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Zero voltage switching (ZVS) of power semiconductor devices is an effective way to improve the efficiency and power density of the power electronic converters with extremely high switching frequency. This article aims to analyze and realize the true full-range ZVS operation of a totem-pole power factor correction circuit. The available ZVS control strategies are investigated, and the conditions for the realization of full-range ZVS operation are analyzed. It is revealed that the existed control strategies did not consider the influence of the dead-time, resulting in the failure of ZVS operation in some input voltage ranges. To address this issue, two feasible retrofitted control strategies, namely the turn-off current optimization control and the adaptive dead-time control, are proposed. The merits and drawbacks of the two control strategies are carefully analyzed and compared. It is found that the adaptive dead-time control can not only achieve the true full range ZVS but also significantly reduce the loss of the freewheeling diodes. The design consideration of key parameters is discussed. Finally, the feasibility of the proposed control and the correctness of the theoretical analyses are verified by simulation and experiment. [ABSTRACT FROM AUTHOR]

This article focuses on the fast finite-time tracking consensus for uncertain nonlinear multiagent systems. By gracefully combining the hierarchical decomposition, adding a barrier power integrator and disturbance compensation techniques, a new adaptive fast finite-time controller is proposed. Compared with the existing works, the proposed method has several distinguishing features: 1) It can achieve fast finite-time convergence with full state constraints; 2) it can deal with some fully unknown nonlinearities and state-dependent disturbance. Moreover, the nonlinearities can be related with the states of all the followers; 3) the input of the leader is not required to be zero, meaning a broad class of references signals can be generated; and 4) the controller is computationally simply and ready to be implemented. No fuzzy logic/neural networks are needed. Applications of the proposed method on multiple servo motors are also studied. [ABSTRACT FROM AUTHOR]

Unexpected faults that occur in the sensors of the single-phase grid-connected converter (SGC) may deteriorate the control performance ofthe grid current and dc-link voltage and pose risks to the connected distributed generation system. This article proposes an unknown input sliding mode observer (UI-SMO) based sensor fault identification and remediation strategy. Therein, an UI-SMO is established that integrates the observations of the grid current and dc-link voltage with the reconstruction of the unknown input (i.e., load condition of the SGC). Such an observer-based analytical redundancy generates the residuals that indicate the abnormalities of the sensors for fault identification. An adaptive fault-tolerant control (AFTC) strategy for the SGC is developed, where an automated control system reconfiguration with unknown input adaptation is seamlessly implemented by substituting the data reported by the faulty sensors with their observed values. Extensive simulation and experimental studies validate the effectiveness of the proposed UI-SMO-based sensor fault identification and AFTC strategy of the SGC. [ABSTRACT FROM AUTHOR]

In this article, for robotic systems with uncertain dynamics and time-varying asymmetric output constraints, we present a composite learning finite-time control scheme with salient features benefited from two design steps. In the first step, unlike existing composite adaptive/learning control algorithms, which result in either exponential convergence or finite-time convergence but exhibit a potential singularity issue, a modified nonsingular terminal sliding mode-based composite learning controller is adopted such that both tracking error and parameter estimation error converge to zero in finite time without singularity. The unknown parameter learning law is constructed by using online historical data and regressor extension, which gives a benefit of relaxing the typically required stringent persistent excitation with a much weaker excitation condition termed interval excitation. In the second step, a universal time-varying asymmetric barrier function (UTABF) is adopted to directly constrain the system output rather than the most commonly used barrier Lyapunov function, which indirectly converts the output constraints into the conservative tracking error constraints. Moreover, the UTABF can handle both constrained and unconstrained cases uniformly without the need for changing the control structure. Both theoretical analysis and experiments results on an industrial manipulator confirm the benefits and effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this article, a robust disturbance rejection controller is designed for quadrotor attitude tracking within dynamic safety operational envelopes. A scalable constraint technique based on the unified barrier function is developed to transform the attitude restricted within the dynamic safety envelope into a new unconstrained state variables. The closed-form state stabilization control input based on the operational safety rules derived from backstepping method with dynamic surface control can simultaneously guarantee the safety and asymptotic stability of underlying system. A sliding mode disturbance observer is used to estimate and compensate for unknown time-varying perturbations with minor chattering and rapid convergence. Finally, numerical simulations and platform experiments are conducted to verify the effectiveness of the proposed dynamic geofencing attitude control. [ABSTRACT FROM AUTHOR]

Human intelligence, cognition, and skills can be applied to robots to accomplish challenging tasks through cooperative control between humans and robots. In this article, we propose two control frameworks for the human teleoperation of multiple robots under undirected and directed graphs. The two control laws are considered for two cases: 1) when both the relative positions and velocities of the robots are available, and 2) when only the relative positions are available. In this article, we first propose a controller that integrates adaptive neural networks, task-space synchronization, and robust control to address several practical issues related to uncertainty in robotic systems, (e.g., model inaccuracy, time delay, and disturbance). Second, another control algorithm using only the relative position information is addressed by using distributed observers on the remote side. As a result, the system is proven to be stable, and the tracking errors of position and velocity are uniformly and ultimately bounded. Finally, the effectiveness of the proposed control algorithms is verified by experimental demonstration. [ABSTRACT FROM AUTHOR]

Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case. [ABSTRACT FROM AUTHOR]

In this article, we consider adaptive output feedback tracking for a class of nonlinear systems with uncertain parameters, multiplicative and additive sensor uncertainties, and external disturbances. A state observer and an adaptive controller are constructed based on gain scaling technique by which a dynamic gain is designed to compensate for the effects of the sensor uncertainties. The adaptive controller is linear-like and, therefore, is simple and easy to implement. It is shown that with this proposed scheme, all states of the closed-loop system are bounded and the real tracking error can converge to an arbitrarily small residual set. Simulation and experiment are presented to illustrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

If one cannot obtain the accurate prior information of the controlled object in advance, it will take much effort to get good dynamic performance. To this end, this article proposes an online estimation method to deal with disturbances and uncertainties for a class of systems with uncertain control gain. Based on the extended-state observer scheme, the estimation problem of disturbance and uncertain control gain is transformed to the convex optimization problem and solved by the sign-projected gradient flow, then the estimation can be applied to compensate disturbance and design controller. Moreover, the exponential and ultimate boundedness is obtained for the derived closed-loop system, together with rigorous theoretical analysis and proof. Finally, both the numerical simulations and the experiment on a brushless dc motor demonstrate that the proposed design has a better tracking performance and stronger robustness to deal with disturbances and uncertainties compared with the existing methods. [ABSTRACT FROM AUTHOR]

In this article, a butterfly-like Prandtl–Ishlinskii (PI) hysteresis model and a novel neural network based adaptive implicit inverse control scheme to describe and control the butterfly-like hysteresis are proposed. The main contributions are: 1) a butterfly-like PI model is developed for the purpose of predicting the hysteresis effects and the model is feasible for controller design; 2) an implicit inverse control scheme especially for mitigating the butterfly-like hysteresis is implemented, which avoids the construction of the direct inverse of the butterfly-like PI model; 3) an adaptive implicit inverse control approach, which integrates the neural network and the implicit inverse technique into the output-feedback control is developed for eliminating the butterfly-like hysteresis and an arbitrarily small $L_{\infty }$ norm of tracking error is achieved. The proposed modeling and control methods are validated experimentally via the dielectric elastomer actuator based motion control platform. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

This study is concerned with the dynamical analysis and vibration attenuation for a three-dimensional (3-D) suspension cable system of a helicopter preceded by output constraints and input backlash. First, the dynamic model of a 3-D suspension cable is constructed in accordance with Hamilton’s principle. Then, a backlash inverse compensator is established to tackle the input backlash nonlinearities. Subsequently, three boundary controllers together with output signal barrier functions are proposed to dampen the oscillations while not violating the cable swing constraints. The uniform boundedness of the closed-loop system is guaranteed with recourse to the direct Lyapunov’s method. Finally, the validity and efficiency of the derived control laws are testified through simulation results. [ABSTRACT FROM AUTHOR]

In this article, event-triggered (ET) observers and ET-distributed ${\boldsymbol {\mathcal {H}}_{\boldsymbol \infty }}$ controllers are investigated for physically interconnected nonholonomic mechanical agents in harsh conditions, such as skidding, slipping, and dead-zone disturbances. The agents’ models are presented by strict-feedback nonlinear large-scale systems, but unlike the existing studies, unknown dynamics with feedback outputs are considered, and assumptions of polynomial-type nonlinearities for physical interconnection functions are relaxed. Initially, the observer of unmeasurable states via the outputs is designed. Then, ET augmented controllers are established to transform the physically interconnected system into isolated subsystems connected by a communication network. By utilizing the local information of each agent and adaptive dynamic programming (ADP), ET-distributed ${\boldsymbol {\mathcal {H}}_{\boldsymbol \infty }}$ optimal control laws and disturbance rejection laws are derived. The parameters of the ET observers and controllers are synchronously updated online by event-triggering mechanisms; thus, it reduces computational complexity and communication. It is guaranteed that the states in the closed dynamics are $L_{2}$ -gain bounded and the Zeno behavior is avoided. Additionally, the convergence of cost functions to the near-optimal values is accelerated by a concurrent learning technique, avoiding dependence on the online examination of the persistence of excitation conditions. Finally, the control performance of a nonholonomic multirobot system in a comparative simulation study shows that the proposed observers and controllers are effective. [ABSTRACT FROM AUTHOR]

This article presents an effective method to address the tracking control problem arising in uncertain switched high-order nonlinear system in strict-feedback form. The system under consideration contains unknown functions, which causally make the asymptotic tracking performance difficult to be achieved. By adopting the adding a power integrator approach in the framework of backstepping, a novel tracking controller is developed to guarantee an asymptotic tracking performance in the presence of the approximation error cased by neural networks (NNs) under arbitrary switching. The main contributions lie in: 1) the article for the first time embeds the backstepping technique in designing a kind of discontinuous controller with proportional integral (PI) compensation and 2) with the help of Filippov’s theory, a new defined system described by differential inclusions can be first obtained by taking some transformations, and then a novel nonsmooth Lyapunov function approach along with its upper right Dini derivative technique is applied to complete the construction of the discontinuous controller. Finally, two simulation examples are exhibited to verify the validity of the proposed design techniques. [ABSTRACT FROM AUTHOR]

