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This paper presents an adaptive memetic algorithm based on a new neighbourhood structure (AMA) for solving the assembly job shop scheduling problem, with the aim of minimising the maximum completion time (makespan). To utilise the knowledge of problem, a theoretical analysis is conducted to explore the criteria for feasible and effective movement of operations under assembly constraints, and a multi-operation precise joint movement neighbourhood structure is proposed accordingly. In the AMA, to ensure the feasibility of solutions during the evolution process, a feasible encoding mechanism based on the constraint degree of operations is designed, a greedy active decoding method as well as feasible crossover operation based on independent operation chains are designed specifically for this encoding method. To avoid premature convergence of the population, a population update operator with diversity adaptive control is proposed. Finally, by comparing the results with five state-of-the-art algorithms, the superiority of AMA in terms of solution quality and stability is verified, particularly with the update of known optimal solutions for 11 instances. [ABSTRACT FROM AUTHOR]

Traditional machining is transforming to digital and intelligent machining, in which adaptive machining cyber-physical system (CPS) provides a useful approach to control the machining quality of large thin-walled parts. And the running of adaptive machining CPS is a complex multi-processes execution flow, which can be regarded as a continuous–discrete hybrid system. To realise adaptive controlling of machining quality and adaptive managing of process flow, a unified model for continuous–discrete hybrid adaptive machining CPS is constructed. Firstly, an architecture of adaptive machining CPS is proposed. Next, the cutting process in adaptive machining CPS is modelled as a continuous-variable system (CVS), while the process flow in adaptive machining CPS is modelled as a discrete-events system (DES). Then, the finite state machine is adopted to integrate the CVS and DES to form the unified model of adaptive machining CPS. Finally, an adaptive machining CPS is developed based on the unified model, and the machining results demonstrate that machining quality is efficiently controlled, as well as the process flow is orderly managed. The built unified model has four features, respectively universality, integrability, scalability, and reconfigurability, which can be reconstructed to form a new instancing model according to the different machining requirements. [ABSTRACT FROM AUTHOR]

In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios. [ABSTRACT FROM AUTHOR]

This article proposes a tracking control strategy based on fixed‐time for uncertain nonlinear systems with prescribed performance and actuator faults. Fuzzy logic systems are used to model unknown functions with uncertainties or fuzzy parameter features. The improvement of the conventional backstepping method through the introduction of command filtering technology has effectively addressed the challenge of a sharp increase in computational complexity during controller design. Additionally, a mechanism for error compensation has been established to mitigate the potential impact of errors that may arise during the filtering process on the system. Then, a new fixed‐time performance function is introduced into the adaptive backstepping method to ensure that the tracking signal with output error converges to an arbitrarily small neighborhood within a fixed time. Additionally, in order to enhance the tracking performance of the control method, the controller algorithm incorporates fault‐tolerant control based on the fixed‐time performance function. Finally, a numerical simulation of a power system with a static reactive power compensation device is presented, and demonstrate the effectiveness and feasibility of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This work presents an event‐triggered model‐free adaptive sliding mode control (MFASMC) strategy for nonlinear networked control systems (NCSs) without and with deception attacks. First, a time‐varying pseudo‐partial derivative is introduced to transform the nonlinear NCSs into a linearized model. In the secure environment without attacks, an event‐triggered MFASMC law is proposed based on the designed sliding surface. Then, considering that the transmitted signal is vulnerable to deception attacks, it assumes that the attacker injects false data into the output signal in a probabilistic manner. Under this case, the above event‐triggered MFASMC scheme is implemented by means of attacked output signals. And the network burden can be reduced while ensuring the reachability of the specified sliding surface and the output tracking performance. The key feature of the proposed MFASMC algorithm is that secure tracking control can be achieved using only input/output data without relying on any model information. Finally, the proposed control strategy is verified via the steam‐water heat exchanger. [ABSTRACT FROM AUTHOR]

This article focuses on a class of nonstrict feedback systems with input delay, state delays and time‐varying full‐state constraints by proposing an adaptive neural control scheme. To overcome the problems of all state variables effected by time‐varying constraints, the asymmetric time‐varying barrier Lyapunov functions are constructed. The influence of state delays and input delay is eliminated by employing suitable Lyapunov–Krasovskii functionals. Additionally, the process of controller design is based on backstepping method and the unknown functions can be approximated by radial basis function neural networks. Moreover, the problem of repeated differentiations for nonlinear components during controller design is hugely simplified by taking advantage of the dynamic surface control method. The boundness of all the closed‐loop signals can be ensured by the designed controller. Finally, two numerical simulations illustrate that the proposed adaptive neural control scheme is effective. [ABSTRACT FROM AUTHOR]

The enhanced model reference adaptive control (EMRAC) algorithm is an effective full‐state adaptive solution to control plants affected by nonlinear unmodelled dynamics and persistent disturbances. The EMRAC strategy improves the tracking performance by equipping the MRAC algorithm with adaptive switching and adaptive integral control actions. However, the need for the plant state prevents the applicability of the EMRAC algorithm to engineering control problems where only the plant output is measurable. To cover this gap and extend the range of plants controllable by EMRAC solutions, this article presents an output‐based EMRAC algorithm leveraging the closed‐loop (CL) MRAC formulation. The robustness of the closed‐loop control system is analytically analysed, not only with respect to plant parameter uncertainties and square measurable disturbances, but also to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unmodelled terms and disturbances. The ultimate boundedness of the closed‐loop control system is assessed with respect to ℒ∞$$ {\mathcal{L}}_{\infty } $$ unknown nonlinear terms and disturbances, by using Lyapunov theory for Filippov systems, as the adaptive switching control action makes the closed‐loop system discontinuous. To assess the effectiveness of the CL‐EMRAC strategy to impose reference trajectories despite the unmodelled plant dynamics and persistent bounded disturbances, the problem of vehicles' direct yaw moment control is used as an engineering case study. The closed‐loop tracking performance is also quantitatively evaluated through a set of key performance indicators and compared to those provided by four benchmark controllers, that is, two LQ‐based strategies and two MRAC‐based control solutions. The CL‐EMRAC and benchmark controllers are implemented and tested in a co‐simulation environment based on a high‐fidelity IPG CarMaker vehicle model. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of robust adaptive safety control of a class of nonlinear systems with parametric uncertainties. Two novel definitions, that is, robust adaptive control Lyapunov function (RACLF) and robust adaptive control barrier function (RACBF) are introduced in this article. Also, an estimation of unknown parameters is obtained based on multiple identification models, state predictors and a logic switching mechanism. The switching mechanism plays a vital role in enhancing the transient properties of nonlinear systems. Meanwhile, a safe motion framework for differential robots is provided based on the existence of parametric uncertainties in RACLF and RACBF, and a safe and stable controller is obtained by solving quadratic programming. Finally, simulation results of differential robot capable of safe motion in space are presented to verify the validity of the design method. [ABSTRACT FROM AUTHOR]

In this paper, a novel decentralized adaptive state‐feedback design method is proposed for large‐scale stochastic nonlinear systems. Different from the existing results, we consider more practical and more general systems, that is, systems with both unknown covariance and time‐varying powers. When dealing with the unknown time‐varying covariance, we do not need to know its bound, but use the estimator to design an adaptive law function. Then, a new adaptive controller is constructed with the backstepping method to ensure that the closed‐loop system is globally stable in probability, and that the states are regulated to the origin almost surely. Finally, a simulation example is given to illustrate the effectiveness of the design scheme. [ABSTRACT FROM AUTHOR]

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO 2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach. • Simple regression tree model able to adapt to a wide variety of conditions. • The model is capable of successfully predicting pH under a different system conditions. • The simple structure of the model facilitates the adaptive tuning of control algorithms. • More than 100 days for 10 months for calibration and validation purposes. [ABSTRACT FROM AUTHOR]

A single-inductor multi-source energy harvesting system that can harvest three types of ambient energy was presented. The system can self-supply using an integrated self-starting circuit that does not require an external battery. It performs hybrid modulation of pulse-frequency modulation (PFM) and pulse-width modulation (PWM) and realizes adaptive asynchronous control for input and output, achieving higher system stability and better energy aggregation. Adaptive maximum power point tracking (MPPT) was implemented to generate inductor charging control signals with different frequencies and duty cycles according to different input sources, enabling the inductor to operate in discontinuous mode (DCM) under different input conditions. The energy harvesting system was designed using a 0.18-μm CMOS process. The simulation results show that the system can realize low-voltage self-startup as low as 200mV and can provide a stable output of 1.4V to the load with a 1V harvested voltage to self-supply. The highest conversion efficiency of the system is 76.43%@Rload=10kΩ, and the average power consumption in sleep mode is 2.38μW. [ABSTRACT FROM AUTHOR]

ABSTRACT This article addresses a solution to one of the main challenges of online data‐driven control (DDC) methods: reducing the sensitivity of the model‐free adaptive control (MFAC) method to initial conditions and control parameters with the new control cost function and added the output error rate and integral along with a new anti‐wind up strategy for multi‐input multi‐output (MIMO) systems. The parameters introduced to the new control law have been validated using the boundary‐input boundary‐output (BIBO) approach to design and converge the controller. The simulation findings on a nonlinear auto‐regressive moving average model with exogenous inputs (NARMAX) system with triangular control input demonstrate that the proposed control rule will outperform to prototype MFAC. Furthermore, to analyze the sensitivity of the controller to the initial conditions and the uncertainties of the control parameters, 30 Monte Carlo simulations were performed with random initial conditions in the presence of disturbance in the control input, and output noise, and the results were compared with the prototype MFAC and conventional PID controller using standard criteria such as integral time absolute error, standard deviation, steady‐state error, and mean maximum error, which shows a noticeable superiority of proposed controller relative to the prototype MFAC. [ABSTRACT FROM AUTHOR]

In this paper, a quick convergence adaptive structure is proposed for trajectory tracking of an articulated type robotic manipulator using the Hardware in the Loop (HIL) simulation technique. A novel Nonlinear Quick Convergence Adaptive Sliding Backstepping Control (QCASBC) algorithm is implemented on a C2000 real-time controller board. The performance of the proposed control algorithm is inspected concerning the sliding mode PID (SM-PID) control technique to estimate its correlation with the proposed algorithm. The experimental part of the HIL simulation tests has been carried out on a simulated model of a three-link serial robot manipulator. A dynamic model of the robotic manipulator has been developed using Matlab Simulink software and its performance is analyzed using the HIL technique via a C2000 real-time controller for tracking the desired trajectory. Results show that the speed of convergence while tracking the desired trajectory of the manipulator is better in the proposed algorithm. It is also estimated that the positional error of the QCASBC algorithm is superior to the SM-PID control algorithm. The observed average angle error is improved by 14% and the average response time error is improved by 11% by using the proposed QCASBC algorithm compared to the SM-PID approach. [ABSTRACT FROM AUTHOR]

In this article, a novel self‐triggered mechanism is developed to reduce the computation burden and communication bandwidth for the optimal regulation problem of nonlinear dynamical systems. Compared with existing results, this article can avoid continuous measurement of online signals while achieving the performance optimization with closed‐loop system stability guarantee. The self‐triggered mechanism is combined with the actor‐critic structure for performance optimization, where the critic is trained to provide a guideline to improve the actor. The actor‐critic learning is implemented as a hybrid system, where the critic weights update as a continuous flow, and the actor weights are adapted intermittently. The simulation study is conducted to verify the effectiveness of the proposed self‐triggered actor‐critic learning. [ABSTRACT FROM AUTHOR]

In this paper, the problem of attitude tracking control for a class of vertical take-off landing (VTOL) aircraft in the presence of uncertain disturbances is addressed. The active disturbance rejection control (ADRC) method is utilized to address uncertain disturbances, while the radial basis function (RBF) neural network is considered for handling unknown nonlinear dynamics. By incorporating a modified tracking differentiator, a nonlinear state error feedback controller, and an extended state observer, the disturbance observation problem in the aircraft system is solved. These disturbances, seen as a combined disturbance in the aircraft system, are counteracted through the extended state observer. Furthermore, the unknown nonlinear dynamics within the aircraft system are approximated using the RBF neural network. Subsequently, the paper suggests adaptive ADRC strategies based on neural network and formulates adaptive control laws to estimate unknown parameters. The Lyapunov stability theory proves the effectiveness of the proposed control strategies. The simulation results show that the attitude tracking control problem of VTOL aircraft can be achieved under the proposed control strategies and adaptive control laws. [ABSTRACT FROM AUTHOR]

To improve the motivation and enthusiasm of subjects during active rehabilitation training, this paper proposes a novel training task planning-based adaptive assist-as-needed (TTP-AAAN) control algorithm for an upper limb exoskeleton. The overall controller contains an outer control loop to determine the required assistive force, and an inner control loop to drive the exoskeleton to track subject motion and to provide desired assistive force obtained from the outer control loop. In the outer control loop, a motion intention and task performance evaluation (MITPE) strategy is established to learn the motor capability of the subject. Based on the obtained evaluation result, the radius and frequency of multi-periodic trajectory tracking task, and the gain of the assistive force are adaptively adjusted by using the adaptive central pattern generator (ACPG) algorithm. Then, in the inner control loop, an asymmetric barrier Lyapunov function-based adaptive output feedback (ABLF-AOF) controller, in combination with a neural network (NN) state observer, is developed. The exoskeleton tracking errors are constrained by the asymmetric barrier Lyapunov function, and the state variables and uncertainty terms of the exoskeleton are simultaneously estimated by the NN state observer. Experiments are carried out with an upper limb exoskeleton to demonstrate the effectiveness of the proposed control strategy. The experimental results show that the developed control scheme can provide assistance and achieve task parameter adaption for the subjects with different motion patterns. In addition, the proposed controller has better training performance than task performance-based adaptive velocity assist-as-needed (AAN) controller and minimal AAN controller. [ABSTRACT FROM AUTHOR]

This paper investigates a finite-time adaptive fuzzy prescribed performance fault-tolerant control (FTC) issue for the steer-by-wire vehicle (SBWV) systems with intermittent actuator faults. Different from the steer-by-wire (SBW) system studied by the previous literatures, the SBWV system involved in this study consists of a vehicle dynamics model and an SBW system, including unmeasurable states and unknown nonlinear dynamics. Fuzzy logic systems (FLSs) are first used to identify the unknown model dynamics, and a fuzzy state observer is constructed to estimate the unmeasured states. Then, to compensate for the influence of intermittent actuator faults, a novel finite-time output-feedback prescribed performance adaptive FTC scheme is developed by using the adaptive backstepping control methodology and co-designing the last virtual controller. The presented control scheme not only guarantees that all signals of the closed-loop system are bounded in the presence of actuator faults, but also ensures that the tracking error converges to a small neighborhood of the zero within the prescribed performance bounded. The computer simulation and comparison results demonstrate the effectiveness of the proposed fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

This article presents an adaptive control algorithm for stochastic nonholonomic systems subject to state constraints and input saturation simultaneously. We give a state‐input scaling transformation and a novel auxiliary variable to transform the stochastic nonholonomic system into a new one to make it easier to design the controller, and adopt a switching strategy to eliminate the phenomenon of uncontrollability. By introducing a barrier Lyapunov function and defining a suitable adaptive parameter, we construct a novel adaptive neural network controller with only one adaptive law, which can alleviate computation burden. The presented controller not only overcomes the effects of both full‐state constraints and full‐input saturation on system performance, but also ensures that all states of the closed‐loop are semi‐globally uniformly ultimately bounded in probability. Additionally, all state variables are restricted to the predefined compact sets. Finally, an example is used to validate the efficacy of the established controller. [ABSTRACT FROM AUTHOR]

This article investigates the command‐filter‐based improved predefined‐time tracking control problem for a class of switched nonlinear systems with the quantized input signal. Essentially different from most tracking control works of switched nonlinear systems under input quantization, the nonlinear decomposition method is employed to tackle the obstacles from the predefined‐time controller design. Subsequently, to achieve more faster convergence times, an improved predefined‐time lemma is designed, and for subsequent simulation work, the designed lemma is coupled with a quantizer. Furthermore, to tackle the problem of complexity explosion encountered during the iterative process of backstepping, the command filter is adopted. Finally, two examples are employed to demonstrate the effectiveness of the established results. [ABSTRACT FROM AUTHOR]

In this paper, a disturbance observer‐based adaptive neural backstepping integral sliding mode control (BISMC) is developed for a flexible joint robot (FJR) with the integration of an adjustable stiffness rotary actuator (ASRA). This system suffers from unknown system dynamics, external disturbance, and the influence of variable stiffness, which is a challenge for achieving precision tracking performance. Considering the lumped disturbances in FJR generated by the hydraulic system and the stiffness modulation of the ASRA, we investigate the structural dynamics nonlinear model of the FJR system, including hydraulic actuator dynamics. While other linear control strategies are applied for the FJR, the proposed controller uses BISMC, neural networks (NN), and nonlinear disturbance observers to deal with the disadvantages mentioned above. Radial basis function neural networks (RBFNN) are designed to tackle unknown nonlinear functions, and the disturbance observers are introduced to compensate for the influence of the variable stiffness, disturbance, and the approximation error caused by NN. Simulations and experiments are independently implemented to demonstrate the effectiveness and feasibility of the proposed controller. Results exhibit that the integral absolute error‐index is reduced by 20.4% when the proposed method is deployed for the experiment with a multistep trajectory. [ABSTRACT FROM AUTHOR]

This paper addresses the issue of actuator and sensor faults causing instability in leaderless/follower multi‐agent systems (MASs) and investigates adaptive fault‐tolerant control (FTC) algorithms to secure stability. Sensor faults can disrupt conventional observers by interfering with system state measurements. To address this issue, the descriptor approach is introduced to construct an augmented system from the original system, which converts the faults into components of the augmented system state. Next, by utilizing a sliding‐mode term to compensate for the lumped uncertainty's impact, an observer is designed to realize state estimation for the augmented system. Then, based on the estimations, adaptive FTC protocols are investigated for leaderless/leader‐follower MASs to retain stability against concurrent faults and uncertainties. To enhance control efficiency, the control gains are updated with the estimations from neighbors and proved to be ultimately upper bounded. Finally, the proposed method is validated through numerical simulations of multiple quadrotors. [ABSTRACT FROM AUTHOR]

In this article, we introduced a novel definition for a class of extended Nussbaum functions that have looser requirements than the traditional Nussbaum function. The extended Nussbaum function achieves comparable performance to the traditional Nussbaum function while addressing the challenges of unknown control directions and time‐varying actuator faults. We then provide a specific example function based on the extended Nussbaum function definition. Unlike the traditional Nussbaum function, which oscillates with fixed frequency and constantly increasing amplitude, this example function constrains the amplitude and oscillates with decreasing frequency. Consequently, its amplitude and derivative remain bounded. The oscillation of the system caused by the too large amplitude of the traditional Nussbaum function can be avoided. Furthermore, this example function is constructed using portions of elliptic curve. As a result, it is continuous and differentiable, in contrast to the normal decreasing‐frequency Nussbaum function using trigonometric function, which is only continuous. The overall closed‐loop system's global stability and asymptotic convergence of system states are successfully achieved. Simulation results demonstrate the effectiveness of the proposed function. [ABSTRACT FROM AUTHOR]

This research paper primarily focuses on the synchronization of a specific class of multi‐weighted coupled neural networks (MWCNNs) with switching topology, employing two adaptive control methods. In complex environments and under the influence of communication disturbances, the topology structures of coupled neural networks with multiple weights inevitably undergo time‐varying changes. To address this, we propose a novel type of MWCNNs with switching topologies. To ensure synchronization within this network, we develop sufficient conditions based on Lyapunov functional and inequality techniques. These conditions guarantee the achievement of synchronization. Moreover, we address the synchronization problem by employing node‐based and edge‐based adaptive controllers. Finally, we provide a numerical example to demonstrate the effectiveness of the obtained results. This example serves as empirical evidence showcasing the successful application of the proposed synchronization approach in practical scenarios. [ABSTRACT FROM AUTHOR]

In this article, the prescribed performance tracking control problem is studied for interconnected nonlinear time‐delay systems with sensor and actuator faults. Based on the dynamic gain technique and the backstepping control method, a delay‐independent adaptive state feedback controller is designed. Besides, by embedding a smooth function into the controller, the unknown external disturbances, sensor and actuator faults are successfully compensated. Furthermore, we can deal with the asymmetric prescribed performance control with semi‐global or global requirements uniformly by selecting the initial value of the the scaling function. Based on Lyapunov theory, it is proved that the system output of each subsystem can closely track the desired signal and the boundedness of all signals in the closed‐loop systems can be ensured under the proposed control method. Finally, the effectiveness of the proposed control strategy is proved by two simulation examples. [ABSTRACT FROM AUTHOR]

AbstractThe traditional time-between-events (TBE) control charts are developed in non-adaptive fashion assuming the Poisson process, where the TBE follows the exponential distribution. However, in many situations we need an adaptive strategy, especially for healthcare or system monitoring. The focus of this study is to introduce new exponentially weighted moving average and cumulative sum adaptive TBE control charts for high-quality processes based on nonhomogeneous Poisson process by assuming the power law intensity. Charts assuming the nonhomogeneous Poisson process are dynamic and can be used where the underlying failure rate may be changing over time. The performance of the proposed control charts is evaluated using the average run length and coefficient of variation of the run length distribution. Three real data examples which correspond to different shape parameters of the power law process are also discussed in the article. [ABSTRACT FROM AUTHOR]

A neural network adaptive integral terminal sliding mode control method with input saturation is proposed for the horizontal vibration problem of high‐speed elevator car systems caused by uncertainties such as guideway excitation and shaft piston wind change. First, considering the input saturation problem existing in the elevator control actuator, a class of smooth functions is introduced to approximate the nonlinearity of switching saturation, and an eight‐degree‐of‐freedom asymmetric anti‐saturation nonlinear system model of the high‐speed elevator car is established; second, in order to solve the singularity problem existing in the terminal sliding‐mode control, a nonlinear term is introduced into the sliding‐mode design, and a neural network is utilized for the fitting of the complex unknown function, and the design of the an adaptive integral terminal sliding mode controller (AITSMC), which enables the state variables of the system to achieve finite time convergence and proves the stability of the system by using Lyapunov theory; finally, under the action of two typical guide excitations, the proposed controller is compared with the passive control and adaptive control (AC), and the results show that, after adopting the proposed control method, the vibration acceleration eigenvalue is reduced by more than 60%, which effectively suppresses the horizontal vibration of the car system and verifies the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper investigates the practical prescribed time tracking for a class of uncertain nonlinear systems based on neural networks and event‐triggered control. Introducing a time‐varying constraint function transforms the original practical prescribed time‐tracking control issue into a tracking error constraint problem. An event‐triggered adaptive control has been proposed, which can effectively reduce the communication burden between the controller and the actuator. Using neural networks to approximate unknown nonlinear functions avoids the differentiation of virtual controllers, thereby reducing the computational burden. In addition, users can independently choose preset time and tracking accuracy without changing the control structure, which remains independent of the initial conditions and any design parameters. Finally, the effectiveness of this method is verified through simulation examples. [ABSTRACT FROM AUTHOR]

As for a class of strict‐feedback nonlinear systems with simultaneous sensor and actuator faults, the article focuses on the problem of fixed‐time fault‐tolerant control. Both fixed‐time command filters and a compensation mechanism are utilized to cope with these issues of differential explosion and filter errors. Meanwhile, through the fusion of fixed‐time control, fuzzy logic systems, adaptive control and the backstepping technology, a n$$ n $$‐step design scheme is presented. Our proposed controller ensures the fixed‐time convergence of all the signals in the system even with sensor/actuator faults, and allows the designers to acquire the upper limit value of the fixed convergence time only by tuning the design parameters. And this scheme takes into account rapidity, adaptability and fault tolerance of the system simultaneously. Based on the numerical simulation analysis, it is shown that the better tracking performance can be achieved. So it is concluded that our strategy is effective. [ABSTRACT FROM AUTHOR]

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field-oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances. [ABSTRACT FROM AUTHOR]

Wind turbine blades bear the maximum cyclic load and varying self-weights in turbulent wind environments, which accelerate the propagation of cracks that ultimately progress from minor faults, resulting in blade failure and significant maintenance and shutdown costs. To address this issue, this paper proposes an adaptive control strategy for the blade's useful life. The control system is divided into the inner control loop and the outer control loop. The outer loop is based on the Paris crack propagation model combined with a particle filtering algorithm and calculates the degradation of the blade life under the crack threshold conditions provided by the operation and maintenance strategy to determine the parameter settings of the inner-loop load-shedding controller. The control strategy we propose can balance the load-shedding capability of the controller with the fatigue load of the pitch actuator while considering the predefined remaining useful blade life in the operation and maintenance strategy, avoiding unplanned downtime and reducing maintenance costs. [ABSTRACT FROM AUTHOR]

For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton–Jacobi–Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed. [ABSTRACT FROM AUTHOR]

In this paper, a gradient dynamics-based control method is proposed to directly tackle the singularity problem in the backstepping control design of the TORA system. This method is founded upon the construction of an energy-like positive function, which includes an auxiliary variable in terms of the intermediate virtual control law. On this basis, a gradient dynamics is created to obtain a new virtual control command, which is capable of making the auxiliary variable gradually approach zero, thereby mitigating the issue of division by zero. The core innovation is the integration of the gradient dynamics into the recursive backstepping design to overcome the singularity problem and stabilize the system at the equilibrium quickly. In addition, it rigorously proves that all the signals in the closed-loop control system are uniformly ultimately bounded, and the tracking errors converge to a small neighborhood around zero through a Lyapunov-based stability analysis. Comparative simulations demonstrate that the proposed approach not only avoids the singularity issue, but also achieves a better transient performance over other methods. [ABSTRACT FROM AUTHOR]

