This paper investigates the adaptive finite-time containment control problem for a class of nonlinear multiagent systems (MASs) under false data injection (FDI) attacks. On account of the fact that instability is inevitable when the false datas are injected into the researched system, the backstepping technique based on the modified coordinate transformation is applied to eliminate the impact of the FDI attacks. Furthermore, the “dynamic surface control”(DSC) approach is utilized to overcome the issue of “explosion of complexity” caused by repeatedly taking derivatives for the virtual control laws. Then, by designing an observer to refactor the immeasurable states of the MASs, a finite-time adaptive output-feedback tracking containment controller is constructed. It is shown that all the signals in the closed-loop systems are semi-globally practical finite-time stability(SGPFS), and the outputs of all the followers converge to the convex hull spanned by the multiple leaders’s outputs. Besides, the observer errors and the containment errors can converge to a small neighborhood of the origin in finite time. Finally, the simulation results are presented to demonstrate the effectiveness of the proposed containment control protocol.

This paper proposes a novel hierarchical methodology to planning safe UAV trajectories in complex environments. We start by improving a canonical hybrid A* in relation to high memory requirements, performance degradation, and the low efficiency customarily observed in the initial global trajectory suggested by the planner. Then, the Marden theorem is applied - for the first time in local path planning - to generate continuous, non-intersecting, enclosed, and safe flight corridors, termed homotopic enclosed safe motion corridors (HESMCs) hereafter. This is efficiently realized through a series of unique ellipsoids along the initial route. Meanwhile, the optimized motion trajectory along the corridors is built by considering two waypoints and prescribed performance functions. The resolved path is safe and complete, with a comprehensive Lyapunov stability analysis included to ensure accurate and efficient trajectory tracking. The simulation and physical tests demonstrate the superiority of our proposed planner over existing state-of-the-art methods, with consistent and significant improvements in processing time and guaranteed completeness. Note to Practitioners—The authors perceived the contribution of the manuscript of particular relevance to users of UAVs seeking advanced safety in their guidance and navigational solutions, offering a blend of theoretical innovation and practical applicability. The work introduces a distinct hierarchical motion planner specifically designed to enhance safety and reliability in UAV navigation. Key to this is the development of an improved hybrid A* algorithm for global planning, which effectively tackles practical issues such as high memory consumption and performance degradation. A significant theoretical contribution is the application of the Marden theorem in local optimization. This facilitates the generation of homotopic enclosed motion corridors using unique safe boundary ellipsoids, thus reducing navigation complexity and the risk of failure during task execution. Additionally, the proposed scheme emphasizes the generation of motion trajectories considering position errors and prescribed performance functions, supplemented by a thorough Lyapunov stability analysis. Looking ahead, we aim to extend the proposed scheme in the context of UAV swarms for more efficient navigation in complex environments.

Effective vehicle control contributes to the safety and efficiency of connected automated vehicles (CAVs). Many existing solutions do not consider the maximum effective communication distance and bearing angle constraints between vehicles. This article proposes a novel prescribed performance method to handle distance and angle constraints to achieve vehicle stability under deception attacks. A key aspect is that the above two constraints are successfully transformed from inequality-constrained form to equivalent equation unconstrained form through introducing error transformations, and we prove that the errors of distance and angle are strictly contained within the boundary of the performance function. Another key aspect is to use adaptive bias radial basis function neural network (RBFNN) to approximate unknown nonlinear functions and deception attacks in the system and integrate the approximated results into recursive construction to design adaptive laws and multilane merging control laws. Analysis shows that all signals in a closed-loop system are practical prescribed time stable. Simulations validate that our control method has a faster convergence time than the existing advanced two-dimensional (2-D) vehicle approach and can adaptively adjust convergence to predefined sets under different attack intensities.

This article investigates the distributed adaptive fuzzy finite-time fault-tolerant consensus tracking control for a class of unknown nonlinear high-order multiagent systems (MASs) with actuator faults and high powers (ratio of positive odd rational numbers). The fault models include both loss of effectiveness and bias fault. Compared with existing similar results, the MASs considered here are more general and complex, which include the special case when the powers are equal to 1. Besides, the functions in this article are completely unknown and do not need to satisfy any growth conditions. In the backstepping framework, an adaptive fuzzy fault-tolerant consensus tracking controller is designed via adding one power integrator technique and directed graph theory so that the controlled systems are semiglobal practical finite-time stability (SGPFTS). Finally, numerical simulation results further verify the effectiveness of the developed control scheme.