This article is concerned with the consensus formation control problem of a class of networked quadrotor aircraft partially bounded and state-dependent perturbations. A general distributed consensus error model is first developed to formulate the formation behavior of the networked quadrotor aircraft. Then, by using adaptive techniques, virtual position control strategies are proposed to eliminate the impacts of perturbations, so that the following quadrotor aircraft can boundedly track the desired position trajectory with a satisfying pattern. Furthermore, based on the designed virtual position control strategies, the attitude reference angles are constructed and adaptive attitude control strategies are further designed to guarantee that the attitude angles track the reference angles asymptotically. In terms of the asymptotic tracking results of attitude control systems, the bounded consensus formation results are obtained based on the Lyapunov stability theorem. Numerical simulations are carried out to verify the efficiency of the designed position formation as well as attitude tracking control strategies of the networked quadrotor aircraft. [ABSTRACT FROM AUTHOR]

This article presents an adaptive control framework for dynamically reconfigurable battery (DRB) systems based on the deep reinforcement learning method. The proposed adaptive control framework relies on deep Q-network to learn the DRB system operations. By utilizing its model-free nature, the proposed framework can significantly reduce the complexity of building experiences or expert models for DRB systems as well as improve battery operating time by ensuring cell balancing. Extensive simulation and experimental study has been carried out with data gathered from a real-world DRB testbed, and the results show the effectiveness and efficiency of the proposed control framework. [ABSTRACT FROM AUTHOR]

An adaptive robot control approach that can achieve an accurate approximation of the complex robot dynamics without knowledge unlearning in the case of multiple sequential tasks is proposed in this article. A locally weighted learning (LWL) model with automatic structure growth according to the size of the learning domain is introduced to approximate unknown robot dynamics, and a composite learning technique with regressor extension is applied to improve parameter convergence. The LWL ensures that learning in one area of the learning domain does not cause unlearning in another area, and the composite learning theoretically guarantees the identification performance of the LWL model. By the exploitation of both stored data in memory and instantaneous data, parameter convergence in the LWL model is achieved under a more achievable interval-excitation condition than the stringent persistent-excitation condition. This further enhances the trajectory tracking performance for multiple sequential tasks, which is generally not achievable by existing approximation-based adaptive robot control approaches. Experimental studies have verified the superiority of the proposed approach over prevalent approaches. [ABSTRACT FROM AUTHOR]

This article presents a saturated adaptive-law-based backstepping control method for a class of uncertain nonlinear systems with external dynamic disturbances. In this approach, a novel adaptive backstepping control method with a new command filter is primarily proposed to design the feedback controllers. In order to address the system unknown nonlinearities caused by external dynamic disturbances, a method named “saturated adaptive law approach” is used to estimate the system unknown nonlinearities online. Meanwhile, a filter called “dual command filter” is applied to estimate the derivative of certain signal such that the saturated adaptive law approach can be well implemented. With the help of Lyapunov stability criteria, it is proved that the target signal can be tracked by the output of the system with small error. Finally, our control method is applied to a quadrotor hover system, and two sets of comparison experiments are given to show the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This article addresses a novel adaptive compensation strategy for a flexible manipulator in the presence of unmodeled system dynamics and an infinite number of time-varying actuator faults. Differing from the existing results, the unmodeled system dynamics are represented by a nonlinear time-varying delay function, which is tackled with the aid of neural network technique and Lyapunov–Krasovskii function. By introducing the projection technique and a piecewise Lyapunov function, a fault-tolerant control method is proposed, the uniformly ultimate boundedness of the closed-loop system is ensured, and the number of actuator faults is allowed to be infinite. Simulation and physical experiment results show that the manipulator can track the desired angle and the vibration can be suppressed effectively under the proposed scheme for the system with uncertainties and actuator faults. [ABSTRACT FROM AUTHOR]

In this article, the geometric adaptive robust hierarchical control strategy is proposed for underactuated quadrotor, in which the negative effects of nonlinearities, uncertainties, and coupling are thoroughly considered and effectively attenuated. For handling nonlinearities, geometric control is adopted, i.e., the attitude error is directly defined on tangent space of rotation group, thus better attitude tracking performance can be achieved. To deal with uncertainties, a comprehensive dynamic model is established with compound damping terms representing the main aerodynamic effects and all inertia parameters including mass, inertia tensor, and center of gravity. Then, integrated direct/indirect adaptive robust control is developed with adaptive compensation of aerodynamic damping and complete inertia, which is beneficial to improve tracking performance. As for the coupling, hierarchical control architecture is utilized to relax the strong coupling between attitude and translational subsystems. Moreover, the stability of the overall closed-loop hierarchical system, together with transient performance and final tracking accuracy, has been rigorously analyzed without time-scale separation assumption. Finally, comparative experiments demonstrate the better performance of the proposed method in terms of tracking accuracy and robustness. [ABSTRACT FROM AUTHOR]