This paper proposes a combined adaptive virtual Inertia and adaptive damping control of a virtual synchronous generator (AID-VSG) to improve the dynamic frequency response of microgrids. In the proposed control scheme, the VSG's virtual inertia and damping coefficients adapt themselves during the transients to, respectively, reduce frequency deviations and increase the oscillations' damping. In addition, as an important feature, the proposed AID-VSG is suitable for distributed control scheme applications and is designed to not rely on phase-locked loop (PLL) measurements, which avoids PLL stability issues on weak grids. The control parameters of the proposed AID-VSG are tuned by the particle swarm optimization (PSO) algorithm to minimize the overshoot and settling time of the microgrid's frequency during an islanding event. The AID-VSG is validated by a comparative analysis with three existing VSG control schemes, also tuned by the stated optimization algorithm. The performance of each compared VSG strategy is evaluated through the simulation of a set of 10,000 initial conditions, using the islanded microgrid's nonlinear model. The best response among the VSG strategies was achieved by the proposed AID-VSG control for both the optimization problem and the set of initial conditions' simulations. [ABSTRACT FROM AUTHOR]

A large input delay, parametric uncertainties, matched disturbances and mismatched disturbances exist extensively in variable air volume air-conditioning systems, which can deteriorate the control performance of the room temperature and even destabilize the system. To address this problem, an adaptive-gain command filter control framework for the room temperature of variable air volume air-conditioning systems is exploited. Through skillfully designing an auxiliary system, both the filtered error and the input delay can be compensated concurrently, which can attenuate the effect of the filtered error and the input delay on the control performance of the room temperature. Then, a smooth nonlinear term with an adjusted gain is introduced into the control framework to compensate for parametric uncertainties, matched disturbances and mismatched disturbances, which relieves the conservatism of the controller gain selection. With the help of the Lyapunov theory, both the boundedness of all the system signals and the asymptotic tracking performance for the room temperature can be assured with the presented controller. Finally, the contrastive simulation results demonstrate the validity of the developed method. [ABSTRACT FROM AUTHOR]

In the face of increasingly complex and dynamic access scenarios, traditional access control exhibits limitations in flexibility, to some extent impairing the availability of resources. To address this issue, this paper proposed a context-aware adaptive access control model(CABAC). This model enhances flexibility and temporal efficiency by incorporating additional special authorization mechanisms to handle specific requests, ultimately improving resource availability. Utilizing a contextaware reasoning approach grounded in fuzzy logic to evaluate the contextual situation during user access, the system could make special authorization decisions, thereby achieving a flexible and fine-grained authorization capability suitable for dynamic environments. Introducing a trust mechanism confined users' special authorizations, preventing misuse of privileges. Simultaneously, tracking and monitoring user activities during sessions were conducted to provide adaptive access control capability. Experimental results demonstrate that CABAC can adapt to dynamic and complex access control scenarios, dynamically adjusting permissions according to different contexts, thereby enhancing resource availability. [ABSTRACT FROM AUTHOR]

In order to suppress the influence of uncertain disturbances on the trajectory tracking of hydraulic manipulator, a composite control strategy for the cutting electro-hydraulic driving system (CEHDS) of the tunneling robot is presented, which synthesizes the advantages of neural networks technique, recursive backstepping and adaptive control theory. The Lagrangian model with actuator dynamics is derived based on the practical tunneling robot. The back-stepping method is utilized for the strictly feedback state-space model. To address the matched and unmatched lumped uncertainties, the radial-basis-function neural networks (RBFNNs) are employed to approximate the unmatched term which contains the nonlinear friction torque and external cutting load in the mechanical subsystem. The nonlinear disturbance observer (NDOB) is utilized to estimate the matched lumped uncertainty in the hydraulic subsystem. Simultaneously, the adaptive robust mechanism is proposed to compensate the residual disturbances. Based on the Lyapunov theorem, the stability and the bounded tracking error of the CEHDS are obtained. The simulation and experimental results validate the effectiveness of the proposed method in comparison with the common backstepping and PID-controller approaches. [ABSTRACT FROM AUTHOR]

Background: While it is well recognized that an automated insulin delivery (AID) algorithm should adapt to changes in physiology, it is less understood that the individual would also have to adapt to the AID system. The adaptive biobehavioral control (ABC) method presented here attempts to compensate for this deficiency by including AID into an information cloud-based ecosystem. Methods: The Web Information Tool (WIT) implements the ABC concept via the following: (1) a Physiological Adaptation Module (PAM) that tracks metabolic changes and adapts AID parameters accordingly and (2) a Behavioral Adaptation Module (BAM) that provides information feedback. The safety of WIT (primary outcome) was assessed in an 8-week randomized, two-arm parallel pilot study. All participants used the Control-IQ® AID system enhanced with PAM, but only those in the Experimental group had access to BAM. Secondary glycemic outcomes were computed using the 2-week baseline period and the last 2 weeks of treatment. Results: Thirty participants with type 1 diabetes (T1D) completed all study procedures (17 female/13 male; age: 40 ± 14 years; HbA1c: 6.6% ± 0.5%). No severe hypoglycemia, DKA, or other serious adverse events were reported. Comparing the Experimental and Control groups, no significant difference was observed in time in range (70–180 mg/dL): 74.6% vs 73.8%, adjusted mean difference: 2.65%, 95% CI (−1.12%,6.41%), P = 0.161. Time in 70–140 mg/dL was significantly higher in the Experimental group: 50.7% vs 49.2%, 5.71% (0.44%,10.97%), P = 0.035, without increased time below range: 0.54% (−0.09%,1.17%), P = 0.089. Conclusion: The results demonstrate that it is safe to integrate an AID system into the WIT ecosystem. Validation in a full-scale study is ongoing. [ABSTRACT FROM AUTHOR]

