This article proposes an active fault-tolerant control (AFTC) scheme for attitude tracking of uncertain spacecraft with actuator faults and unavailable angular velocity. First, a second-order fully actuated system (FAS) model is derived from the dynamics of the spacecraft. The unwinding phenomenon is solved by controlling the scalar quaternion. Then, the state observer and the FAS-based controller are designed, and the assumption that all the system states and their derivatives are requested to be known is relaxed, which is a typical assumption in the existing FAS approach. The proposed method facilitates the detection and analysis of the fault information for a spacecraft, based on which an AFTC scheme is designed to address uncertain spacecraft attitude tracking under uncertain time-varying inertia parameters, faults, and disturbances. Moreover, angular velocity measurements are not needed. The uniformly bounded stability of the proposed control scheme is theoretically proved. Finally, the numerical simulation results are provided to illustrate the performance of the proposed control scheme.

The complex coupling relationship among attitude, orbit, and vibration challenges the control of flexible spacecraft. This article investigates the integrated attitude–orbit dynamical modeling and control of flexible spacecraft, which accurately describes the above complex coupling relationship to improve control performance. First, a twistor-based attitude–orbit integrated model of flexible spacecraft is established, avoiding the unitary constraint of the dual-quaternion-based model. Then, the attitude–orbit integrated control strategy based on the fully actuated system (FAS) approach is proposed for flexible spacecraft, which yields an arbitrary designable linear closed-loop system. Finally, the correctness of the dynamical model and the effectiveness and superiority of the proposed control strategy are demonstrated by two sets of simulations.

Time-optimal path generation is critical for maximizing throughput in high-speed PCB assembly, yet existing approaches predominantly focus on geometric distance minimization, overlooking the fundamental impact of acceleration dynamics and multi-axis coordination on temporal efficiency. This study addresses this gap by introducing a physics-based time estimator that explicitly models trapezoidal acceleration profiles for synchronized X/Y/Z/R-axis motions, enabling precise performance evaluation under realistic kinematic constraints. Experimental validation on production PCBs demonstrates that the proposed estimator achieves much higher estimation accuracy, outperforming conventional methods. When integrated as the objective function in multi-chromosome genetic algorithm optimization, time-optimal solutions effectively reduce actual movement times compared to distance-optimal baselines, despite requiring longer travel paths. These findings confirm the time-optimal estimator’s superiority over pure distance minimization, proving that peak efficiency is achieved by balancing travel distance with movement speed.

In surface mount technology (SMT) assembly, the increasing complexity and miniaturization of electronic components pose critical challenges to balancing placement precision and production efficiency. This paper presents a quality-efficiency driven scheduling and process co-optimization (SPCO) methodology for the pick-and-place (PAP) process in SMT production lines. Leveraging a cyber-physical system framework integrated with automated optical inspection, the proposed approach dynamically couples offline scheduling with online process capability feedback to achieve adaptive allocation of components. A precision-aware SPCO model is formulated to assign components to placement heads based on real-time process capability indices, ensuring compliance with stringent precision constraints. To enable real-time deployment, a precision-prioritized allocation heuristic (PPAH) is introduced, supporting component-head assignments under heterogeneous head capabilities. Experiments on industrial datasets demonstrate that PPAH completely eliminates precision violations while improving the overall process capability margin by 2.6-fold compared to state-of-the-art benchmarks, with only a moderate increase in total PAP time. These results validate the effectiveness of the proposed co-optimization strategy in improving first-pass yield and robustness in high-mix SMT environments.