An industrial integrated energy system gathers a variety of production capacity and energy-related units, which also involves the production, transformation, and consumption of cold, heat, power, gas, etc. The combined cooling, heating, and power (CCHP) system built upon the cascade utilization of energy plays a significant role in improving the utilization rate of multiple energy resources. In this article, a controlled hierarchical relevance vector machine is modeled for identifying the scheduling event in the by-product gas system of a steel industry, which can effectively overcome the disturbance triggered by the changing of operational modes (OMs). Considering the multiple time scales (MTSs) property and the changing of seasons and load conditions of the CCHP, a unified modeling approach is proposed to describe MTSs and multiple OMs of the CCHP by using fuzzy singular perturbation here. Furthermore, an extended $Q$ -learning approach by incorporating a hierarchical interval type-2 fuzzy system is designed for dynamically choosing the optimal controller’s parameter for the proposed unified model along with the changing of OMs. The performance of the proposed method can be experimentally validated by a series of industrial application cases to highlight the necessity of modeling and self-adaptive parameter of the controller. [ABSTRACT FROM AUTHOR]

Line-commuted converter-based high-voltage direct current (LCC-HVDC) has undergone extensive development in power systems. Commutation failure (CF) in LCC-HVDC poses a considerable threat to the safe and stable operation of power systems. The coupling of electrical quantities, including DC current, DC voltage, AC voltage, and extinction angle (EA) of an inverter station, is a critical factor that makes CF difficult to suppress effectively. Existing methods adopt preset fixed control parameters and a single control target. The demand of CF recovery typically cannot be satisfied. Control even becomes the driving force of subsequent CF (SCF). Accordingly, a new concept of a fault security region (FSR) in an inverter station under the effects of multi-electrical quantities is proposed. The security range under the coupling effects of electrical quantities is described by the active power and exchanged reactive power of the inverter station. EA, active power, and exchanged reactive power are coordinated through the adaptive regulation of DC current. Thus, SCF can be prevented under the condition that the effect of varying active power and reactive power of LCC-HVDC on a power system is balanced. First, the coupling characteristics of multi-electrical quantities of the inverter station are analyzed. Second, the modeling method for the FSR in the inverter station is proposed. Third, an adaptive current control method is developed by analyzing the evolution characteristics of the FSR. Moreover, a calculation method for the DC current order value is presented. Finally, the effectiveness of the proposed method is verified using the CIGRE HVDC standard system. [ABSTRACT FROM AUTHOR]

Predictive speed control (PSC) is a promising control strategy for high dynamic performance electrical drives. However, its implementation faces two challenges: the selection of weighting factors and the elimination of steady-state errors (SSEs). The weighting factors are commonly selected through trial-and-error, which is time consuming. Additionally, SSEs are frequently observed due to model uncertainties and parameter mismatches. Focusing on the aforementioned issues, this article proposes a PSC strategy for surface-mounted permanent-magnet synchronous motor drives, where the weighting factors are designed algebraically and SSEs are eliminated. The weighting factor design process is illustrated through rearranging the equivalent speed tracking error. The SSEs are eliminated by incorporating integral terms into the cost function. The performance of the proposed strategy is validated through comparative experiments with field-oriented control and adaptive integral sliding-mode predictive control. The results demonstrate the effectiveness of the designed weighting factors and the robustness of the system. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) strategy can provide significant benefits for controlling nonlinear systems over classical cascade field-oriented control (FOC). However, the MPC is still in the development stage for a high-performance predictive model. Therefore, the proposed MPC in this article updates the internal linear predictive model at each time step to accurately represent the nonlinear plant of a permanent-magnet synchronous motor (PMSM) plant over different speed regions. In other words, the adaptive discrete linear plant model (ADLPM) is designed to update the current operating conditions of the machine parameters and the equilibrium points of the measured stator currents, speed, and load torque. For the operation in the flux-weakening region, the proposed MPC depends on a performance control algorithm (PCA) to obtain high dynamic performance. In this PCA algorithm, the proposed MPC depends on the modified reference velocity rather than the original reference velocity, which can calculate the required d-axis current directly. Moreover, the proposed cost function is designed directly in terms of the error values of the velocity and d-axis current, which fits the motor performance based on the further constraint of the maximum magnitude of the drawn stator current provided to control the acceleration of the rotor. Finally, comprehensive simulations and experiments have fully demonstrated that the proposed MPC can reduce the speed drop, and torque ripple in response to those of the FOC and traditional MPC strategies. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The nonignorable grid impedance is one of the characteristics of a weak grid. Meanwhile, the filter inductance also fluctuates with temperature or aging factors. The resonant frequency offset of an LCL filter will be caused by the change in power grid impedance and filter inductance. To this end, a strong adaptability control based on dual-division-summation (D-D-Σ) control is proposed in this article, which considers both the accuracy of grid-connected current tracking and resonance suppression under the variation of grid impedance and filter inductance. A full inductance parameter identification method based on an extended Kalman filter for simultaneously estimating filter inductance and grid impedance is proposed without remote voltage sampling. An enumeration method of the Routh array with less calculation is proposed. The algebraic relationship between control parameters and total inductance parameters is obtained by the Routh criterion. A gain correction method to realize strong adaptive control is proposed to deal with the variation of grid impedance and filter inductance. The experimental results prove the feasibility of D-D-Σ control in a three-phase LCL-type grid-connected inverter under the variation of grid impedance and filter inductance. [ABSTRACT FROM AUTHOR]

Compared with stochastic differential equations (SDEs) driven by white noise, random differential equations (RDEs) generated by colored noise are claimed to be more practical. This article considers reduced-order high-gain observer (ROHGO)-based neural tracking control on random nonlinear systems having output delay. In order to foster the design and analysis, the estimated states and the estimation errors are scaled by the high gain of the observer. Based on neural network (NN) approximation and state observation, an adaptive controller is designed for the overall system using the backstepping method. It is proved that all the closed-loop signals are bounded almost surely, letting alone the tracking error. By tuning the related design parameters, the asymptotic tracking error could be regulated arbitrarily small. Within the best of our knowledge, this article serves as the first attempt for NN-based control on RDE systems. Finally, the validity of main results is confirmed by a simulation example. [ABSTRACT FROM AUTHOR]

This article presents the observer-based event-triggered adaptive hybrid fuzzy dynamic surface control strategy for a category of uncertain nonstrict-feedback fractional-order nonlinear multi-input multi-output systems, including unknown time-varying delays and actuator faults. First, an adaptive hybrid fuzzy state observer and a serial-parallel estimation system are constructed to estimate the unmeasured system states and incorporate them into the control design scheme, respectively, where some appropriate fuzzy logic systems are introduced to approximate the unknown nonlinear functions. According to the dynamic surface control technique, the designed adaptive fuzzy control approach can surmount the deficiency of “complexity explosion.” Then, an observer-based adaptive event-triggered control algorithm is developed by constructing the Lyapunov–Krasovskii functionals and estimating the compounded disturbances. Furthermore, it is proved that under the drive of the reference signals, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded. Finally, an example with numerical simulations is utilized to exhibit the applicability of the obtained observer-based event-triggered adaptive fuzzy control approach. [ABSTRACT FROM AUTHOR]

This article investigates the adaptive fuzzy output-feedback predefined-time control of nonlinear switched systems with an admissible edge-dependent average dwell time. Different from most of the existing results on neural or fuzzy adaptive finite/fixed-time tracking control of switched systems, where the tracking error goes to a small neighborhood of the origin within finite time, the proposed one can ensure the output tracking error to reach user-defined accuracy within predefined time, which can be arbitrarily preassigned by the designers. Technically, based on the mode-dependent fuzzy state observer, an adaptive output-feedback predefined-time controller is constructed by incorporating a new state-scale transformation function into the barrier Lyapunov function, ensuring predefined transient behavior and tracking accuracy. Besides, by extending the adaptive control problem to the admissible edge-dependent average dwell time framework, the proposed adaptive control scheme can guarantee that all the closed-loop signals are bounded under switching signals with an admissible edge-dependent average dwell time property. It is less conservative than the average dwell time and mode-dependent average dwell time. Finally, two illustrative examples validate the performance of the method presented. [ABSTRACT FROM AUTHOR]

This article considers the problem of prescribed performance control for a class of affine nonlinear systems with state constraints. Fuzzy logic systems (FLSs) are employed to cope with unknown nonlinear functions. In addition, an additive transformation and one-to-one mapping method are introduced to handle the control problem of nonlinear systems with full-state constraints. Then, based on adaptive backstepping control, we develop a new prescribed performance adaptive fuzzy control method, which not only makes the tracking error be constrained within the prescribed range but also ensures the other state variables do not violate the predefined constraints. The simulation example verify the feasibility of the control method. [ABSTRACT FROM AUTHOR]

In this article, an adaptive fuzzy predefined time and accuracy tracking control scheme is proposed for strict-feedback nonlinear systems. A sufficient condition that can be used to determine whether the tracking error converges to a predefined region within a predefined time is first derived based on the definition of predefined time stability. Under the proposed stability criterion, an adaptive fuzzy controller is developed by applying the backstepping technique, and the stability time and convergence accuracy of the tracking error can be set previously, which facilitates the engineering application with requirements for convergence time and accuracy. Especially, to reduce chattering and decrease energy consumptions on the premise of ensuring the stability of the system, an inequality is introduced to construct a continuous term which can replace the sign function. Finally, a practical example is presented to illustrate the feasibility of main results. [ABSTRACT FROM AUTHOR]

A systematic differentiator-based adaptive backstepping control methodology is proposed for a class of sandwich-like nonlinear system with unknown state-dependent deadzone nonlinearity and parametric uncertainties. The novelty of our approach is that a high-order sliding mode differentiator is utilized to estimate the nonstrict feedback coupling term resulting from the sandwiched deadzone, and all the outputs of the differentiator are integrated into the backstepping procedure based on Lyapunov functions with flat zone recursively. By this approach, all the unknown parameters are estimated online, the discontinuity of the virtual input caused by bound estimations is avoided. It is shown that the ultimate boundedness of all the closed-loop signals is achieved and the output tracking error converges to a preset set. Simulation is performed to verify the theoretical findings. [ABSTRACT FROM AUTHOR]

This article considers the problem of adaptive prescribed performance tracking control via slow switching for a general class of uncertain switched nonlinear systems (SNSs) with unmodeled dynamics (UDs) and nonstrict-feedback structure. The UDs are not in their form of the input-to-state practical stability and their state information is unmeasurable. By reassigning the function variables, the coupling effects between UDs and the prescribed performance function are eliminated through the iterative process. In virtue of the neural networks (NNs) approximation capability, a novel adaptive backstepping procedure is proposed without adding extra first-order filters. By choosing an appropriate slow switching law, all the signals of the closed-loop system are bounded, and the system output tracks the reference signal with a prescribed performance level (PPL). [ABSTRACT FROM AUTHOR]

In this note, we investigate the adaptive quantized or event-triggered control designs for strict-feedback nonlinear systems with time-varying input delay. Because the control signal is discontinuous in the quantized control and event-triggered control, many existing methods to deal with the input delay are no longer applicable. Through constructing an auxiliary tracking error and an auxiliary system, the input-quantized control is implemented for nonlinear systems with unknown control gain and unknown input delay. With the well-designed Lyapunov–Krasovskii functionals and the linear growth condition of input delay, the stability analysis is achieved. The studied method is also applicable to the event-triggered control for systems possessing unknown input delay. The stability analysis shows that all the signals are semiglobally uniformly ultimately bounded (SGUUB). The use of dynamic surface control (DSC) effectively simplifies the controller structure. The quantized and event-triggered control simulations illustrate that the proposed schemes are effective. [ABSTRACT FROM AUTHOR]

This article focuses on the problem of adaptive finite-time tracking control for nonlinear stochastic systems under asymmetric constraints based on dynamic event-triggering control. Different from the existing works, a novel adaptive tracking control algorithm is proposed with asymmetric time-varying constraints and dynamic event-triggering mechanism. First, to constrain the state variable within given time-varying boundaries, a novel predefined-time performance function is constructed. Second, a novel barrier function related to state variable is constructed, by means of which the state variable is directly constrained within the asymmetric time-varying boundaries without the virtual controller. In addition, by establishing a novel dynamic function, we propose a dynamic event-triggering mechanism, and then design controller accordingly, which can reduce computation burdens and save the network resources. By the aid of the Lyapunov stability theory, it is proved that the system tracking error converges to an adjustable bounded set in probability in a finite time and all state variables are successfully constrained into the asymmetric time-varying boundaries. Finally, the effectiveness of the proposed control algorithm is verified by a simulation example. [ABSTRACT FROM AUTHOR]

This article investigates the leaderless consensus problem for uncertain high-order nonlinear multiagent systems with event-triggered communication. Under a directed graph condition, a fully distributed adaptive control strategy is presented. The main contributions are summarized as follows: 1) globally Lipschitz condition, as required in many existing literatures, is not needed in this article; 2) for each agent, only one filtered output signal is required to be broadcast to its neighbors, which can effectively relieve network transmission burden; 3) the distributed adaptive controllers and event-triggered conditions are co-designed based on a single Lyapunov function such that continuous monitoring of neighbors’ states is not needed; and 4) by introducing an exponential convergence term to the designed triggering condition, Zeno behavior is avoided while all the agents’ outputs can reach asymptotically leaderless consensus. Moreover, to easily implement the designed triggering condition and further reduce the triggering frequency when the consensus errors converge to the neighborhood of origin, a switching type of triggering condition is presented and bounded consensus can be guaranteed. Simulation results are given to validate the presented distributed consensus control scheme. [ABSTRACT FROM AUTHOR]

This article presents a photovoltaic (PV)–battery energy storage (BES) system functioning in both grid-tied and standalone modes while performing multifunctional operations, including reactive power compensation, power balancing, and power quality enhancement. The PV–BES system ensures uninterrupted power supply to the critical loads even during seamless transitions from grid-tied mode to islanded mode, and vice versa. The incremental conductance technique controls the boost converter and confirms maximum PV power extraction. The bidirectional converter connects BES to the dc bus. The dc bus voltage $({{V}_{dc}})$ is regulated by a slime mold optimized proportional-integral controller to limit the dynamic state variation and improve stability. The voltage-source converter (VSC) is controlled by a minimum kernel-risk-sensitive mean p-power loss algorithm during the grid-synchronization mode. In contrast, the voltage control algorithm executes islanded control of VSC. The VSC control transition is achieved seamlessly without any significant disturbance at the point of common coupling. The hardware test results demonstrate that the present system remains stable during dynamic conditions and performs adequately as per the IEEE 519-2014 standard. [ABSTRACT FROM AUTHOR]

