Accurate robotic external force estimation is fundamental to sensorless physical human-robot interaction (pHRI), as it enables robots to interact with environments compliantly and safely. While the Kalman filter-based generalized momentum force estimation method (KF-GM) is widely adopted, its static covariance matrices and Gaussian noise assumption constrain adaptability and robustness, degrading estimation accuracy. This paper proposes a novel Hybrid Adaptive-Robust Kalman Filtering Approach (HARKF) integrating adaptive Kalman filter (AKF) and robust Kalman filter (RKF), with real-time covariance adjustment and outlier rejection, substantially improving adaptability and robustness. However, the fusing of AKF and RKF introduces inherent inter-filter coupling interferences, significantly compromising estimation accuracy due to incompatible noise adaptation mechanisms. Therefore, a noise-type-based module decoupling scheme and a parameter transfer mechanism are proposed, establishing synergistic collaboration between AKF and RKF, where their complementary mechanisms enable reciprocal reinforcement. The decoupling scheme eliminates cross-coupling through noise characteristic analysis, thus preserving system adaptability while enhancing disturbance robustness, resulting in significantly enhanced force estimation accuracy. The transfer mechanism resolves inter-filter parameter conflicts, thereby considerably improving filtering continuity and estimation robustness. Experimental results indicate that compared with existing Kalman filter-based methods, HARKF exhibits superior force estimation accuracy across diverse interactive scenarios.

Operational efficiency of placement machines constrains the overall production capacity of printed circuit board (PCB) assembly lines. Existing state-of-the-art algorithms face challenges, such as conflicts between multiple objectives and coupling within different problems. This article proposes a multiobjective hybrid evolutionary multitasking algorithm (MOHEMTA) to address PCB assembly optimization in beam-head placement machines. The algorithm divides the problem into pickup and placement tasks, leveraging implicit parallelism to enhance solution efficiency. A nozzle block encoding method and heuristic decoding strategies with domain knowledge are introduced to reduce encoding complexity and accelerate algorithm convergence. MOHEMTA enhances offspring population diversity and quality through an elitist strategy, evolutionary operators, and knowledge transfer mechanisms, while incorporating safeguards against negative transfer. Experiments demonstrate that the multiobjective solution performance and practical results of MOHEMTA are better than those of other state-of-the-art algorithms.

Automatic Optical Inspection (AOI), as a core equipment in quality inspection process of printed circuit board (PCB) assembly lines, directly impacts overall production capacity through its inspection efficiency. However, existing research on AOI route scheduling problem exhibits limitations such as neglecting image acquisition center adjustment and lack of efficiency for large-scale PCBs. A hierarchical heuristic algorithm based on neighborhood search is proposed to address large-scale AOI route scheduling. The problem is decomposed into component clustering, path sequencing, and image acquisition center adjustment. The method features adaptive neighborhood construction through search area adjustment, enabling component clustering via neighborhood operations. Cluster centers are then sequenced using the Lin-Kernighan algorithm. A greedy heuristic algorithm for image acquisition center adjustment is further developed to optimize path length. Experimental results demonstrate that this algorithmic framework outperforms state-of-the-art methods, particularly showing significant improvements in solving large-scale problems.

This paper proposes a novel state/fault estimation observer for uncertain polynomial fuzzy systems (PFSs) with unmeasurable premise variables (PVs), where the uncertainties in PVs and membership functions (MFs) are modeled by interval type-2 (IT2) fuzzy systems. To handle the effect of unmeasurable PVs caused by inherent uncertainties and sensor faults (SFs), compensation vectors are employed, thereby eliminating the need for restrictive assumptions on system stability or the linear growth conditions of these effects. Furthermore, taking the singularity problem in the estimation of actuator fault (AF) into consideration, a fully mismatched observer is constructed and eliminates the need for special structure of the traditional methods while enhancing the design flexibility simultaneously. In the stability analysis process, a one-step method is proposed to simplify the computational complexity compared to path-following techniques while introducing a trade-off in conservativeness, and a membership-function-dependent (MFD) theorem is given to reduce the conservativeness. Finally, a single-link rigid robot arm is introduced as an simulation example to verify the effectiveness of the proposed method.