This paper proposes an adaptive super-twisting sliding mode resilient control method for uncertain nonlinear cyber-physical systems (CPSs) subject to actuator and sensor attacks. Two super-twisting algorithms with adaptive gains are designed based on the compromised system states to counteract the effect of cyber-attacks to ensure the system states converge to a small region near zero. Furthermore, the gain overestimation problem of the sliding mode resilient controller is also discussed. Finally, a formal analysis of the closed-loop system is derived using Lyapunov function techniques. Simulation results on a two-link manipulator system validate the effectiveness of the proposed resilient control strategy. Note to Practitioners—The motivation of this paper is to address the secure issues of the nonlinear cyber-physical systems, including applications in robotic manipulators, vehicle systems, and power systems. Since some devices of CPSs, such as sensor and controller components, communicate via networked channels in practice, the original system states and control signals of CPSs may be tampered by cyber-attacks, leading to unavailable system states and severe uncertainties with unknown bounds. Although the super-twisting sliding mode control (ST-SMC) method is an effective tool for dealing with uncertainties, the control gains may be overestimated due to the intermittent nature of cyber attacks, resulting in severe chattering caused by the discontinuous term of the ST-SMC method. Furthermore, it is extremely challenging when only compromised system states are available for the controller, potentially generating false commands and destroying the entire system. Therefore, based on Lyapunov theory, a novel multivariable adaptive sliding mode resilient control method with two adaptive super-twisting algorithms is proposed to mitigate the effects caused by cyber-attacks. The effectiveness of the proposed method is validated using a two-link manipulator. Note that the proposed method has the potential for broader engineering applications in real-world scenarios.

This paper addresses the challenge of attitude stabilization for a class of underactuated rigid and flexible spacecrafts by utilizing only two control inputs. The attitude stabilization problem of underactuated spacecraft poses substantial challenges to the High-Order Fully Actuated (HOFA) system approach, due to the fact that it heavily depends on the full actuation characteristics of the system. To circumvent the aforementioned limitation, we first derive a HOFA system for underactuated rigid spacecraft using homogeneity theory and averaged system approach. Subsequently, we design a controller based on the HOFA system approach, which yields a linear closed-loop system. This methodology is then extended to address the simplified attitude stabilization problem of underactuated flexible spacecraft. By utilizing the HOFA system approach in tandem with an observer for estimating modal variables, we propose a control strategy that not only stabilizes the attitude but also actively suppresses elastic vibration. The performance of the proposed control schemes are demonstrated through simulation studies.

This paper addresses fault-tolerant adaptive synchronization for a category of high-order discrete-time multi-agent systems (MASs) influenced by stochastic noise. Built upon a spanning directed acyclic graph (SDAG), a backstepping-based framework is introduced for implementing controllers. To be specific, under the proposed framework, all unknown information are accumulated at last step and only one neural network (NN) is utilized to implement the controller design for each agent. As a consequence, the corresponding intermediate NN approximations for virtual controllers are released. Furthermore, circular dependencies are avoided with the assistance of SDAG, ensuring the feasibility of the controllers in multiagent setting. By utilizing the Lyapunov difference approach, all signals in the MASs are guaranteed to be exponential mean-square (EMS) bounded. Ultimately, simulation studies involving five pendulum systems are performed to demonstrate the effectiveness of the proposed strategy.

Printed circuit board assembly line scheduling (PCBALS) is a difficult task in the electronic industry for assembly lines using surface mounters, which is critical for production efficiency. This is a special type of line optimization problem that uses different allocation techniques, resulting in wide differences in assembly times between machines. This article proposes a hyper-heuristic optimizer embedded with a multifeature fusion ensemble estimator (HHO-MFEE) for PCBALS using linear-aligned-heads surface mounters. The objective and constraints of the problem are discussed, and a min-max integer model for small-scale problems is built. At the hyper-heuristic low level, seven data- and target-driven heuristics are presented for allocating components to different machines. Strategies for duplicated conditions with component types and placement points allocation are proposed to improve the applicability of the algorithm and the quality of the solution. An ensemble assembly time estimator that incorporates the coding of multifeatures, including estimated subobjectives, is proposed for evaluating the quality of the solution. Experimental results show that: 1) the gaps between the solution from HHO-MFEE and the optimal solution of the model are 3.44%~7.28% for small-scale data; 2) the proposed time estimator has higher accuracy than regression and heuristic-based ones, with mean absolute error of 2.01% and 3.43% for training and testing data, respectively; and 3) HHO-MFEE is better than other state-of-the-art algorithms, with average improvement of 7.21%~9.47%.