This article studies the problem of obstacle-avoidance tracking control for a class of uncertain nonlinear robot systems in multiple-dynamic-obstacles environment. The main challenge focuses on how to simultaneously ensure tracking performance and obstacle avoidance. The existing collision-avoidance tracking control schemes cannot guarantee the tracking performance inside the obstacle detection region, since the use of additive Lyapunov-barrier function (LBF) generates the dynamic mismatching. To overcome this difficulty, a novel integral-multiplicative LBF is constructed. The adaptive mechanism is designed to compensate for the mismatching uncertainties. By incorporating the barrier function into backstepping procedure, a secure adaptive tracking control scheme is proposed. Compared with the existing results, the proposed control scheme can simultaneously achieve the obstacle avoidance and tracking performance regardless of being inside the obstacle sensing region and unknown nonlinear uncertainties. [ABSTRACT FROM AUTHOR]

In this article, the formation tracking control problem with prescribed performance is studied for multiple unmanned aerial vehicles whose disturbance is bounded with unknown boundary. Different from most existing works where prescribed performance control is realized based on error transformation, a new prescribed performance function-based adaptive strategy is proposed to guarantee both transient and steady state performance, which simplifies the design procedure. Furthermore, the proposed adaptive distributed sliding mode controller is of low complex and easily implementable, without utilizing the information of the external disturbance or any approximating structures to acquire such knowledge. Additionally, collision avoidance constraints among neighbors can be satisfied through appropriately designed performance bounds. Simulation verification also confirms the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The compensation for uncertain deadzone is of both theoretical and practical importance in the adaptive fixed-time control of switched systems. So far, there exists no result reported on this problem because it is difficult to construct a fixed-time controller in the presence of the complicated switched network topology and the deadzone input. By using a newly proposed mode-dependent power integrator (MPI) method and a fuzzy deadzone model, a novel mode-dependent fuzzy fixed-time controller is presented to overcome the above problem. The proposed MPI method is more applicable in practice than the power integrator method in which each subsystem in the underlying system has its own power integrator. Stability analysis and simulation examples can be utilized to confirm the feasibility of the presented design. [ABSTRACT FROM AUTHOR]

This article aims at proposing an adaptive neural control strategy for a class of nonlinear time-delay systems with input delays and unknown control directions. Different from previous researches that investigated delays and constraints separately, the novelty of this article lies in that it simultaneously considers delays (state and input delays) and input constraints (magnitude and rate constraints) for a class of uncertain nonlinear systems. In this article, the uncertain states and input delays are handled by integrating a constructed auxiliary system that functions as an observer with neural networks (NNs), with which the adverse effects caused by the uncertain states and input delays can be approximated and compensated. By involving smooth hyperbolic tangent functions in the designed auxiliary system, the problem of magnitude and rate constraints of the control input is fully addressed. Then, the backstepping technique runs through the entire control designing process, which allows the designed adaptive neural control strategy to handle the input constraints and delays at the same time. Furthermore, Nussbaum functions are employed to resolve the problem of unknown control directions. Due to the introduction of an input-driven filter, only the output of the system is required to be measured as the control feedback, which promotes the applicability of the designed controller. Under the proposed control scheme, semiglobal, uniform, and ultimate boundedness of all signals of the closed-loop system is realized with uncertain control directions, input and state delays, and guaranteed magnitude and rate constraints of control inputs. Finally, simulation results are illustrated to verify the effectiveness of the presented control method. [ABSTRACT FROM AUTHOR]

Some specific services for Internet of Things, such as real-time map and providing local weather information, depend strongly on geographical time and location. We refer to the data for such service as spatio-temporal data (STD). When STD is used in a query response system similar to conventional Internet services, users not only need to acquire data actively as required, they must also have functions for retrieving data available STD. Therefore, we propose an STD retention system that uses vehicles as information hubs (InfoHubs) for disseminating and retaining the data in a specific area. In our system, InfoHubs diffuse, maintain, and advertise STD over places and times where the STD are strongly dependent, thereby allowing users to receive such data passively within the specific area. Additionally, because STD are associated with a particular space, the system can reduce search costs. We also propose an adaptive transmission control method that each vehicle effectively operates its wireless resources autonomously and STD are retained and distributed efficiently. Finally, we evaluated our proposed method using simulations and clarified that our proposed system is capable of achieving a coverage rate of nearly 100 percent for STD while reducing the number of data transmissions compared to existing systems. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive human pilot model that is able to mimic the crossover model in the presence of uncertainties. The proposed structure is based on the model reference adaptive control, and the adaptive laws are obtained using the Lyapunov–Krasovskii stability criteria. The model can be employed for human-in-the-loop stability and performance analyses incorporating different types of controllers and plant types. For validation purposes, an experimental setup is employed to collect data and a statistical analysis is conducted to measure the predictive power of the pilot model. [ABSTRACT FROM AUTHOR]