This paper studies the fault estimation problem for polynomial fuzzy systems with unmeasurable premise variables. Considering the limitations of existing methods that require the convergence of the original system, a novel augmentedstate observer is proposed for polynomial fuzzy systems. Unlike compensation-vector-based approaches, the proposed method addresses the singularity problem and eliminates the traditional linear growth assumptions on unmeasurable premise variables, while only existence of the corresponding upper bounds rather than knowledge of their specific values is assumed. Moreover, the proposed method enables fully mismatched design, thereby enhancing both design flexibility and computational efficiency. Finally, the effectiveness of the proposed method is demonstrated through a bridge crane system as a case study.

The fuzzy adaptive state and output quantization bilateral cooperative control problem for nonlinear multiagent systems (NMASs) is studied. Since the considered system is nonlinear, fuzzy logic system (FLS) is applied to approximate the unknown nonlinear function, and a fuzzy state observer is constructed because the state cannot be measured. A second-order command filter is used to solve the complex problem of calculating the time derivative of the virtual control function, and a uniform quantizer is used for fuzzy adaptive inversion design in the process of controller design. Ultimately, the effectiveness of the proposed control method is verified by a series of simulation experiments and research results.

Route scheduling for automatic optical inspection (AOI) of printed circuit boards (PCBs) impacts the productivity of surface mount production lines. Current state-of-the-art mathematical models in the area are not rigorous enough and neglect significant practical constraints, such as component geometric constraints. This article proposes a hierarchical mixed integer programming model to describe the route scheduling problem for AOI of PCBs. The model allows theoretical optimal solutions to be obtained for small-scale problems. In addition, a two-stage heuristic framework, consisting of clustering and path planning stages, is proposed to improve efficiency in solving large-scale problems, achieving near-optimal solutions. Taking into account that component distribution affects clustering results, the clustering stage is developed with a hierarchical heuristic algorithm based on block density with an aggregation strategy. The Lin–Kernighan algorithm is first used to quickly generate the scheduling sequence in the path planning stage. Image acquisition centers are initially adjusted with a customized heuristic. After that, a hybrid evolutionary multitask algorithm is proposed to further reduce path distance by dividing the image acquisition center adjustment task into several subtasks using heuristic rules. The algorithm obtains better quality results and is faster than traditional evolutionary algorithms. Experiments on an actual industrial AOI platform demonstrate that the proposed two-stage heuristic route scheduling algorithm outperforms state-of-the-art research in the area.

Structured light imaging is a typical technique for industrial 3-D microscopic measurement. Extensive research on structured light codecs has been conducted to accurately correlate camera and projector pixels. However, these methods suffer significant degradation when measuring low-reflectivity and complex surfaces. This article introduces a deep correlation-based cascade structured light network (CasSLNet) that utilizes deep phase and column features to calculate correspondences at the subpixel scale. To mitigate the huge computational cost of full correlation, a coarse-to-fine approach is proposed. Specifically, multiscale features from the camera observation sequence and the 1-D encoding pattern are extracted through a pseudosiamese network, and cascade cost volumes are constructed. An initial column map is then regressed from the low-resolution column cost volume. Based on this, an iterative update operator is introduced to refine initial estimates, resulting in a full-resolution column map. Furthermore, a structured light dataset has been collected and experiments have been conducted on a typical structured light imaging platform. Experimental results demonstrate that CasSLNet outperforms both traditional and state-of-the-art deep learning-based methods.

Dual-gantry surface mount optimization effectively improves the productivity of printed circuit board assembly (PCBA), but also brings new challenges. Optimizing workload allocation to balance the front and rear gantry placement completion time is a significant challenge for improving PCBA productivity. This study proposes a finite potential game heuristic algorithm (FPGHA) to solve the workload allocation problem. The algorithm generates game agents by analyzing the feeding characteristics of the dual-gantry placement machine and using an improved bisection K-means clustering method. Agent utility is calculated based on metrics affecting productivity of the pick-and-place process, including the number of simultaneous pickups, nozzle changes, cycles, and mounting points. Nash equilibrium of FPGHA is obtained by a best-response dynamics and heuristic algorithm. Then, the effectiveness of FPGHA in solving the workload allocation problem is first demonstrated in simulated experiments with different nozzle and feeder configurations. Finally, FPGHA is compared with the hierarchical restricted balance algorithm, adaptive clustering algorithm, and the popular industrial optimizer software in actual placement experiments using real-world industrial printed circuit boards. The effectiveness and accuracy of FPGHA are verified by analyzing the correlation between three variables: The FPGHA estimated value, the actual assembly value, and the PCB assembly time.

This article focuses on the co-design of fault detection (FD) and time-varying formation control for a nonlinear multiagent system (MAS) over a signed switching digraph. The interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy model is utilized to represent the nonlinearities and parameter uncertainties, while a Markov process describes a signed digraph indicating possible environmental changes. To further handle the co-design problem over a signed digraph, the equivalence between FD with time-varying formation control and FD with a bipartite time-varying formation protocol is first established. Then, the sufficient conditions of stochastic stability are derived based on a mode-dependent Lyapunov function. It can be proven that the formation error is uniformly ultimately bounded, and the FD performance complies with a dissipative index. Finally, simulations of two-link robotic arm systems are performed to validate the effectiveness and feasibility of the proposed approach.

This article addresses the asynchronous fault detection (FD) problem for fuzzy hidden Markov jump systems with generally hybrid probabilities under limited communication. To reduce network load, a novel bandwidth-aware dynamic event-triggered communication scheme (DETCS) is developed to transmit necessary sampled signals, where the threshold coefficient in the triggered protocol can be dynamically adjusted over time in accordance with both the system dynamics and bandwidth status. A multiple-hierarchical structure is constructed to simultaneously describe both the mismatch of premise variables due to the introduction of the DETCS and the mode asynchronization between the filter and the plant, where the mode asynchronization is fully characterized by a hidden Markov model with generally hybrid transition probabilities and mode detection probabilities. By applying the double variables-based decoupling principle and variable substitution principle, a co-design criterion for DETCS and optimal$\mathscr {L}_{\infty }/\mathscr {H}_{\infty }$asynchronous reduced-order FD filter in the finite-frequency domain is derived and given by an exact expression. Besides, unlike the constant thresholds in existing FD works, an innovative zonotope-based dynamic threshold strategy is developed for residual evaluation. Finally, two illustrative examples are utilized to verify the effectiveness of the proposed method.

In this article, by using the output feedback information, an adaptive fuzzy decentralized controller for a class of nonlinear large-scale cyber–physical systems (CPSs) is developed. The considered system encompass unmeasurable states and are susceptible to denial-of-service (DoS) attacks. The approximations of unknown nonlinear functions are achieved through the utilization of fuzzy logic systems, and a switching-type fuzzy state estimator is proposed to obtain the estimations of unavailable system states. Through the application of adaptive backstepping design mechanism and the dynamic surface control method, an adaptive fuzzy output feedback decentralized controller is ultimately proposed. By utilizing a combination of Lyapunov stability theory and average dwell time, it is demonstrated that the proposed adaptive fuzzy decentralized resilient controller ensures that the tracking errors converge to a small bounded neighborhood under the circumstance of DoS attacks, and all signals of CPS are bounded. The efficiency of the proposed approach is validated through simulation studies of a nonlinear inverted pendulum large-scale system.

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 paper for a series of interval type-2 polynomial fuzzy systems with both sensor faults and actuator faults, where the bi-directional 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 perfor-mance requirements. Membership-function-dependent stability constraints are given in the format of bi-linear 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.

This article studies the problem of event-triggered fault detection (FD) for 2-D systems subjected to amplitude-bounded exogenous disturbance and measurement noise via a zonotopic residual evaluation mechanism. An event-triggered mechanism is introduced into the FD framework to save limited communication resources. A finite-frequency (FF) mixed$\ell _{\infty }/h_{\infty }$index is derived to ensure the residual signal is sensitive to a fault signal while robust to disturbance and noise, based on which an optimal mixed$\ell _{\infty }/h_{\infty }$FD filter design criterion is provided. Instead of constant thresholds, novel zonotope-based dynamic thresholds are utilized for residual evaluation. Finally, simulation results are presented to illustrate the effectiveness of the developed mechanism.

This paper is concerned with the problem of reliable finite frequency (FF) control for a cloud-aided active quarter-car vehicle suspension system subject to actuator failure. Instead of considering the entire frequency domain, we study the FF control which could restrain the external vibration input more effectively in the concerned frequency domain. In order to improve network bandwidth utilization, an adaptive hybrid event-triggered scheme is proposed for the cloud-aided quarter-car suspension framework. A fault-tolerant controller is designed with considering the communication time-delay and event-triggered mechanism. The adaptive event-triggered condition and the reliable controller are co-designed. Finally, a quarter-car active suspension model is studied to illustrate the efficient performances of the designed controller and the proposed event-triggered scheme.

This article investigates the fault-tolerant control for attitude tracking problem of flexible spacecraft in the presence of actuator degradation, external disturbance, and signal quantization, where a logarithmic encoder-decoder scheme is employed for input quantization. A quantized adaptive terminal sliding mode control law is proposed to solve the attitude tracking problem. In this design, the quantizer parameters are injected to the controller gains to reject quantization errors, and the designed controller can compensate the effects of the unknown information, including actuator efficiency factors, external disturbance, and bounds of rigid-flexible coupled nonlinearity. It is shown that the attitude of the spacecraft can track the desired objective under the developed attitude controller. Finally, a simulation example is provided to verify the validness of the proposed attitude tracking control law.

Computer vision based detection approaches are widely employed to detect or calibrate different types of meters nowadays. However, traditional detection algorithms suffer drawbacks in accuracy and adaptability upon detecting various types of automobile dashboards. Plenty of parameters of these algorithms need to be tuned to suit certain types of dashboards. Besides, theses algorithms cannot automatically read the speed value, which requires manual setting operations. In this paper, a novel approach is presented to adaptively detect different types of automobile dashboards. The contour analysis based method is first implemented to extract the connected component of the pointer. A robust character segmentation classifier, which is designed by cascading histogram of oriented gradients (HOG)/support vector machine (SVM) binary classifier, character filter as well as HOG/multiclass SVM digit classifier, is then proposed to recognize digit characters on the dashboard. Simultaneously, tick marks are then extracted based on recognition results. Finally, Newton interpolation linear relationship is established to diagnose the potential responding errors of the pointer. The experimental results show that the pointer extraction method is robust to interferences caused by connected components of digits and also that the established character segmentation classifier has a more accurate detection result. Furthermore, compared with similar algorithms, it has a significant advantage in detecting a vast majority of different dashboards without manual tuning of the parameters.

This paper is concerned with the event-triggered fault detection filter (FDF) design for an unmanned surface vehicle (USV) under the network environment. A framework of fault detection is established for a USV subject to wave-induced disturbance and actuator failures, in which an FDF is utilized to construct a residual model and an integral-based event generator is introduced to save communication resources. Compared with the traditional instantaneous value based event-triggering scheme and periodic sampling, the proposed event-triggering mechanism can not only reduce bandwidth utilization of the network more significantly, but also get rid of the Zeno phenomenon fundamentally. The event-triggering scheme and the FDF are co-designed. Finally, the efficient performance of the proposed fault detection method based on integral-based event-triggering scheme is illustrated by simulation.

This paper discusses adaptive event-triggered fault detection filter design for fuzzy stochastic models with missing measurements. First, a novel event-triggered strategy is introduced, while an adaptive law is provided to adjust communication threshold dynamically. Compared with traditional event-triggered methods with fixed threshold, the proposed strategy is more effective on saving network communication resources. Second, a Bernoulli stochastic process is proposed to describe the measurement missing phenomenon, which always appears in real network environment. Then, an integrated fault detection model for fuzzy stochastic systems is constructed by taking network-induced delays, adaptive event-triggered strategy and missing measurements into account. A new method is provided to achieve mean-square asymptotical stability of residual model with one desired fault detection objective. Finally, simulation cases are introduced to verify the validity of the designed strategy.

This paper investigates the coupled disturbance reconstruction, state estimation and trajectory tracking problems for a class of nonlinear systems. The sliding mode observer and the state feedback approaches are utilized to solve these problems. Considering only a partial of the system states are measured but the disturbances couple with time-varying parameters that the observer is designed by transforming the original system into a descriptor one. Hence, from the estimated values of system states and the decoupled disturbance, one can reconstruct the coupled disturbance. Upon these estimates, a state feedback based fault tolerant controller is designed such that the system states converge to the desired trajectory. Finally, to verify the validity of our scheme, a numerical simulation together with an experiment of 3-DOF robot are offered.

