This paper studies the differential flatness of time-delay systems (TDSs). Based on factorizations in the pseudo-polynomials ring, a flat output for the single-input linear commensurate TDS (namely, the delays in the system are multiplies of a certain unit delay) is constructed explicitly. Then the TDS can be converted into a high-order fully actuated system (HOFAS) model with the generalized state as the flat output, and the finite spectrum assignment (FSA) problem can be solved immediately by using the HOFAS approach, which provides some new insight into the study of the FSA problem. By parameterizing the considered TDS via the constructed flat output and applying the interpolation theory, the state trajectory planning problem and state tracking problem are solved, resulting a two-degree-of-freedom (2DOF) controller. Numerical examples demonstrates the effectiveness of the presented approach.

A polynomial fuzzy fault detection scheme for sampled-output-measurements-based interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) systems is investigated in this paper, where the uncertainties in the premise variables (PVs) and membership functions (MFs) are described by IT2 fuzzy sets. Fully or partially unmeasurable PVs cause the parameter matrices of the polynomial fuzzy fault detection observer (PFFDO) to rely on the estimated states and the corresponding mismatching problems are further considered. Lyapunov stability theory is carried out with a novel multi-order homogenous polynomial Lyapunov functions (MHPLF) to introduce more information of the states when eliminating the partial derivatives, and the time-delays introduced by sampled-output measurements are handled by L-K functions. Unlike the membership-function-independent (MFI) approaches, the membership-function-dependent (MFD) approaches carry the information of the MFs for the relaxation of the stability constraints. Corresponding stable constraints in sum-of-squares (SOS) form are given to hold the asymptotic stability of the fault detection system with H∞ performance γ. A numerical example with many cases illustrates the effectiveness of the proposed techniques in uncertainty handling and conservativeness reduction, while an inverted pendulum example verifies the feasibility of the method on physical systems.

The integrated fault estimation and fault-tolerant control scheme is developed in this article for a series of interval type-2 polynomial fuzzy systems with both sensor faults and actuator faults, where the bidirectional influence between fault estimation unit and fault-tolerant control unit is investigated. Considering the existence of sensor faults, unmeasurable premise variables are investigated for more general situations and Class III state/fault estimation observers are established for the final fault estimation and fault-tolerant control purposes. To increase design flexibility and reduce physical implementation complexity, the proposed method allows the observer and original system to share asynchronous membership functions and a different number of fuzzy rules. $(\mathcal {Q,S,R})-\alpha$ dissipative performance index is also introduced to fulfill a wider vary of performance requirements. Membership function-dependent stability constraints are given in the format of bilinear polynomial matrix inequalities to obtain less conservative results, which are computed by a two-step path-following method. Superiority and validity are demonstrated by an inverted pendulum example in terms of estimation errors, fault-tolerant control performance, and control inputs.

Due to their exceptional capacity to extract features, methods based on deep learning have demonstrated good performance in industrial vision inspection applications. However, the generalizability and robustness of inspection models are challenged by factors such as insufficient data, great differences in data types, and complex illumination. To address these problems, multiscale feature correlation perception network (MFCP-Net), a semantic segmentation model with variable topology, is proposed to segment images with prior background. MFCP-Net uses a Siamese structure with shared weights to extract the multiscale features of the template and test images. A feature correlation perception (FCP) block is designed to measure the correlation of latent representation between the template and test images at multiscales, using background information more effectively. In addition, MFCP-Net provides different topological structures for the training and testing phases. A background auto-correlation branch is included during the testing phase to eliminate false positives observed in the background. MFCP-Net was compared with the state-of-the-art models on the proposed automobile instrument detection system, demonstrating that it offers superior accuracy and generalizability, especially in the case of insufficient data and big variances across data.

This paper investigates the resilient sliding mode control problem for cyber-physical systems (CPSs) with multiple transmission channels under denial-of-service (DoS) attacks. A set of finite-time observers is designed, and a switched integral-type sliding surface is introduced. Thus, the impact of unreliable state estimating channels is reduced, and the disturbance rejection performance is also improved. The number of linear matrix inequalities (LMIs) decreases compared with some existing results in designing the observer-based controller, and the input-to-state stability (ISS) is guaranteed. Moreover, the input saturation and event-triggering scheme are considered in the controller and handled by an auxiliary system. The network congestion in the control channel is thus relieved, and the Zeno behavior is excluded simultaneously. Finally, an example of an unmanned stratospheric airship is given to demonstrate effectiveness of the proposed resilient control approach.