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.

The optimization of fuzzy grey cognitive map (FGCM) can enhance the decision quality of the system in managing uncertainties and incomplete information. Addressing this issue requires a method that effectively balances the competing demands of the speed and precision in the optimization process. Therefore, a tradeoff genetic algorithm (TOGA) is proposed to refine the FGCM optimization process in this article. First, a modified fitness function with a penalty term is designed to improve the convergence rate, which can drive the FGCM population to obtain the optimal solution during the evolutionary process. Second, an adaptive genetic mechanism based on horizontal comparison and longitudinal assessment, is designed to strike a balance between accelerating convergence and avoiding the dilemma of falling into local optima. Finally, in the simulation section, the effectiveness of the proposed method is validated by optimizing FGCM using synthetic datasets and applying it toa spacecraft debris threat avoidance scenario.

This article investigates a sliding-mode resilient control problem for uncertain strict-feedback nonlinear cyber-physical systems (CPSs) subject to deception attacks. The main characteristic of the proposed sliding-mode control method is its ability to mitigate undesirable system behaviors caused by deception attacks in a fixed-time interval. In particular, a novel fixed-time integral nonsingular terminal sliding-mode (INTSM) surface is designed recursively by adding a power integrator (API) technique, wherein the fractional power can be chosen to ensure the fixed-time stability of the closed-loop system and to estimate the upper bound of settling time. In addition, an adaptive super-twisting reaching law is proposed to counteract the deception attacks and ensure the continuous control action of CPSs, effectively reducing the effects of the chattering problem. Simulation results are given to demonstrate the effectiveness of the proposed sliding-mode resilient control scheme.

In this article, the twistors modeling method and high-order fully actuated (HOFA) system approach are utilized to investigate the attitude and orbit integrated control problem of spacecraft. Initially, a first-order state-space model is formulated to represent the attitude and orbit dynamics of spacecraft. This model is subsequently transformed into a HOFA system model. Based on this transformed model, a control strategy is meticulously designed. With the developed control strategy, a linear closed-loop system is obtained, whose poles can be arbitrarily configured. The effectiveness of the proposed control strategy is ultimately verified through detailed simulation results.

To minimize the disturbance of the Tunnel Boring Machine (TBM) cutterhead on the surrounding rock during the coal mine roadway excavation process and ensure that the cutterhead rotation speed achieves fast tracking performance with minimal overshoot, we propose a direct adaptive robust control method for the cutterhead rotation speed hydraulic system based on inversion design. This method considers the strong disturbances such as loads and motion affecting the cutterhead hydraulic drive system, as well as the uncertainties in the cutterhead model. We establish the nonlinear model of the cutterhead hydraulic drive system and employ virtual control to reduce the model order. Using Lyapunov functions, we ensure the stability of the entire system and derive the control law for the cutterhead rotation speed controller, along with parameter-adaptive laws acting as parameter estimators. We validate the effectiveness of the proposed control strategy through joint simulation using AMESim and Simulink. The results show that the designed cutterhead rotation speed controller achieves high tracking accuracy and good adaptability.

This article investigates the attitude containment control problem for multiple spacecrafts based on optimized backstepping design strategy. First, the spacecrafts' attitude dynamics are modeled by modified Rodrigues parameters and are rewritten in the strict-feedback form. Then, we incorporate the idea of optimal control into each step of backstepping design procedure. At each step, the optimal virtual/actual control law is obtained via actor–critic reinforcement learning algorithm and approximated by fuzzy logic systems (FLSs). The updating laws of the parameter matrices are designed in a more concise form and the persistent excitation condition is relaxed. According to the Lyapunov stability analysis, the containment error and FLS parameter errors are semiglobally uniformly ultimately bounded. Finally, a simulation example is given to illustrate 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.

In this article, we study the fault-tolerant control (FTC) issue of stochastic nonlinear controlled plants. The considered plant is of strict-feedback form, which subjects to multiple faults, i.e., sensor and actuator faults. For the purpose of estimating the unmeasured system state vectors, a neural-network-based observer is presented. With the help of a combined quadratic, cubic, and quartic Lyapunov functional candidate, a new type of adaptive FTC strategy is formulated to ensure the overall tracking performance with multiple faults. Besides, the corresponding stability analysis is carried out as well. Comparing with the simulation results without fault compensation strategy, the validity of the designed FTC mechanism is verified.

This article addresses the adaptive secure control problem for a class of uncertain nonlinear large-scale cyber-physical systems (CPSs) subjected to deception attacks. Specifically, a novel adaptive fuzzy control scheme for each subsystem of CPSs is designed to mitigate the effects of cyberattacks that intentionally tamper with control signals from controllers to actuators and the state signals from sensors to controllers, and a Nussbaum function is proposed to tackle unknown time-varying control directions. Moreover, a finite-time converging secure control scheme is designed for compromised CPSs to guarantee all the states converge to a predetermined small set in finite time. It is shown that with the proposed control scheme, all the signals of the closed-loop system are proved to be semiglobally bounded. Two simulation examples are provided to illustrate the effectiveness of the proposed secure control method.

We consider the tracking control problem for a class of stochastic nonlinear systems in strict-feedback structure via output feedback signal in this article. The controlled plant is assumed subject to unknown dead-zone input. By utilizing the fact that the unknown dead-zone input function can be modeled as a time-varying nonlinear function and a bounded disturbance, and selecting appropriate design parameters, we show that the effect of unknown dead zone can be compensated. We design a fuzzy state observer via fuzzy logic modeling technique to estimate the unknown system states. Then, we prove, via Lyapunov stability analysis, that the controlled plant is bounded in probability. In addition, all the signals in the closed-loop system are guaranteed to be globally bounded in probability. The tracking errors are also ensured to converge to a small neighborhood of the origin. Finally, to show the effectiveness of the proposed control strategy, a simulation example of one-link manipulator is presented in the simulation section.

In this article, a combined gradient descent-Barzilai Borwein (GD-BB) algorithm and radial basis function neural network (RBFNN) output tracking control strategy was proposed for a family of nonlinear systems with unknown drift function and control input gain function. In such a method, a neural network (NN) is used to approximate the controller directly. The main merits of the proposed strategy are given as follows: first, not only the NN parameters, such as weights, centers, and widths but also the learning rates of NN parameter updating laws are updated online via the proposed learning algorithm based on Barzilai-Borwein technique; and second, the controller design process can be further simplified, the controller parameters that should be tuned can be greatly reduced. Theoretical analysis about the stability of the closed-loop system is manifested. In addition, simulations were conducted on a numerical discrete time system and an inverted pendulum system to validate the presented control strategy.

This article develops a novel cost function (performance index function) to overcome the obstacles in solving the optimal tracking control problem for a class of nonlinear systems with known system dynamics via adaptive dynamic programming (ADP) technique. For the traditional optimal control problems, the assumption that the controlled system has zero equilibrium is generally required to guarantee the finiteness of an infinite horizon cost function and a unique solution. In order to solve the optimal tracking control problem of nonlinear systems with nonzero equilibrium, a specific cost function related to tracking errors and their derivatives is designed in this article, in which the aforementioned assumption and related obstacles are removed and the controller design process is simplified. Finally, comparative simulations are conducted on an inverted pendulum system to illustrate the effectiveness and advantages of the proposed optimal tracking control strategy.

This article investigates the adaptive fuzzy control algorithm for a class of large-scale switched fractional-order nonlinear nonstrict feedback systems. In this algorithm, we utilize fuzzy-logic systems (FLSs) to approximate the complicated unknown nonlinear functions. Based on the fractional Lyapunov stability rules, a virtual control law is presented. A fuzzy adaptive decentralized control method is developed under the technique of the Lyapunov function. Under the operation of the proposed algorithm, the stability of the proposed systems and the control performance can be guaranteed. Finally, simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.