This investigation mainly deals with positioning operation of the electric servo system and an adaptive sliding-mode-based controller via the output feedback scheme is derived for the servo actuator in the presence of the unknown model, nonlinear input, dynamic nonlinear friction and disturbances. The designed controller based on sliding-mode approach includes a LuGre model-based friction state estimator that ensures the cancelation of the friction, and a set of model parameter estimation algorithms for the servo actuator, which are derived in the sense of Lyapunov stability theorem. In addition, in response to uncertainties and disturbances with model or not in practical applications, especially for the unknown bounded ones, the adaptive law is presented to guarantee the system's robustness. Also, the chattering problem in sliding-mode control is confronted based on the fuzzy logic. In the end, the simulation examples under the continuous trajectory commands verify the robustness and performance of the proposed control strategy.

This paper deals with the problem concerning the design method of synchronous motion controllers in form of output feedback for dual linear actuators with load differences, dynamic nonlinear friction and force ripples. The authors focus on not only nonlinear friction and disturbance but also the dual motors synchronous objective. The energy upper bound for conquering disturbances is estimated and used as compensation. After that, in order to improve the robustness of the dual-motor motion plant in the presence of external disturbances, an interference rejection approach is presented. Furthermore, due to load variation which degrades synchronous tracking performance for dual motors, the controller design method based on the convex optimization scheme is proposed. The illustrative examples show that the controller can significantly improve the tracking performance under nonlinear frictions. In the meanwhile, the disturbances with known model and random one can be restrained well.

In the article, the observer-based fault detection approach is studied for the linear time-invariant system (LTI). The main idea of this technology is designing a constant observer gain for the purpose of the generated dynamic residual system is asymptotically stable and satisfies the desired property, simultaneously. A new dynamic observer structure is designed in this article by changing the observer gain basing on the error value of the measurement output of the physical model and the estimation output of the observer. With the extra adding design degrees of freedom, the proposed dynamic observer provides a sufficient condition to solve the fault detection and estimation problem at the same time, and the new dynamic observer design problem can be formulated as a linear matrix inequality (LMI) feasible problem. A numerical example is provided to illustrate the proposed procedures.

This paper presents the problem of finite-time stabilization for vehicle suspension systems with hard constraints based on terminal sliding-mode (TSM) control. As we know, one of the strong points of TSM control is its finite-time convergence to a given equilibrium of the system under consideration, which may be useful in specific applications. However, two main problems hindering the application of the TSM control are the singularity and chattering in TSM control systems. This paper proposes a novel second-order sliding-mode algorithm to soften the switching control law. The effect of the equivalent low-pass filter can be properly controlled in the algorithm based on requirements. Meantime, since the derivatives of term with fractional power do not appear in the control law, the control singularity is avoided. Thus, a chattering-free TSM control scheme for suspension systems is proposed, which allows both the chattering and singularity problems to be resolved. Finally, the effectiveness of the proposed approach is illustrated by both theoretical analysis and comparative experiment results.

In this paper, a novel adaptive fuzzy sliding mode controller for BTT missile is proposed, which incorporates the auto-tuning fuzzy logic system to sliding-mode control, the influence of the uncertainty of parameters can be alleviated. Besides, Lyapunov stability theorem is used to prove the stability of the system and the adaptive laws are deduced which possess the characteristics of sliding-mode control. Finally, the correctness and effectiveness of the method have been verified by simulation using nonlinear overload model of pitch channel of some BTT missile.

A new adaptive fuzzy sliding mode control scheme is proposed, which combines the fuzzy logic with sliding-mode control together using fuzzy logic system to approximate the difference of the equivalent control between the nominal and the real system. Lyapunov stability theorem is used to prove the stability of the system and the adaptive laws are deduced which possess the characteristics of sliding-mode control. In addition, for better control performance, the upper bound of the uncertainty is estimated. Finally, the correctness and effectiveness of the method have been verified by simulation using nonlinear overload model of pitch channel of some BTT missile.

A scheme of integrated guidance/autopilot design for STT missile against maneuvering targets on the sea is proposed. Three channels independence design idea is adopted. Firstly, an integrated guidance/autopilot model of the yaw plane is formulated and the guidance/control law is designed based on the backstepping idea and variable structure control theory. Secondly, control laws are proposed for the height and roll angle, so that when a missile has the capability to hit the target on the yaw plane, and the height approaches zero as the distance between the missile and target on the yaw plane tends to zero, the missile will hit the target. Finally, to verify the effectiveness and rightness of the integrated design scheme, a simulation of some anti-vessel missile against high maneuvering targets was made and the results show that a high accuracy of hitting a target can be achieved.