This paper investigates the observer-based resilient sliding mode control problem for cyber-physical systems (CPSs). In multiple transmission channels under aperiodic denial-of-service (DoS) attacks, a decentralized dynamic event-triggering scheme is designed to reduce the network transmission burden. To construct such a periodically sampled event-triggering scheme, a set of finite-time observers is designed to obtain estimated system states. On the controller side, to obtain higher control reliability under DoS attacks, decentralized observers are correspondingly arranged in these channels. In this process, an exponential stability condition is obtained using a piecewise Lyapunov–Krasovskii functional approach. The stability conditions of linear matrix inequalities (LMIs) are given, and the computational complexity is much less compared with the counterparts in some existing results for multiple channels. To further improve the disturbance rejection performance, a weighed integral-type sliding surface is introduced, and the impact of unreliable state estimating channels is reduced. An auxiliary system is also designed to handle the input saturation, and the closed-loop system under DoS attacks is asymptotically stable. Finally, an example of an unmanned aerial vehicle is given to verify the effectiveness of the obtained resilient control approach.

Since there are many kinds of automobile instruments and the industrial environment is usually complex, automatic instrument detection during automobile production test becomes a challenging task. This article presents an automobile pointer instrument detection method based on prior information and fuzzy sets. The proposed method consists of two frameworks built around a pointer meter prior information model (PMPIM). The first one targets PMPIM construction to obtain the required prior information. With this purpose, a pointer-free template is obtained from a template generation algorithm and pointer positions are mapped into an energy function for optimization, using an energy function-based pointer positioning algorithm. The energy function is defined based on the distance between the crisp and fuzzy sets. The second framework targets PMPIM utilization to detect pointer meters during production test. A fuzzy-based image enhancement method is proposed to enhance test images and the template simultaneously. A prior information and energy function-based pointer positioning algorithm is also proposed to locate pointers in test images. Finally, the indicator value (the value the pointer points to) is calculated according to the positions of the pointer and scale marks. Experimental results show that the proposed method achieves better generalization and robustness than existing state-of-the-art methods.

As a critical interface between humans and automobiles, the precision of automobile instruments is directly related to safety. During production testing, the check for automobile instruments is essential. Considering that a manufacturing line is necessary to detect various types of instruments frequently, this article proposes a unified and generalized detection framework for pointer meters. The proposed framework innovatively gives the corresponding solutions in view of the existing problems for each subtask. First, a pointer-free template is established by pixelwise background modeling. Then, the two-stage similarity measurement based on fuzzy theory and image reconstruction is designed to enhance images and emphasize pointers, boosting the generalization of the detection framework. For the pointer positioning, the edges of pointers are fit by the set-to-set distances introducing the directionality and intensity of pointers to improve the robustness. For the scale marks’ positioning, the spatial periodicity of scale marks is creatively introduced to address the challenges caused by noises and the connected scale marks. Finally, the indicator value (the value that the pointer points to) is calculated according to the obtained positions of the pointer and scale marks. Experimental results show that the proposed detection framework can reliably detect various types of pointer meters, even under complex illumination conditions.

This paper is concerned with the event-triggered asynchronous fault detection (FD) problem for Markov jump systems with partially accessible hidden information and subject to aperiodic denial-of-service (DoS) attacks. The hidden Markov model with partially unknown probabilities is introduced to characterize the asynchronous phenomenon between the system and the filter, where the partially unknown probabilities may exist in the transition rate matrix of Markov chain, the conditional probability matrix of detected signal, or in both of them. In order to save the limited network bandwidth while resisting the aperiodic DoS jamming attacks, a new resilient dynamic event-triggered communication strategy is devised. Then, a new switched residual model for asynchronous FD is formulated by comprehensively considering the effects of the event-triggered scheme, asynchronization, and DoS attacks. By means of this model combined with the piecewise stochastic Lyapunov-Krasovskii functional approach, sufficient conditions are derived to guarantee the stochastic stability of the resulting switched residual system with desired dissipativity performance. Based on convex optimization techniques, an explicit expression of the desired asynchronous FD filter is derived. Finally, a single-link robot arm and a mass-spring system model are utilized to demonstrate the effectiveness of the proposed design technique. (C) 2022 Elsevier Inc. All rights reserved.