This article investigates the adaptive fuzzy finite-time control problem for a class of high-order stochastic nonlinear systems with a class of exponential type prescribed performance function. It is assumed that the nonlinear functions in the controlled plant are unknown, in which fuzzy logic systems (FLSs) are utilized due to the approximation ability of any unknown continuous functions with arbitrary approximation errors. Based on the FLSs and backstepping design technique, a novel adaptive fuzzy tracking control strategy is proposed to guarantee that the closed-loop nonlinear system is semiglobally finite-time stable in probability via Lyapunov stability theory and It$\hat{o}$formula. Compared with existing results, the transformed error signal was regarded as a stochastic variable. In addition, the expressions of the first and second-order partial derivatives of the transformed error signals are given in this article. Finally, a simulation example with different covariance values is given to show the effectiveness of the proposed control strategy.

This article studies the disturbance observer-based adaptive fuzzy finite-time control issue of strict-feedback nonlinear systems. Specifically, to meet practical application requirement, the finite-time prescribed performance is considered, which can guarantee the tracking error enters into the prescribed bounded set in a known time. A disturbance observer is proposed to estimate the external disturbance. It is proved that the closed-loop system is semi-globally practically finite-time stable. Finally, simulation studies for a one-link manipulator are shown to verify the effectiveness of the proposed approach.

In this article, the finite-time adaptive fuzzy state-feedback tracking control problem for the pure-feedback system with full state constraints is studied. In order to transform the pure-feedback form into system strict-feedback case, the mean value theorem is introduced. By employing finite-time-stablelike function and state transformation for output tracking error, the output tracking error converges to a predefined set in a fixed finite interval. To tackle the problem of state constraints, integral barrier Lyapunov functions are utilized to guarantee that the state variables remain within the prescribed constraints with feasibility check. Fuzzy logic systems are utilized in this article to approximate nonlinear uncertainties. In addition, all the signals in the system are guaranteed to be semiglobal ultimately uniformly bounded. Finally, two simulation examples are given to show the effectiveness of the proposed control strategy.

An adaptive fuzzy decentralized control algorithm is developed in this article for interconnected nonlinear large-scale systems with unmodeled dynamics. This method is proposed by applying a fuzzy logic system (FLS) to identify unknown nonlinear functions. In addition, dynamic signals are introduced in design process of the backstepping to compensate the effect of unmodeled dynamics. A novel adaptive state feedback control algorithm is proposed with the help of Lyapunov function. Then, the state feedback control algorithm is extended to the case of output feedback by constructing a fuzzy state observer with FLSs. The large-scale and closed-loop nonlinear system is ensured to be semi-global uniformly ultimately bounded (SGUUB). Apart from this, all signals are guaranteed to be bounded. Both numerical and practical simulation examples are utilized to elaborate the feasibility of the developed control algorithms.

This article investigates the command filtering-based event-triggered adaptive fuzzy control problem for a class of stochastic nonlinear systems with stochastic faults and input saturation. The unknown nonlinear functions and system dynamic changes that caused by stochastic faults are approximated by fuzzy logic systems (FLSs). In order to reduce the computational burden, the command filtering design technique is incorporated into the adaptive fuzzy event-triggered control strategy. Finally, the effectiveness of the proposed method is verified by simulation studies, in which the uniform ultimate boundedness of the system is guaranteed and all the signals in the closed-loop system are bounded.

Multi-target tracking application for fast-moving workpieces has drawn increasing attention in the industrial field. For the dense, fast moving workpieces with few texture features, traditional cameras get poor quality images with dynamic blur and object adhesion, which makes the detection and tracking of workpieces unreliable. However, the event camera outputs events asynchronously at a microsecond speed when the pixel intensity changes, which can capture the contours of fast-moving workpieces well. In this paper, we propose a parallel two-pipe multi-target tracking algorithm based on the event camera for fast-moving workpieces. RGB-E image obtained by fusing the RGB image and the event solves the unreliable detection caused by dynamic blur and object adhesion. The parallel mechanism ensures that the low-speed detection pipeline does not have much impact on the speed of the high-speed tracking pipeline. Hungarian algorithm is used to associate the detection results obtained by the YOLOv4-tiny detector with the tracking results obtained by the KCF tracker. A correction algorithm based on pixel speed is proposed to synchronize detection results and tracking results. Experimental results prove the proposed algorithm can achieve reliable detection and tracking performance for fast-moving workpieces.