Modeling the behavior of a prototype cantilevered X‐ray adaptive mirror (held from one end) demonstrates its potential for use on high‐performance X‐ray beamlines. Similar adaptive mirrors are used on X‐ray beamlines to compensate optical aberrations, control wavefronts and tune mirror focal distances at will. Controlled by 1D arrays of piezoceramic actuators, these glancing‐incidence mirrors can provide nanometre‐scale surface shape adjustment capabilities. However, significant engineering challenges remain for mounting them with low distortion and low environmental sensitivity. Finite‐element analysis is used to predict the micron‐scale full actuation surface shape from each channel and then linear modeling is applied to investigate the mirrors' ability to reach target profiles. Using either uniform or arbitrary spatial weighting, actuator voltages are optimized using a Moore–Penrose matrix inverse, or pseudoinverse, revealing a spatial dependence on the shape fitting with increasing fidelity farther from the mount. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications. [ABSTRACT FROM AUTHOR]

We proposed an online identification and adaptive control framework for the nonlinear dynamical systems using a novel hybrid‐feed‐forward recurrent neural network (HFRNN) model. The HFRNN is a combination of a feed‐forward neural network (FFNN) and a local recurrent neural network (LRNN). We aim to leverage the simplicity of FFNN and the effectiveness of RNN to capture changing dynamics accurately and design an indirect adaptive control scheme. To derive the weights update equations, we have applied the gradient‐descent‐based Back‐Propagation (BP) technique, and the stability of the proposed learning strategy is proven using the Lyapunov stability principles. We also compared the proposed method's results with those of the Jordan network‐based controller (JNC) and the local recurrent network‐based controller (LRNC) in the simulation examples. The results demonstrate that our approach performs satisfactorily, even in the presence of disturbance signals. [ABSTRACT FROM AUTHOR]

This paper developed a scalable parallel computing method that can be used for platoon simulations and controller validations. A scalable adaptive platooning control law was firstly designed, which accommodates a variety of vehicle-to-vehicle communication topologies. A road vehicle dynamics model that considered the Magic Formula tyre model and suspension dynamics was then derived and validated. The parallel computing method adopted the Message Passing Interface technique to allow fast and scalable simulations. Platoon length changes do not require controller and algorithm changes. An 11-vehicle platoon on a real-world 10 km long road section was simulated. Different localisation sensor errors, communication delays, heterogenous vehicle masses and driving modes were considered. Results show that localisation errors have negligible influences on space errors. Aggressive driving and heterogeneous vehicle masses slightly increase space errors (increases less than 0.23 m). Communication delays are the greatest influencer for space errors. Increases for 15, 45 and 75 ms delays were 0.43, 1.41 and 2.41 m, respectively. It is further shown that parallel computing can improve the computing speed by three times on personal computers and seven to 12 times on workstations. [ABSTRACT FROM AUTHOR]

Surface movement exploration techniques are an important approach to further the understanding of small celestial bodies. Hopping movement is employed to take advantage of the weak gravitational field of small celestial bodies, facilitating long-distance mobility of the rover. The rover exhibits a strong attitude-orbit coupling characteristic during the hopping movement. Different contact attitudes contribute to the challenge of determining the rebound trajectory, rendering it difficult for the rover to maintain continuous movement towards the target position along the nominal trajectory. In this paper, a dual-stage control method for continuous surface hopping movement is developed, wherein the control is applied separately during the flight stage and the collision stage. During the flight stage, applying attitude control enables the rover to establish contact with the surface in a specific attitude, ensuring accuracy in movement direction and creating favorable conditions for the control process in the collision stage. The flywheel is controlled in the collision stage to correct the state change and energy loss due to the collision, enabling the rover to attain the desired hopping state. Considering the energy consumption of the rover, the minimum hopping velocity sequence and angular velocity sequence are designed based on the hopping angle and heading angle. The intricacies and uncertainties inherent in the surface environment of small celestial bodies pose challenges for the rover during hopping and flying. In response to the issue, a finite-time adaptive sliding-mode control law is devised. The control law facilitates the rapid adjustment of the rover to the target attitude in flight, demonstrating strong robustness. In addition, by integrating the changes in contact point velocity with the impulse contact model, the accuracy of the rover's rebound state updates is enhanced. Finally, a set of simulations is performed, and simulation results indicate that the dual-stage control method can achieve a final landing position error of less than 0.2 m for a continuously hopping. The finite-time adaptive sliding-mode control law can stabilize the rover's attitude within 10 s. Conducting 500 Monte Carlo simulations of the continuously hopping rover in three small celestial body surface simulation environments shows that the position endpoint error is within 0.3 m. • An innovative control method for continuous hopping on the surface of small celestial bodies with low gravity is proposed. • The attitude-orbit coupling challenge is addressed through a staged control. • A sequence of hopping states with minimum velocities is devised. • A finite-time adaptive sliding-mode control law is designed to effectively address unknown disturbances. • A corrected sliding-mode control law is developed for precise hopping state updates. [ABSTRACT FROM AUTHOR]