Compared with integral-order calculus, fractional calculus is better at depicting the real process with memory property and history-dependent property. As a result, this work investigates a class of nonlinear strict-feedback fractional-order systems and presents a novel control strategy. To begin, in order to cope with the unknown drift functions and unmeasurable system states, fuzzy logic systems (FLSs)and a robust fractional-order state observer are designed. Second, in order to further reduce the FLS approximation error and state estimation error, a hyperbolic tangent function is implemented. Third, the problem of the differential explosion caused by repeated differentiation when employing the backstepping technique when designing a control scheme is also overcome without the help of a command filter or dynamic surface control. Finally, theoretical analysis and simulation results show that by combining the backstepping procedure with the sliding mode technique, not only is it possible to achieve strong robustness against unknown drift function and unknown external time-varying disturbance, but also that the tracking error can converge to the vicinity of the origin.

The paper investigates the backstepping robust control problem for a class of nonlinear networked systems. The non-Lipschitz conditions of linear growth condition and Holder condition are considered in the time-varying disturbance and uncertainty. An important innovation is that the virtual controller is designed to be piecewise constant such that the usage of the command filter is avoided. In the process of designing the robust controller, a semi-synchronous event-triggered scheme is proposed to relieve the communication pressure in high-order systems. Thus, a switched controller without using average dwell time (ADT) is considered. The finite-time practical stability with the form of fast terminal sliding mode (TSM) is also obtained. Then, the Zeno behavior is excluded and a self-triggered scheme is further proposed to avoid the continuous monitoring of the event-triggered conditions. Compared with some existing command-filtered results, the design process is simplified and more suitable for networked systems. The effectiveness of the proposed theory is validated by the numerical simulation of a hypersonic vehicle. (C) 2022 Elsevier Inc. All rights reserved.

The paper investigates the finite-time state and fault estimation problem for continuous-time descriptor switched systems. A more representative faulty plant is considered, where unknown additive sensor and process faults, the Lipschitzian disturbance in state dynamics, and the external composite disturbance in the measured output exist simultaneously. Compared to some existing actuator models, a more representative actuator fault model is given, where some bounded conditions of the degradation and the additive fault are canceled. The actuator empirical efficiency is also introduced to estimate the time-varying actuator degradation. An important highlight is the extension of the Lyapunov terminal sliding mode (TSM) condition of finite-time stability into descriptor switched systems, where the method of average dwell time (ADT) is considered. A novel adaptive sliding mode observer is developed by constructing an augmented system model, and the relevant rank conditions are proved. To validate the effectiveness of the proposed observer, a descriptor switched circuit system is simulated under different control inputs and various actuator fault models. It can be seen that the TSM finite-time state and fault observer acquires the fast and accurate estimation of the targeted vector. (C) 2021 Elsevier Inc. All rights reserved.

This paper studies event-triggered fault detection filter (FDF) and controller coordinated design for delta-operator-formulated Networked Control Systems (NCS) with high-speed sampling. By considering event-triggered scheme and time-varying network-induced delays, a networked residual system based on delta operator is established. Event-triggered asymptotic stability conditions and H∞ performance analysis of the constructed system are presented by using the Lyapunov-Krasovskii functional approach in delta-domain. A sufficient condition for the solvability of FDF and controller is derived in terms of linear matrix inequalities. The desired FDF and controller can maximize the sensitivity of the residual signal to the fault signal and guarantee the robustness of the networked system to external disturbances. Finally, a numerical example is provided to demonstrate the efficacy of the proposed method.

This paper investigates the sliding mode control for high-frequency sampled-data systems with actuator faults. Besides matched nonlinearity, this paper also considers unmeasurable states and unknown actuator degradation ratio as important factors of the overall system. The estimates of system state vector are obtained by an adaptive sliding mode observer method firstly. Then, a novel integral-type sliding surface, corresponding to the unified closed-loop delta operator system, is provided based on aforementioned estimation values, and the fault closed-loop system is proven to be stable by the proposed sliding mode control law. Finally, the fault-tolerant control theory is verified to be valid via a practical simulation example.