For nearly a decade, quality-related fault detection algorithms have been widely used in industrial systems. However, the majority of these detection strategies rely on static assumptions of the operating environment. In this paper, taking the time series of variables into consideration, a dynamic kernel entropy component regression (DKECR) framework is proposed to address the instability of quality-related fault detection due to the existing dynamic characteristics. Compared with the typical kernel entropy component analysis method, the proposed method constructs the relationship between process states and quality states to further interpret the direct effect on the product taken by the fault. In the proposed approach, process measurements are converted to a lower-dimensional subspace with a specific angular structure that is more comprehensive than traditional subspace approaches. In addition, the angular statistics and their relevant thresholds are exploited to enhance the quality-related fault detection performance. Finally, the proposed method will be compared with three methods by means of a numerical example and two industrial scenarios to demonstrate its practicality and effectiveness. Note to Practitioners—This paper studies a quality-related fault detection problem for the dynamic nonlinear industrial system. Controlling and measuring the quality state is challenging for the high-level monitoring system of the manufacturing process due to the nonlinear dynamic feature in states. This paper proposes a new data-driven method based on the kernel entropy component analysis method to assess the correlation between the quality and fault in the industrial system, reducing unnecessary overhaul and maintenance. Based on the autoregressive moving average exogenous algorithm, the proposed method captures the dynamic interaction between the process states to decrease false alarms. In the experimental section, the DKECR method outperforms the compared approaches, which can provide stable fault detection results. Additionally, the unique angle structure of the proposed method can supply more information for engineers’ monitoring needs.

The assembly process of car body in white (BIW) in body shop is a very important link in the whole vehicle manufacturing process. Hundreds of stamping parts must be located and joined on the correct location of the platform, and the related accuracy influence the performance and quality of the cars. To guarantee the BIW manufacturing accuracy, the dimension control is a critical task in each automobile production line. In Comparison to traditional offline dimension control, this article proposed an event-triggered feedforward predictive control method to realize real-time inline dimension control during BIW assembly process. Experiments based on the actual production data from BMW Brilliance Automotive Ltd. demonstrate this control strategy can significantly improve the final products key point pass rate and decrease the fixture adjustment frequency.

This paper studies the fault-tolerant control (FTC) problem for unknown affine nonlinear systems with actuators faults. The considered types of faults are stuck (lock-in-place), loss-in-effectiveness (LIE), and bias, under which a part of the actuators is disabled. The objective is to find the remaining (not fully LIE) actuators, and manipulate them to obtain the best achievable performance in real time. First, considering that the best achievable performance is determined by the remaining actuators, a set of basic policies is predesigned with multiple levels of performances for different groups of activated actuators. Second, an identifier is designed based on history data to find the remaining actuators and, thus, the suitable predesigned basic policy. Third, to further accommodate the partial LIEs and biases, a compensator works together with the selected basic policy, to build the predesigned performance. In addressing the FTC problem, several techniques are developed: adjustable mechanisms are novelly integrated to deal with the state-dependent nonlinearities in neural network (NN) approximation, disturbances, and mismatch errors; history data are newly applied to estimate the faulty parameters; and a compensator is specially designed to deal with LIEs and biases in different input channels. Also in theory, the convergences of algorithms and the stability of closed-loop systems are proved, by formally giving the invariant sets of the initial state and the NN weights. Unlike the existing FTC methods dealing with LIE and bias based on model information to optimize the tracking error, this result can handle stuck faults without knowing system dynamics and satisfy different levels of performances described by Hamilton-Jacobi-Bellman equations. Finally, a simulation example of quadrotor unmanned aerial vehicle is given to verify the effectiveness of the proposed FTC scheme.

This article presents an adaptive fuzzy output-feedback dynamic surface control (DSC) for the nonlinear systems with dead-zone output nonlinearity. First, a novel smooth approximation of the output dead zone is given to conveniently fuse with the backstepping technique. After that a nonlinear fuzzy state observer is constructed to estimate the unmeasurable states, by employing the fuzzy logic systems for identifying the unknown compounded nonlinear functions, which releases the limitation in the existing references that the state observer needs to be linear in the presence of dead-zone output nonlinearity. Then, to reduce the effect of unknown dead-zone coefficients, an adaptive compensation mechanism is introduced by using the characteristic of hyperbolic tangent function, which can replace the widely used Nussbaum-type function-based control strategy. Furthermore, a novel DSC method based on the nonlinear filters is proposed, which not only avoids the issue of explosion of complexity inherent in the backstepping procedure, but also improves the system control performance. Under the certain assumptions, the stability of the closed-loop system is proved by use of Lyapunov function stability theory. Finally, the applicability of the proposed control method is rigorously verified by a single-link robot arm simulation example.