This article investigates the adaptive fuzzy fault-tolerant control problem for a class of strict-feedback stochastic nonlinear systems with quantized input signal. A hysteretic quantizer is utilized to avoid chattering caused by quantized input signals. The fuzzy-logic systems are utilized to approximate the unknown nonlinear functions and also to construct the fuzzy state observer, which is used to estimate the immeasurable state vector. The actuator faults considered in this article are loss of effectiveness and lock-in-place faults. By using the Lyapunov stability theory, the closed-loop stochastic nonlinear system is guaranteed to be stable in probability, and all the signals of the closed-loop system are bounded in probability in the presence of quantized input and actuator faults. Finally, a simulation example is given to verify the validity of the proposed control strategy.

This article investigates the adaptive fuzzy control problem for a class of stochastic nonlinear systems with the risk-sensitive performance index. The desired cost level of the risk-sensitive index, which could be arbitrarily small, is guaranteed by the solution of a specified Hamilton–Jacobi–Bellman (HJB) equation. By considering the unknown uncertainties of the stochastic nonlinear systems, a novel adaptive fuzzy risk-sensitive control method is proposed, which guarantees the input-to-state stability of the system. In addition, the proposed control strategy also reduces the conservatism of the existing adaptive robust control method. Finally, the effectiveness of the proposed approach is verified by a simulation example of one-link manipulator.

This article investigates the problem of adaptive fuzzy decentralized tracking control design for a class of large-scale interconnected nonlinear systems with network-induced input time delay. First, fuzzy logic systems are used to approximate the unknown nonlinear functions. Then, a newly defined filtered error is introduced to compensate the network-induced delay, and a novel adaptive fuzzy decentralized control strategy is proposed to guarantee the uniform ultimate boundedness of the closed-loop nonlinear system. Finally, a simulation example is given to further demonstrate the effectiveness of the proposed method.

This article investigates the event-triggered adaptive tracking control for a class of pure-feedback stochastic nonlinear systems with full state constraints and input saturation. The saturated input is expressed as a smooth nonlinear function with bounded disturbance. The pure-feedback structure is transformed into strict-feedback case via mean value theorem, and a novel event-triggered adaptive fuzzy tracking control scheme with relative threshold is then proposed. The barrier Lyapunov function is introduced to analyze the system stability, and the state constraints are, thus, guaranteed. It is proved that the closed-loop stochastic nonlinear system is semiglobally uniformly ultimately bounded in probability, and the output tracking error converges to a small neighborhood of zero. Finally, the effectiveness of the proposed method is verified via simulation studies.

This article investigates the adaptive learning control problem for a class of nonlinear autonomous underwater vehicles (AUVs) with unknown uncertainties. The unknown nonlinear functions in the AUVs are approximated by radial basis function neural networks (RBFNNs), in which the weight updating laws are designed via gradient descent algorithm. The proposed gradient descent-based control scheme guarantees the semiglobal uniform ultimate boundedness (SUUB) of the system and the fast convergence of the weight updating laws. In order to reduce the computational burden during the backstepping control design process, the command-filter-based design technique is incorporated into the adaptive learning control strategy. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.

This paper concerns with the stability analysis for a class of discrete-time switched Takagi-Sugeno (T-S) fuzzy systems. By establishing a semitime-dependent Lyapunov function and exploring the property of mode-dependent average dwell-time switching, improved stability criteria are provided for switched T-S fuzzy systems containing both stable and unstable modes. Then, slow and fast switching strategies are designed, which are adopted for stable and unstable subsystems, respectively. Especially, we also provide the stability conditions for switched systems with all modes stable and unstable. Finally, the validities and advantages of provided techniques are illustrated by some simulation examples.