Executing safe brachiation maneuvers with a cable-suspended underactuated robot is a challenging problem due to the complications induced by the cable dynamics. We present and experimentally implement an online adaptive controller for a wire-borne brachiating robot swinging on a vibrating cable. An adaptive function approximation approach is proposed to estimate the unknown dynamics of the flexible cable as an external force applied to the robot. Robust control Lyapunov and barrier functions are designed and incorporated into quadratic programs to synthesize a unified adaptive control framework, which formally guarantees the stability and safety of the brachiating robot in the presence of dynamic uncertainties, actuator constraints, and obstacles in the environment. The stability analysis and derivation of the adaptation law are carried out through a Lyapunov analysis. We demonstrate and validate the proposed control framework using extensive hardware experiments with an underactuated brachiating robot operating on a flexible cable. Simulation results, hardware experiments, and comparisons with a baseline controller show that the proposed quadratic programming-based controller achieves reliable tracking performance and disturbance estimation in the presence of model uncertainties, actuator limits, and safety constraints. [ABSTRACT FROM AUTHOR]

With the rapid growth of the coronavirus disease of 2019 (COVID-19) cases, massive amounts of relevant data are being trained on machine learning models for countering communicable infectious diseases. Federated Learning (FL) is a paradigm of distributed machine learning to deal with the individual COVID-19 data, and enable the protection of data privacy. However, FL has low efficiency in Edge-Based wireless communication systems with system heterogeneity. In this paper, we propose an “Asynchronous-Adaptive FL” (AAFL) scheme. Specifically, we allow that medical devices with different performances have a heterogeneous number of local SGD iterations in each communication round, called asynchronous iteration strategy which is balanced under adaptive control. We theoretically analyze the convergence of the AAFL scheme under a given time budget and obtain a mathematical relationship between the heterogeneous number of local SGD iterations and the optimal model parameters. Based on the mathematical relationship, we design an algorithm for parameter server and work nodes to adaptively control the heterogeneous number of local SGD iterations. Subsequently, we build a prototype heterogeneous system and conduct experiments on various scenarios for analyzing the general properties of our algorithm, and then apply our algorithm to public COVID-19 databases. The experimental results and application performance demonstrate the effectiveness and efficiency of our AAFL scheme. [ABSTRACT FROM AUTHOR]

In this article, we study the robust quantized feedback control problem for nonlinear discrete-time systems that are described by Takagi–Sugeno (T–S) fuzzy model with norm-bounded uncertainties. The dynamic quantizer composed of a dynamic parameter and a static quantizer is considered to quantize the control input signal. An improved two-step approach to design controller and dynamic quantizer for T–S fuzzy system is proposed based on the LMI technique. In the first step, a robust controller is designed to guarantee that the quantized fuzzy closed-loop system with norm-bounded uncertainties is asymptotically stable with prescribed $\mathcal {H}_{\infty }$ performance. Then, the parameter-dependent (membership function) scalar variable is obtained to determine the dynamic quantizer’s parameter in the second step. Finally, the simulation result of truck–trailer system is presented to validate the effectiveness and feasibility of the proposed two-step design approach. [ABSTRACT FROM AUTHOR]

In this article, an adaptive tracking control approach is developed for a class of strict-feedback nonlinear systems with time-varying full state constraints. As a breakthrough in this system, the special function constraints (whose constraint boundary is relevant to both state variables and time) are considered, which are rarely studied by research work. And there is no doubt that this method increases the complexity of designing this scheme. Furthermore, the time-varying integral barrier Lyapunov functions combining with backstepping technique is introduced to break the limitation of traditional methods as well as achieve the full state constraints. Meanwhile, fuzzy logic systems are selected to approximate unknown nonlinear functions. It is verified that all closed-loop signals are bounded and all states are forced in the time-varying boundness. In addition, the proposed control strategy has a good performance. The effectiveness of the theoretical analysis results is proved via a simulation example. [ABSTRACT FROM AUTHOR]

This study addresses adaptive fuzzy control for an axially moving slung-load cable system (AMSLCS) of a helicopter in the presence of an actuator fault, system uncertainty, and disturbances with the aid of a fuzzy logic system (FLS). The actuator fault considered is depicted by a more general faulty plant that includes an unknown actuator gain fault and a fault deviation vector. First, to compensate for system uncertainty and the fault deviation vector, a fuzzy control technique is adopted. Then, under the introduced FLS, a novel adaptive fuzzy control law is developed by employing a rigorous Lyapunov derivation. The closed-loop system of the AMSLCS is proved to be uniformly bounded even when considering the actuator fault, system uncertainty, and disturbances. Finally, a simulation is executed to expound the performance of the developed controller. [ABSTRACT FROM AUTHOR]

This article presents a new stabilizing control scheme for a class of interconnected nonlinear systems subjected to unmodeled dynamics and immeasurable states. Fuzzy logic systems are applied to approximate the unknown functions, and a fuzzy-based state observer is constructed. The interconnection of the overall system is completely compensated via the cyclic-small-gain condition theorem, and the small-gain theorem is introduced to overcome the unmodeled dynamics in each subsystem. Furthermore, assumptions from prior literature are relaxed, and computing burden is reduced through the design of less adaptive laws. This article proves that under the designed control scheme, the closed-loop systems are controlled to be input-to-state practically stable and that all signals are guaranteed to be semiglobally uniformly ultimately bounded. Finally, this article’s simulation section illustrates the effectiveness of the proposed approach through an example derived from a practical system model. [ABSTRACT FROM AUTHOR]