This paper investigates the connectivity preservation and collision avoidance problems of a multi-unmanned surface vehicle (USV) system. In order to achieve these two goals more effectively, an improved artificial potential function (APF) is designed. For the sake of solving the issue of nonlinear disturbance, a fuzzy sliding mode control method combined with fuzzy radial basis function neural network (Fuzzy-RBFNN) is introduced. Then by Lyapunov method, it can be proved that the system which employs the control scheme proposed in this paper is stable. In addition, the theoretical deduction proves that the USV can track a given ideal signal, and the connectivity preservation and collision avoidance of the multi-USV system can be achieved during formation. Finally, a multi-USV system model including four USVs is established, and the control strategy designed in this paper is adopted to conduct simulation experiments with MATLAB. The results testify that this control project is valid.

This paper presents a cooperative fault diagnosis scheme for a class of uncertain nonlinear multiagent systems component and sensor faults in individual agents. Since the faulty system affects the healthy systems through interconnections, for each agent an estimator is designed to collect neighboring output estimations errors to consider its faulty effects on others, when computing its estimations for local state and faulty parameters. A new structure of distributed estimators is proposed by filtering regressor signals and sharing them among agents. Then, the sharings of signals are planned by properly constructing auxiliary graphs for undirected and directed networks. Two conditions are given to preselect estimators parameters for the convergences of the estimation errors. Unlike the existing results dealing with one common parameter with full state measurement and only for undirected graphs, this paper presents an output measurement-based approach for multiple parameters in undirected/directed networks. It shows that for the faults not providing persistent excitation in a signal agent, it is possible to estimate the faults exactly if the they excite all agents persistently. A simulation example of a group of single-link flexible-joint robots is given to verify the effectiveness of the proposed method.

This paper presents a novel adaptive fuzzy backstepping dynamic surface control (DSC) scheme for a class of single-input single-output strict-feedback fractional-order uncertain nonlinear systems. The controlled systems contain unknown nonlinear functions and unknown external disturbances. Fuzzy logic systems are employed for approximating the unknown nonlinear functions. Further, an auxiliary function is introduced into the control function to simultaneously compensate the unknown external disturbance and the approximation error caused by fuzzy approximation, which erases the possible chattering phenomenon in the existing results. Meanwhile, a new DSC method based on the fractional-order filter is proposed to avoid the issue of explosion of complexity inherent in the backstepping procedure, which releases the limitation that the fractional-order derivative of the intermediate control function needs to be completly known in the existing references. Under certain assumptions, the stability of the closed-loop system is proved by using the fractional-order Lyapunov function stability criterion. Finally, contrastive simulation results are provided to validate the effectiveness of our proposed control strategy.

This article presents two adaptive fuzzy output-feedback dynamic surface control schemes for single-input-single-output (SISO) strict-feedback fractional-order uncertain nonlinear systems under the complete smooth nonlinearity and partial piecewise-smooth nonlinearity, respectively. For the former, complete smooth nonlinear functions are approximated by employing fuzzy logic systems, based on which a novel fractional-order reduced-order observer is constructed to estimate the unmeasurable states. For the latter, a new switched approximation and estimate strategy is proposed to deal with the problem of piecewise-smooth nonlinearity. Meanwhile, fuzzy basis function property is utilized to eliminate the assumption requirement that the nonlinear functions must satisfy the Lipschitz condition inherent in the output-feedback control, which is suitable for two cases. Under certain assumptions, the stability of two closed-loop systems are proved via fractional-order Lyapunov function stability criterion. Specifically, the stability proof of the second case is built on the foundation of the common Lyapunov function method. Finally, two diverse Chua's circuit simulation examples are provided to, respectively, validate the effectiveness of the proposed two control strategy.

The adaptive fuzzy prescribed performance tracking control design problem is investigator switched stochastic nonlinear systems with unknown control directions in this paper. The Nussbaum-type functions are introduced to handle the issue of unknown control directions. The constructed controller guarantees that all the signals in the closed-loop are bounded in probability, whereas the convergence of the tracking error and the prescribed performance bound are also ensured. Finally, simulations on a mass-spring-damper system with controller switching are performed to illustrate the developed control scheme.

The problem of adaptive decentralized fault-tolerant control is investigated in this paper for the pure-feedback nonlinear switched large-scale systems. The considered systems possess the unknown compounded nonlinearities (unknown nonlinear functions, unknown interconnected terms, unknown nonlinear function faults, and unknown actuator dead-zone nonlinearity). The radial basis function neural networks are utilized for identifying the unknown compounded nonlinear functions so that the problems of unknown nonlinearities can be solved. A switched neural networks k -filter observer is constructed to estimate the unmeasurable states, deal with the unknown actuator dead-zone nonlinearity and compensate the nonlinear function faults simultaneously. Then, by combining the average dwell time theory, adaptive decentralized control method, and under the framework of backstepping design, an adaptive output-feedback fault compensation control method is presented. Under the certain assumptions, the stability of the closed-loop system is proved by use of Lyapunov function stability theory. Finally, the applicability of the proposed controller is well carried out by a double-inverted-pendulums simulation example.

This paper studies the diagnosis and compensation for sensor and actuator faults in a quadrotor unmanned aerial vehicle. Without adding sensors or actuators for increased hardware redundancy, an observer-based adaptive controller is proposed to estimate and compensate for the faults. First, using a feedback linearization technique, an inner controller is designed to transform the form of the considered quadrotor unmanned aerial vehicle with faults into a nonlinear system with Lipschitz-like nonlinearities and parametric faulty models. Second, the estimations for unmeasurable state and actuator faults are performed in an output-feedback outer controller to compensate for the actuator faults. Third, a nonlinear high-gain observer is designed to provide the information of the state and faults to the outer controller, with the compensations for sensor faults. A Lyapunov-based analysis shows that appropriate choices of the controller parameters can guarantee the exponential convergence of errors in estimation and trajectory tracking under uncertainties and faults. The robustness to the external disturbances is also discussed. Simulations are given to verify the effectiveness of the proposed scheme. The proposed approach is also implemented on a quadrotor unmanned aerial vehicle to show its feasibility in real-time applications.

Adaptive fuzzy fault-tolerant control problem for a class of uncertain switched stochastic nonlinear systems with time-varying asymmetric output constraints is addressed in this study. Under the action of well-designed asymmetric nonlinear mapping, fuzzy control technology, and backstepping recursive design scheme; the actuator faults of both loss of effectiveness and lock-in-place are considered to develop the adaptive fuzzy controller. The boundedness of all signals as well as the convergence of the output tracking error of the closed-loop plant to an arbitrary small neighborhood about zero are guaranteed by the developed fuzzy adaptive control strategy, and the time-varying output constraints are not violated. A simulation example is worked out to demonstrate the validity of the proposed control scheme.

This paper is concerned with the design of observer for discrete-time nonlinear systems in the Takagi-Sugeno (T-S) fuzzy form. Under the framework of multi-instant homogenous matrix polynomials, a novel fuzzy observer and a new Lyapunov function, which are homogenous polynomially parameter-dependent on both the current-time normalized fuzzy weighting functions and the m-steps past-time normalized fuzzy weighting functions, are proposed for conceiving less conservative observer design conditions. Since the algebraic properties of both the current-time normalized fuzzy weighting functions and the m-steps past-time normalized fuzzy weighting functions are fully considered, the relaxation quality of the fuzzy observer design of discrete-time T-S fuzzy systems is significantly improved. In particular, some existing fuzzy Lyapunov functions and fuzzy observers are special cases of the Lyapunov function and the fuzzy observer given in this paper, respectively. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach.

This paper proposes relaxed stabilization conditions of discrete-time nonlinear systems in the Takagi–Sugeno (T–S) fuzzy form. By using the algebraic property of fuzzy membership functions, a novel nonparallel distributed compensation (non-PDC) control scheme is proposed based on a new class of fuzzy Lyapunov functions. Thus, relaxed stabilization conditions for the underlying closed-loop fuzzy system are developed by applying a new slack variable technique. In particular, some existing fuzzy Lyapunov functions and non-PDC control schemes are special cases of the new Lyapunov function and fuzzy control scheme, respectively. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method.