This article investigates the adaptive fuzzy tracking control problem for a class of strict-feedback nonlinear systems with time-varying input delay and full state constraints. By using state vector transformation and barrier Lyapunov function techniques, the effect of time-varying input delay and full state constraints are compensated. The unknown nonlinear functions are approximated by utilizing fuzzy logic systems, and then a novel adaptive fuzzy backstepping control strategy is proposed to guarantee that the closed-loop nonlinear system is semiglobally ultimately uniformly bounded. Finally, two simulation examples of an inverted pendulum system and three-degrees-of-freedom helicopter nonlinear system are studied to verify the effectiveness of the proposed control strategy.

The H∞filter design for a class of switched Takagi-Sugeno (T-S) fuzzy systems with persistent dwell time (PDT) is investigated in this paper. The considered switched fuzzy systems contain a limited number of subsystems and each local subsystem is represented by the well-known T-S fuzzy model. Compared with the dwell time (DT) switching or average DT switching that attracted quantities of interests over the last decade, the PDT switching considered in this paper is known to be more general. The stability and £2-gain analysis for switched systems with PDT switching are derived first, based on which a set of full-order H∞filter is designed to guarantee the global uniform asymptotic stability with a prescribed non-weighted H∞noise attenuation performance for the resulting filtering error system. Finally, the effectiveness of the provided method is illustrated with an example.

This paper studies the problem of fuzzy adaptive event-triggered control for a class of pure-feedback nonlinear systems, which contain unknown smooth functions and unmeasured states. Fuzzy logic systems are adopted to approximate unknown smooth functions and a fuzzy state observer is designed to estimate unmeasured states. Via the event-triggered control technique, the control signal of the fixed threshold strategy is obtained. By converting the tracking error into a new virtual error variable, an observer-based fuzzy adaptive event-triggered prescribed performance control strategy is designed. The key advantage is that the proposed method does not require a priori knowledge of partial derivatives of system functions, i.e., it relaxes the restrictive condition that the partial derivatives of system functions need to be known for pure-feedback nonlinear systems. Simulation results confirm the efficiency of the proposed method.

The problem of adaptive fuzzy control is investigated for a class of nontriangular structural stochastic switched nonlinear systems with full state constraints in this paper. A remarkable feature of the nontriangular structural nonlinear system is the so-called algebraic loop problem in the existing backstepping-based analysis and design. Properties of fuzzy basis functions are utilized to circumvent this algebraic loop problem. Based on the Barrier Lyapunov function, an adaptive fuzzy stochastic switched control scheme is designed. It is proven that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded with full state constraints. The effectiveness of the proposed control scheme is verified via simulation studies.

For a class of stochastic nonlinear time-delay systems with multiple constraints-predefined tracking constraint, input saturation, and output dead zone-the output tracking control problem is addressed in this paper. By expressing the saturated actuator as a smooth nonlinear function and employing the Nussbaum function technique, the input and output constraints problems are solved. The tracking performance is achieved under the predefined tracking constraint by utilizing the backstepping recursive design technique and the approximation property of neural networks. Then, based on the utilization of the Lyapunov-Krasovskii functional, the stochastic stability of the closed-loop system is achieved. Finally, the proposed control method is verified through a simulation example.

In this paper, the output feedback control problem is investigated for general nonlinear industrial processes. At the device layer, the nonlinear industrial processes with disturbances are modeled by utilizing Takagi-Sugeno modeling approach, and the corresponding local controllers are then designed to guarantee that the outputs for the local subsystems can track the decomposed setpoints. At the operation layer, considering the effect of radial basis function performance index and packet dropout phenomenon, a setpoint compensator is constructed to dynamically regulate the setpoints and track the given operation index. Finally, a network-based continuous stirred tank reactor system is considered to verify the validity of the proposed strategy in the simulation part.