• The adaptive nonlinear damping is integrated into lateral suspension to improve hunting stability of high-speed train. • Three typical hunting bifurcations with different wheel/rail equivalent conicity functions are analyzed. • An adaptive constraint-following control method is employed to implement nonlinear damping via active suspension system. • The proposed method can significantly improve both carbody and bogie hunting stability. This study employs an active secondary suspension with adaptive nonlinear damping to enhance the hunting stability of high-speed trains. Adjusting passive suspension parameters to optimize ride comfort and hunting stability simultaneously in varied extreme operational conditions poses a significant challenge for high-speed trains. This research integrates high-order displacement-dependent nonlinear damping into the secondary lateral suspension, drawing on insights from field test data. We analyze three typical hunting motion bifurcations: carbody hunting and both subcritical and supercritical bifurcations of bogie hunting. Theoretical analysis demonstrates that (a) the adaptive nonlinear damping narrows the unstable speed range and reduces the amplitude of the limit cycle in carbody hunting, (b) while it does not increase the critical speed for supercritical bogie hunting bifurcation, it substantially reduces the amplitude of the limit cycle, and (c) it increases the nonlinear critical speed for subcritical bogie hunting bifurcation, with the potential to alter the bifurcation from subcritical to supercritical, which holds considerable practical significance. Implementing such nonlinear damping directly through passive structure is challenging. Therefore, in view of this underactuated control problem, the constraint-following control is applied in the active suspension system to inherit the benefits of adaptive nonlinear damping. The simulation results show that the proposed active suspension system can effectively suppress the abnormal vibration caused by hunting motions. [ABSTRACT FROM AUTHOR]

Summary: Problems like intermittent photovoltaic (PV) power, disconnected PV arrays, etc. are brought on by the extensive integration of PV generation. In this article, a single‐stage PV‐integrated grid‐tethered system is controlled using a reconfigurative multiple random features kernel mean p‐power (MRFKMP) control algorithm. The algorithm circumvents the dependence of kernel parameter in the classic random Fourier features mapping and lower the computing complexity in the traditional multiple kernel learning adaptive filter. MRFKMP algorithm with a linear filter structure can reduce both the computational and storage cost. This is the novel application of MRFKMP algorithm to extract fundamental component of nonlinear load currents in PV‐integrated grid‐tied system. Furthermore, to increase the reliability of the system, a reconfigurable control is developed, which adaptive regulates DC link voltage from PV mode (PVM) during PV hours to active power filtering mode (AFM) during PV nonavailability/PV disconnection. On a created laboratory testbed, the applied control's practical feasibility for numerous scenarios is evaluated. Comparison with existing control algorithms reveals that the MRFKMP controller outperforms the LMF based PV system by 79% less peak‐to‐peak load current ripple and 60% less than the best performing SRF control algorithm. The grid current THD is 2.60%, which is 30% low than that presented by SRF technique. [ABSTRACT FROM AUTHOR]

The study targets uncertain coupling faults in robotic arm actuators and proposes a new fault-tolerant visual servo control strategy. Specifically, it considers both multiplicative and additive actuator faults within the dynamic of the robotic arm, treating the coupling faults and time-varying disturbances as an aggregate of concentrated uncertainties. A radial basis function neural network-based state observer is introduced to online approximate these concentrated uncertainties, which include fault information, eliminating the need for prior knowledge of faults. Furthermore, a fault-tolerant controller based on a non-singular fast terminal sliding mode is proposed, which separately decouples the nominal quantities and concentrated uncertainties and develops individual adaptive control laws for each. This effectively reduces the detrimental impact of coupled faults and disturbances on the system's performance, facilitating image feature trajectory tracking control with minimal jitter, high precision, and strong transient response capabilities. The stability of the state observer and the fault-tolerant controller has been substantiated through Lyapunov's theory. Lastly, numerical simulations validate the efficacy and robustness of the proposed fault-tolerant visual servo control approach. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive lateral control problem is considered for autonomous electric vehicles under denial-of-service (DoS) attacks. Autonomous electric vehicles require high-precision position and pose information for the lateral control, which can be obtained from the global positioning system (GPS). However, this kind of sensors is easily susceptible to malicious DoS attacks. To mitigate the performance degradation caused by DoS attacks on GPS, a state observer is designed based on the measurements of inertial measurement units (IMUs), such that the position and pose information of vehicles can be estimated. Then, by adopting the observer states and backstepping technique, a novel adaptive secure switching control scheme is proposed. Subsequently, a stability condition is derived, which guarantees that the tracking errors converge to a compact set, and the attack effects on system dynamic performance are weakened. The stability condition also reveals the relationships among the attack durations, design parameters, and tracking errors, which can be used to guide the selection of design parameters. Simulation results are provided to validate the effectiveness and robustness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the event-triggered control for uncertain switched systems under injection attacks. An adaptive event-triggered control method for neural network–approximated switched systems (NNA-SSs) is proposed. The main works are as follows: First, a neural network is introduced to approximate the uncertain nonlinear item of the systems. Second, the observer-based adaptive event-triggering (OB-AET) strategy is designed to efficiently utilize communication and computing resources. Furthermore, the closed-loop switched systems considering injection attacks are established. By utilizing the Lyapunov function method and average dwell time technique, sufficient conditions for the exponential stability of the closed-loop switched systems are given. Accordingly, the gains of the state feedback controllers and observers are solved. Finally, simulation examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]