This paper investigates the adaptive fault-tolerant control problem for a class of continuous-time Markovian jump systems with digital communication constraints, parameter uncertainty, disturbance and actuator faults. In this study, the exact information for actuator fault, disturbance and the unparametrisable time-varying stuck fault are totally unknown. The dynamical uniform quantizer is utilized to perform the design work and the mismatched initializations at the coder and decoder sides are also considered. In this paper, a novel quantized adaptive fault-tolerant control design method is proposed to eliminate the effects of actuator fault, parameter uncertainty and disturbance. Moreover, it can be proved that the solutions of the overall closed-loop system are uniformly bounded, which is asymptotically stable almost surely. Finally, numerical examples are provided to verify the effectiveness of the new methodology.

In this paper, the H-infinity filtering problem is investigated for a class of discrete-time Markovian jump nonlinear systems with partly unknown transition probabilities and subject to sensor saturation over unreliable communication. The description of researched plant includes global Lipschitz nonlinearities and state-dependent random noise and external-disturbance. A decomposition approach is used to deal with the characteristic of sensor saturation. Since the communication links between the plant and filter lack enough reliability, the effects of output quantization and data packet losses should both be considered. The proposed quantizer's parameter is on-line updating and the corresponding practical adjusting rule can ensure the dynamic performance of the controlled system. Among different operation modes, the cross coupling between system matrices and Lyapunov matrices is disposed by introducing proper slack matrix variables. The purpose of this work is to design a full-order filter based on incomplete output measurements in order to guarantee the stochastic stability of the estimation error. Precise expression of the filters and related analysis are depicted in this paper. Finally, a numerical simulation is provided to show the effectiveness of the designing filtering method. (C) 2018 Elsevier B.V. All rights reserved.

This paper investigates the adaptive fault-tolerant control problem of a class of Markovian jump systems with high frequency sampling. In this design, matched non-linearity, unknown actuator degradation factor and unmeasurable states are considered in a unified framework. First, an adaptive sliding mode observer approach is developed to obtain the estimates of system state vector. Second, based on the state estimation, an integral-type sliding surface for the overall closed-loop delta operator system is presented, and a sliding mode control law is proposed to guarantee the stochastic stability of the fault closed-loop system. Finally, a simulation example is provided to verify the effectiveness of the designed fault-tolerant control method.

This paper concentrates on the estimation of system states and the reconstruction of disturbances in a class of nonlinear systems considering stateless situation. The disturbances are coupled with time-varying parameters. The sliding mode observer approach is utilized to solve these two issues. Firstly, a descriptor model is presented by transforming the coupled disturbances into the decoupled form. Secondly, the sliding mode observer is designed to estimate the decoupled disturbances and system states of nonlinear systems. The coupled disturbance can be reconstructed hereafter. The detailed methods of designing the observer, together with the sufficient condition to guarantee the existence of the observer are also given. Finally, a simulation and a robot manipulator experiment are provided to examine the validity of the proposed design approach.

In this paper, the event-triggered fault-detection problem is investigated for Itô-type stochastic systems. A filter structure is used to construct a residual model for fault detection. For limited network resources, a novel event-triggered strategy is proposed, while a trigger condition is utilized to determine and transmit the useful data through the network. Compared with the traditional periodic release strategy, the proposed design approach can significantly reduce the utilization of network bandwidth. A new condition is proposed to achieve the mean-square asymptotical stability of the residual model with the desired fault-detection objective. A novel algorithm is derived to obtain the parameters of the filter and the event detector. Two simulation cases are provided to illustrate the effectiveness of the design strategy.

There are many space servicing tasks for space robots. With a single end effector, the space robot even can not meet the operation requirements of individual task. The space robot system has a self-relocating main manipulator arm and a dexterous robot arm to perform the tasks. To mount/de-mount payloads and tools, interfaces between the end effector and payloads/tools are standardized using grapple fixtures. An end effector and its grapple fixtures constitute an interchangeable device. According to the requirements, three interchangeable devices have been developed based on the different alignment features. Their comparisons indicate that the latest interchangeable device is for the main manipulator and also for the dexterous manipulator with the second.