In this paper, the performance-based control design problem for double-layer networked industrial processes is investigated. At the device layer, the prescribed performance functions are first given to describe the output tracking performance, and then by using backstepping technique, new adaptive fuzzy controllers are designed to guarantee the tracking performance under the effects of input dead-zone and the constraint of prescribed tracking performance functions. At operation layer, by considering the stochastic disturbance, actual index value, target index value, and index prediction simultaneously, an adaptive inverse optimal controller in discrete-time form is designed to optimize the overall performance and stabilize the overall nonlinear system. Finally, a simulation example of continuous stirred tank reactor system is presented to show the effectiveness of the proposed control method.

This paper investigates the multirate networked industrial process control problem in double-layer architecture. First, the output tracking problem for sampled-data nonlinear plant at device layer with sampling period T(d) is investigated using adaptive neural network (NN) control, and it is shown that the outputs of subsystems at device layer can track the decomposed setpoints. Then, the outputs and inputs of the device layer subsystems are sampled with sampling period T(u) at operation layer to form the index prediction, which is used to predict the overall performance index at lower frequency. Radial basis function NN is utilized as the prediction function due to its approximation ability. Then, considering the dynamics of the overall closed-loop system, nonlinear model predictive control method is proposed to guarantee the system stability and compensate the network-induced delays and packet dropouts. Finally, a continuous stirred tank reactor system is given in the simulation part to demonstrate the effectiveness of the proposed method.

This paper investigates the adaptive fuzzy backstepping control and H∞performance analysis for a class of nonlinear systems with sampled and delayed measurements. In the control scheme, a fuzzy-estimator (FE) model is used to estimate the states of the controlled plant, while the fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. The controller is obtained based on the FE model by combining the backstepping technique with the classic adaptive fuzzy control method. In the stability analysis, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SUUB) and the outputs of the system are proven to converge to a small neighborhood of origin. Furthermore, the H∞performance is investigated and the outputs of the closed-loop system are bounded in the H∞sense. Two examples are given to illustrate the effectiveness of the proposed control scheme.

This paper focuses on the performance tracking issue of complex industrial processes in double layer architecture. First, the nonlinear plants in the device layer are modeled by using Takagi-Sugeno (T-S) fuzzy technique, and are controlled by local proportional integral (PI) controller with the H∞performance guaranteed. Then, the outputs and inputs of local plants are sampled and transited to the operation layer to form the economic performance index (EPI), which is used to represent the performance of the tracking of economic objective. Furthermore, the setpoints, which are dynamically changing, are calculated via a compensator based on the error between the objective and the EPI at each step of the operation layer. Finally, the effectiveness of the proposed method is demonstrated by a nonlinear continuous stirred tank reactor (CSTR) model.

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

This paper investigates fuzzy adaptive actuator failure compensation control for a class of uncertain stochastic nonlinear systems in strict-feedback form. These stochastic nonlinear systems contain the actuator faults of both loss of effectiveness and lock-in-place, unmodeled dynamics, and without direct measurements of state variables. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy state observer is established to estimate the unmeasured states. By introducing the dynamical signal and the changing supply function technique design into the backstepping control design, a robust adaptive fuzzy fault-tolerant control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are bounded in probability in the presence of the actuator failures and the unmodeled dynamics. Simulation results are provided to show the effectiveness of the control approach.

In this paper, an adaptive fuzzy output feedback control approach is investigated for a class of stochastic nonlinear strict-feedback systems without the requirement of states measurement. The stochastic nonlinear system addressed in this paper is assumed to possess unstructured uncertainties (unknown nonlinear functions) and, in the presence of unmodeled dynamics, dynamics disturbances. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By combining the backstepping design technique with the stochastic small-gain approach, a new adaptive fuzzy output feedback control approach is developed. It is proved that the proposed control approach can guarantee that the closed-loop system is input-state-practically stability (ISpS) in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by appropriate choice of the design parameters. Simulation results are included to indicate that the proposed adaptive fuzzy control approach has a satisfactory control performance. In addition, the simulation comparisons with the previous methods show that the proposed adaptive fuzzy control approach has robustness to the dynamical uncertainties.