finite time. In the end, through an ingenious Simulink model to characterize the combined switching law and finite-time H∞ rate anti-bump controller, an actual engine model is used to demonstrate the validity of the proposed approach.

Chen, Yunjun; Li, Xiunan; Li, Xiehuan

doi: 10.1177/01423312241247875pmid: N/A

This paper investigates the event-triggered sliding mode control (SMC) problem using the reduced-order method for high-order systems subject to perturbation. To reduce the complexity of control design, a reduced-order approach is introduced by retaining the dominant mode of the system state. In addition, by injecting a power term, a new controller is constructed that aims to decrease the partial system control chattering. An event-triggered mechanism is designed by utilizing a time-varying trigger threshold; it is obvious that the proposed event-triggered strategy has fewer triggering times, larger triggering intervals between two neighboring events, and conserves system communication resources. Then, the existence of a positive lower bound on inter-event time is ensured to avoid the Zeno phenomenon. The effectiveness of the proposed approach is verified by magnetic control systems.

Chong, Zhi Lin; Yeong, Wai Chung; Khaw, Khai Wah; Chew, XinYing; Teoh, Wei Lin

doi: 10.1177/01423312241249824pmid: N/A

The side sensitive group runs double sampling (SSGRDS) X¯ scheme is previously studied under the known process parameters (Case-K) assumption. As the process parameters are hardly known in practical situations, they must be estimated using a suitably chosen in-control (IC) Phase-I sample. However, various studies demonstrated that a substantial amount of Phase-I sample is necessary so that the scheme with unknown process parameters (Case-U) attains a performance almost similar to that of the Case-K counterpart. Since it is challenging to acquire a vast number of IC samples, we examine the optimal designs of the Case-U SSGRDS X¯ scheme such that the average number of observations to signal (ANOS) and expected ANOS (EANOS) are minimised. The optimal parameters obtained for the Case-U SSGRDS X¯ scheme enable it to have a performance comparable to that of the Case-K counterpart, without the need for a large number of Phase-I samples. We show that the Case-U SSGRDS X¯ scheme is effective in identifying the process mean shift of a silicon epitaxial process.

Degachi, Oumayma; Ouni, Kais

doi: 10.1177/01423312241252459pmid: N/A

Electrocardiogram (ECG) records a series of heart depolarization and repolarization of the atria and ventricles that manifest in waves. They are systematically deployed as a standard non-invasive tool to monitor the cardiac activity and reliably detect eventual heart diseases or any abnormal heart activity. To relief the medical staff of the burden of diagnosing long ECG records, classification algorithms have been tested and explored to give a new way for heartbeat failure detection. This paper proposes a method based on a convolutional neural network (CNN) to extract time-invariant features and a bidirectional long short-term memory (BiLSTM) network-based sequence-to-sequence (seq2seq) architecture augmented with an attention mechanism (AM) to classify the heartbeats into five classes according to the ANSI/AAMI/ISO EC57, 1998-(R)2008 standard. We also use the adaptive synthetic sampling (ADASYN) which is a data augmentation technique to reduce the bias caused by imbalanced data. Finally, to avoid skewing the classification results, we use the inter-patient paradigm diagnosis. For verification, we used the MIT-BIH arrhythmia database, the experiment achieved an accuracy rate of 99.87%. The evaluation results demonstrate that the proposed method obtains excellent performances for heartbeat classification task. The introduction of an AM improves the efficiency of the encoder–decoder architecture.

Türkmen, Gökmen Atakan; Çetin, Levent

doi: 10.1177/01423312241251391pmid: N/A

Untethered electromagnetic actuation becomes an appealing concept for developing applications in microscale motion control. Although actuator modeling is critical, there is a lack of inverse modeling methods for untethered electromagnetic actuators (EMA) for control design and implementation. Herein, we focused on a machine learning-based framework to obtain inverse models of untethered EMAs. The inverse model is defined as a model which takes a point in the workspace of EMA together with the magnetic field at that point as input and gives the current(s) and position(s) of electromagnets as output. To obtain the inverse model; initially, the Maxwell Equations are solved for the defined set of coil currents and electromagnet positions numerically. Then, the classification problem is defined by concerning the obtained magnetic field values as data and corresponding the input values (currents and positions) as labels. The Random Forest Classifier is trained to obtain an inverse model to match the given magnetic field vector at a position with input values. The proposed approach is employed for three common structures: Single, Double, and Quadruple EMA. The performance test showed that the obtained inverse model is capable of giving the required magnetic field with accuracy of 1.43% Moreover, experimental study shown that the obtained inverse model is also capable of simulating the real-time behavior of EMA systems.

Li, Mengjie; Li, Shaobao; Luo, Xiaoyuan; Fan, Yanyan; Guan, Xinping

doi: 10.1177/01423312241249818pmid: N/A

Platooning of vehicular systems has been considered as an effective solution for alleviating traffic congestion. However, the widely existing matched and mismatched disturbances can greatly affect the safety, efficiency, and comfort of the vehicular systems. In this study, to compensate for both the matched and mismatched disturbances while improving convergence performance, a distributed platoon control problem of vehicular systems is studied under the recursive terminal sliding mode control (RTSMC) framework. A finite-time disturbance observer (FTDO) based on the high-order homogeneous differentiator is proposed for accurate estimation of the matched and mismatched disturbances in finite time. Based on the nested sliding surface structure, an RTSMC scheme is developed for the platooning of vehicular systems. The salient features of the proposed control algorithm are that the fast convergence rate can be reached and the reaching phase is eliminated, which guarantees the safer, more efficient, and more comfortable platooning. Finally, simulations and experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm.