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This letter investigates secure global asymptotic consensus (SGAC) with non-weighted L 2 gain of second-order multi-agent systems (MASs) under deception attacks by designing time-delayed state feedback control with switching topology. Compared with the existing methods in which the Lyapunov–Krasovskii functional (LKF) has to jump high at switching instants, the most important breakthrough is that a discretized LKF is designed such that it is monotone decreasing at switching instants, which not only can obtain low conservative results but also is convenient to study consensus with non-weighted L 2 gain without any additional inequality transformation. The Results are universal since they can be applied with no difficulty to any other switched time-delay systems. A numerical simulation illustrates the less conservative SGAC. [ABSTRACT FROM AUTHOR]

This paper investigates the optimal consensus problem for heterogeneous discrete-time(DT) linear multi-agent systems. The optimal consensus problem is formulated as finding a global Nash equilibrium solution subjected to the defined local performance index. A reinforcement learning(RL) value iteration(VI) algorithm is introduced to obtain the optimal policies in the sense of Nash equilibrium. To ensure the effectiveness of the VI algorithm, the admissibility of the iterative control policies for multi-agent systems is considered. With theoretical analysis, a new termination criterion is established to guarantee the admissibility of the iterative control policies. Furthermore, an online learning framework is designed with an actor-critic neural network(NN) to implement the VI algorithm. Finally, two simulation examples are presented respectively for leader–follower and leaderless multi-agent systems to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: In this article, an adaptive neural fault‐tolerant decentralized tracking control problem is investigated for switched stochastic large‐scale uncertain nonlinear systems, in which time‐varying delay and unknown control coefficient are under consideration. By incorporating the quadratic Lyapunov function and Lyapunov Krasovskii function into the backstepping approach, an adaptive neural fault‐tolerant decentralized control strategy is proposed, which ensures that signals in the closed‐loop system are stable in probability and the tracking error converges to a small neighborhood of the origin under arbitrary switching. At last, to verify the presented strategy is feasible, a simulation example is given. [ABSTRACT FROM AUTHOR]

This work discusses the fault detection problem for two-dimensional (2-D) positive systems (PSs) with distributed delays. First, a systematic method is proposed for the computation of exact values of $\ell _{1}$ -gain and $\ell _{-}$ index for delay-free systems, and the dimension expansion technique is used to transfer the computation of $\ell _{1}/\ell _{-}$ index for 2-D delayed PSs to one of the delay-free systems. Second, necessary and sufficient conditions are established such that the 2-D delayed PSs are asymptotically stable with a desired $\ell _{1}/\ell _{-}$ performance level (PL) $\gamma /\beta $. Third, based on the above results, some NCSs are established such that the disturbance and the fault impact on the output signal are minimized and maximized, respectively. An algorithm (iterative) is formulated for the solution of the convex optimization problem. Finally, the potency and accuracy of the developed theoretical results are exhibited by an example. [ABSTRACT FROM AUTHOR]

The contribution of this investigation lies in solving a sampled‐data output feedback stabilization problem for a class of upper‐triangular stochastic nonlinear systems in p$$ p $$‐normal structure with unavailable states and uncertain time‐varying input delay. A reduced‐order observer with the available sampled output detection is developed for the focused system to estimate its unavailable states, and under such observer, a sampled‐data output feedback stabilizer is constructed by combining the backstepping and adding a power integral techniques. Through selecting the appropriate Lyapunov–Krasovskii functionals, we can demonstrate that the adverse influences caused by the uncertain time‐varying delays in the resulting closed‐loop system can be restricted effectively by the constructed stabilizer, and further, by following the stochastic differential theories, the global stability of the considered resulting closed‐loop system can be analyzed under the suitable design parameters and allowable sampling period. Finally, two examples are given to verify the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

Collision avoidance is critical in unmanned surface vehicles (USVs) and is especially challenging in scenarios involving ships of different intelligence levels. To address this problem, inspired by risk analysis and collision avoidance habits of seafarers, this paper proposes a human-like collision avoidance method based on deep reinforcement learning (DRL) and velocity obstacle (VO). It first introduces a navigation impact factor (NIF) calculation module based on fuzzy theory to simulate human beings' attention mechanisms when facing multiple ships. To be compatible with human beings' regulations, the convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is first to be integrated into the calculation of the NIF calculation module, and the VO algorithm is incorporated into the reward function, which can reduce collision risks in paths and improve safety requirements. In addition, a series of reward functions are carefully designed to balance safety, smoothness, and driving operation. To validate the performance, experiments are conducted on our virtual simulation platform. The results show that our algorithm can accurately assess the impact of target ships (TS) on own ship (OS) in complex environments and can obey the COLREGs. Collision avoidance can be achieved effectively and the path is smoother. [ABSTRACT FROM AUTHOR]

In this paper, a fixed-time fault-tolerant control strategy is proposed for the trajectory tracking of aquatic–aerial unmanned amphibious vehicles (AquaUAVs) under multiple actuator faults. A switched error system is introduced to describe the AquaUAV trajectory tracking motion under actuator faults in different modes. A switched fixed-time extended state observer (FESO) is developed to observe the tracking errors of the vehicle and the actuator faults, and a fault-tolerant control strategy. The proposed composite controller not only ensures the stability of the system, but also compensates the faults of the actuator. Simulation and experiment results verify that the proposed control strategy can realize the trajectory tracking control of amphibious vehicle under the condition of multiple actuator faults. [ABSTRACT FROM AUTHOR]

This paper proposes a learning-based hyper-heuristic algorithm for the coverage path planning problem of multiple unmanned surface vehicles (USV). The makespan and coverage of the USVs are considered simultaneously. The proposed method does not need to specify the location of the mapping task points in advance; instead, the task allocation and path planning can be realized only based on the parameter information of the USV and the watershed. Considering the uncertainty in the actual mapping process, the triangular fuzzy numbers are used to represent the mapping time and mapping radius of USVs. In order to solve the multi-USV scheduling problem efficiently, this paper proposes an NSGA-II based on dueling double deep Q-network called NSGA-II-DQN. NSGA-II-DQN integrates ten effective global search operators and five local search operators, which can achieve a good balance between exploration and exploitation. Experiment results in various scenarios demonstrate that NSGA-II-DQN can efficiently search for travel paths covering the entire water area for multiple USVs in a short planning time. • Triangular fuzzy numbers are used to represent the mapping time and mapping radius. • A multi-layer encoding is designed to optimize makespan and coverage of multiple USVs. • A local search operator based on shortest distance prioritization is designed. • Ten global operators and five local operators are adopted as a pool of candidate operators. • The dueling double deep Q-network is adopted to learn and select the optimal operator. [ABSTRACT FROM AUTHOR]

• In some existing ETC results, such as [25, 30, 31, 37], the relative or fixed threshold strategies have been investigated. In this paper, a novel dynamic ETC strategy is proposed, in which the threshold parameter is dynamically adjusted with the change of the virtual control signal. Therefore, the proposed dynamic triggering strategy is more flexible than those in [25, 30, 31, 37]. • For p -normal nonlinear time-delay systems like [22, 23], the growth constraint condition imposed on the delayed nonlinear function is required. To relax this condition, the adaptive NN and LKF techniques are used in this paper. Specifically, a modified LKF related to the system powers is constructed to handle time delay; radial basis function (RBF) NN and hyperbolic tangent function are introduced to deal with the time-delay residual terms. • An adaptive fault compensation control scheme is presented for a class of p -normal nonlinear systems. Compared with existing literature [16–18], the total number of actuator failures is allowed to be infinite, which expands the applications of the systems. Hence, the proposed adaptive NN compensation scheme is applicable to a wider class of nonlinear systems. This paper investigates the adaptive tracking control problem for a class of p -normal nonlinear time-delay systems with actuator failures via a dynamic event-triggered strategy. To deal with the time-varying delay, a modified Lyapunov-Krasovskii functional (LKF) with the system powers is constructed. By using radial basis function neural networks and hyperbolic tangent function, the residual terms caused by the derivative of LKF can be handled. Then, to achieve better communication efficiency, a dynamic event-triggered strategy is proposed, in which the threshold parameter is dynamically adjusted with the change of the virtual control signal. With the help of the proposed adaptive controller, all states of the closed-loop system are bounded and a favorable tracking performance is ensured. Finally, two examples are given to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the adaptive fuzzy output feedback fault‐tolerant tracking control problem for a class of switched uncertain nonlinear systems with sensor faults and actuator faults. Both the faults considered are unknown, the existing schemes fail to solve the control problem. In this paper, an adaptive estimation coefficient is introduced to mitigate the effects of sensor faults, then a state observer is constructed to estimate unmeasured states and mitigate the effects of actuator faults. Fuzzy logic systems are employed to approximate the unknown nonlinear functions. By the backstepping technique and the Lyapunov function method, an adaptive fuzzy output feedback fault‐tolerant controller is designed. It is proven that the proposed control scheme with average dwell time constraints can guarantee that all states of the closed‐loop system are semi‐global uniformly ultimately bounded and the tracking error can converge to a small neighborhood of zero. Finally, a simulation example is given to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article investigates the sampled-data stabilization problem of a class of switched nonlinear systems. All subsystems of the considered system are allowed to be unstabilizable. To relax the restrictions on unknown nonlinear functions in some existing results, we use the nonlinear approximation ability of radial basis function neural networks. Novel mode-dependent adaptive laws and sampled-data control laws are constructed by only using the system states’ information at sampling instants. A novel sampled-data switching condition is derived, which can avoid Zeno behavior effectively. To guarantee that all states of the closed-loop system (CLS) are bounded, a new allowable sampling period is deduced. Finally, we demonstrate the proposed method’s effectiveness through two examples. [ABSTRACT FROM AUTHOR]

In the presence of global positioning system signal rejection, the low-cost inertial navigation system exhibits a significant cumulative error, leading to poor positioning accuracy of slave unmanned surface vehicles (SUSVs) in the cooperative navigation (CN) system for leader-slave. To improve the positioning accuracy of SUSVs, the extended Kalman filter (EKF) is augmented with distance information from external distance sensors. However, in the CN system of single leader unmanned surface vehicle, there may be a weaker observability, resulting in suboptimal estimated effects of the EKF. For the first time, the CN algorithm with observability and trilateral positioning method (TPM) is proposed to compensate the estimated position errors for EKF. An event trigger constructed from observability is used to assign the base stations and tags of the TPM. Then, the estimated positions of the tags are obtained by EKF when observability becomes weaker and the estimated positions are corrected by the measurements from TPM. Finally, more accurate estimated positions are obtained by weighted mean and the weights of compensation are automatically adjusted with observability. The results of the validation experiments and simulations show that the estimated positional accuracy of the proposed CN algorithm has improved by 40–50% compared to EKF. • Trilateral positioning method and observability are first offered to compensate the estimated position errors for EKF when observability becomes weaker in the cooperative navigation system of unmanned surface vehicles. • In order to improve the operational efficiency of the proposed collaborative navigation algorithm, event trigger is constructed based on observability to selectively compensate the estimation errors from the EKF. Finally, estimated positions with higher accuracy are achieved by obtaining automatically varying compensation weights through observability. • The experimental results and simulations show that the positional accuracy of the algorithm proposed in this paper is improved by 40–50% compared to EKF. [ABSTRACT FROM AUTHOR]

Neural network method is an effective tool for approximating the unknown function in controller design for nonlinear systems. To guarantee the validity of the approximation, state variables in approximated unknown functions need to stay in a compact set. However, in most existing results, the existence of the compact set has not been correctly proven; therefore, the proof is not actually complete in these existing works. In this paper, we analyze the existence of compact sets for two typical nonlinear systems with novel neural network-based controllers and show the strict proof for the semi-global uniform ultimate boundedness of the closed-loop system. [ABSTRACT FROM AUTHOR]

The global output feedback stabilization problem is investigated in this paper via sampled-data control for switched nonlinear systems in the $p$ -normal form. First, a reduced-order state observer is designed. Then, an output feedback sampled-data controller is constructed with the relaxation of some restrictions of switched nonlinear systems. The proposed controller can ensure that all states of the corresponding closed-loop system can converge to the origin. Simulation results are given to show the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the event-triggered leader-following consensus problem for a class of multiple mechanical systems with switched dynamics. Based on the graph theory, Lyapunov stability theory, backstepping technique and event-triggered control theory, an event-triggered protocol is proposed for the considered multiple mechanical systems. It is proved that the practical leader-following consensus can be achieved by the proposed protocol. Meanwhile, to exclude the Zeno behaviour, a positive lower bound of inter-event intervals is given. Finally, we provide a numerical simulation to illustrate the effectiveness of the given protocol. [ABSTRACT FROM AUTHOR]

Summary: This article investigates the problem of global adaptive output feedback control for a class of p‐normal nonlinear systems with time‐varying sensor failure and unknown growth rate via an event‐triggered strategy. Due to the existence of time‐varying sensor failure, only the system output with sensor failure is available for feedback control design. To overcome this obstacle, an adaptive reduced‐dimensional observer using only failure output is constructed. Subsequently, a novel adaptive event‐triggered controller is designed by virtual of a dynamic gain technique, under which all the states of the considered closed‐loop systems are bounded as well as the proposed event‐triggered mechanism is Zeno‐free. Finally, two examples are provided to demonstrate the validity of the presented control method. [ABSTRACT FROM AUTHOR]

In this study, the consensus tracking control problem is considered for a class of second‐order non‐linear multi‐agent systems with unmodelled dynamics. Radial basis function neural networks are introduced to approximate the unknown non‐linearities and a dynamic signal is utilised to deal with the unmodelled dynamics. Meanwhile, appropriate Lyapunov functions are constructed and a new distributed adaptive control protocol is designed to guarantee that all the tracking error signals in the considered multi‐agent systems are ultimately bounded, under which the distributed tracking consensus is reached for all undirected connected communication graphs. Finally, two demonstrative examples are given to illustrate the validity of the designed protocols. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the finite‐time stabilization problem for a class of nonlinear systems with time‐varying delay. Under suitable assumptions, a new state feedback control law is designed by using the adding a power integrator technique. By constructing an appropriate Lyapunov‐Krasovskii functional, it is shown that the corresponding closed‐loop system is finite‐time stable. Two simulation examples are given to verify the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

• A navigation accuracy compensation algorithm for INS is proposed for low-cost USVs. • An event-triggering mechanism is introduced, and the thrust models of the USVs are adopted to design the event trigger. • According to different events, various weights can be assigned to various integrators and PF. • Field experiments are conducted to verify the navigation performance of our algorithm. Integrated navigation is often used for navigation of unmanned surface vehicles (USVs). For a class of cost-sensitive USVs, it is desirable to compose integrated navigation by low-cost inertial navigation systems (INS) and global positioning systems (GPS). In the cases of GPS signal failure or interference, only INS onboard can be used for navigation, which often leads to the navigation accuracy deteriorating and may fail to meet application requirements. Therefore, a navigation accuracy compensation algorithm for low-cost USVs is proposed. An event-triggering mechanism is introduced, and the thrust models of the USVs are adopted to design the event trigger. According to different events, various weights can be assigned to the integrator based on INS, and measurement-based linear velocity V INS and heading angle ψ I N S can be obtained. Moreover, based on dynamic models, the model-based linear velocity V m and heading angle ψ m also can be calculated. Meanwhile, the event trigger is combined with the Particle Filter (PF) algorithm to accurately estimate the linear velocity V ^ P F and heading angle ψ ^ PF for navigation. Finally, field experiments are conducted and the differential GPS (DGPS) is used as the benchmark for comparison. The results show that our algorithm can improve navigation performance for the low-cost USVs. [ABSTRACT FROM AUTHOR]

• A novel SSR model is proposed for a distribution system utilizing the MEGs. • A two-stage operation model is formulated to pre-position and re-route the MEGs. • SP and RO are used and compared when addressing discrete and continuous uncertainty. • The PHA and convex hull relaxation are used to reduce the computational complexity. A fast and reliable sequential service restoration (SSR) methodology is necessary to enhance the resilience of the distribution system (DS) after an outage. However, traditional SSR strategies ignore the utilization of mobile emergency generators (MEGs) to reduce the system outage duration. This paper exploits the enhanced resilience of DSs by dispatching MEGs as backup sources to form microgrids (MGs) and sequentially restore out-of-service loads. A two-stage dispatching model is proposed for the SSR of MGs that takes into consideration the preventive control stage (PCS) and the emergency control stage (ECS). Moreover, SSR decisions are optimized depending on stage-based uncertainties. Specifically, in PCS, MEGs are pre-positioned prior to event strikes. Considering contingency uncertainty, a scenario-based stochastic model is proposed to minimize the expected outage duration of loads, and a progressive hedging algorithm is applied to improve computational efficiency. With the realization of contingency in ECS, MEGs are rerouted from pre-positions to target locations, and provisional MGs are sequentially formed to restore critical loads. Considering wind power uncertainty, a robust model is formulated, and the convex hull relaxation method is customized to reduce the computational complexity. The resulting SSR scheme can be adaptively and securely adjusted in response to random contingencies and wind power fluctuation. The effectiveness of the proposed method is validated by numerical simulations. [ABSTRACT FROM AUTHOR]

• Within our knowledge, this is the first time to handle the adaptive Event-Triggered output feedback control problem for nonlinear switched interconnected systems with unknown control coefficients and unknown backlash hysteresis. • To deal with the unknown hysteresis and the unknown control coefficients, firstly through introducing a transformation for the system states, the research system is transformed into a new one, and an observer is designed to estimate the unmeasurable system state signals. Then the problem of unknown control coefficients is addressed through employing the Nussbaum technology, so the limitation that the control gains must be known is removed. • For the nonlinear system subject to unknown control gains, compared with the time triggered control strategy, this paper constructs the corresponding event-triggered controller, which not only ensures that the whole signals are semi-globally uniformly ultimately bounded, but also reduces the transmission burden between the controller and the actuator, and finally eliminates the Zeno phenomenon. This paper proposes an event-triggered adaptive control scheme for switched nonlinear interconnected systems with unknown control coefficients and unknown backlash hysteresis. Firstly, unknown interconnected terms and unknown nonlinearities of the system are approximated through fuzzy logic systems. By introducing the Nussbaum function and choosing an appropriate Lyapunov function, an event-triggered output feedback controller is developed. The proposed control scheme can guarantee that the whole states are semi-globally uniformly ultimately bounded, meanwhile, the elimination of Zeno behavior is ensured. Eventually, the effectiveness of the method is verified by simulation results. [ABSTRACT FROM AUTHOR]

[Display omitted] • Bi 4 O 5 I 2 were regulated via controlled hydrolysis of urea in polyhydric alcohol. • Polyhydric alcohol with proper viscosity is the key role of controlled hydrolysis. • Bi 4 O 5 I 2 with tuned band structure showed superior photodegradation activity. • The reaction rate constant of Bi 4 O 5 I 2 is almost 3 times than that of BiOI. In this study, the phase transition of Bi 4 O 5 I 2 was regulated via the controlled hydrolysis of urea in polyhydric alcohol. The systematic conditions of the controlled hydrolysis for Bi 4 O 5 I 2 phase transition were examined. Polyhydric alcohol solution with proper viscosity (such as EG, and mixture of EG and G), temperature over 180 °C, over 3 h, and tetrabutylammonium iodide (TBAI) over 4 mmol could contribute to the controlled hydrolysis of urea (1.0 g) for Bi 4 O 5 I 2. The band structure of resulted Bi 4 O 5 I 2 was altered, resulting in superior photodegradation performances of BPA removal due to the efficient charge separation and transfer. [ABSTRACT FROM AUTHOR]

• Within our knowledge, this is the first time to handle the adaptive neural finite-time tracking control problem for uncertain switched nonlinear systems with unknown time-varying delay and backlash-like hysteresis. • The singularity problem of many existing adaptive finite-time control methods for singleinput and single-output n th order nonlinear systems is solved in this article. Additionally, the problem of complexity explosion is solved by dynamic surface technology. • The restriction on the time delay term (i.e., the time derivative of the time-varying delay needs to be less than 1), has been removed, which is more reasonable and of great use in practical. Furthermore, radial basis function neural networks are introduced to remove the assumption of the growth condition for unknown functions. • Constructing individual Lyapunov function for the individual subsystem, an adaptive finitetime controller design technique for switched nonlinear systems with time delay is proposed, which ensures that the system is stable within a finite time even if backlash-like hysteresis exists. This work investigates the adaptive finite-time tracking control problem for switched nonlinear systems, in which backlash-like hysteresis and time-varying delay are taken into account. The nonlinear estimation ability of radial basis function neural networks is employed to relax the restriction on unknown nonlinear functions. The dynamic surface technology and the finite-time control approach avoid the "curse of dimensionality" and "singularity" problems existing in the backstepping design procedure, separately. By Lyapunov–Razumikhin function scheme, the proposed finite-time signal guarantees superior tracking performance under the average dwell time switching. Finally, to testify the practicability of the presented strategy, two simulation examples are shown. [ABSTRACT FROM AUTHOR]

• Propose a novel SSR approach to restore the outage system sequentially. • Formulate a two-stage robust optimization model to address uncertainties during the SSR process. • Apply the EC&CG algorithm for effectively solving the proposed robust SSR problem. When a distribution system encounters a major outage due to extreme events, an efficient sequential service restoration (SSR) strategy is required to restore the outage system. However, uncertainties associated with renewable energy generation and load demands pose significant and complex challenges for the optimization of SSR strategies. This paper proposes a robust multiple power source co-optimization approach for the SSR of an outage distribution system. First, network configuration and sequential switching actions are jointly optimized to sequentially form microgrids (MGs). Second, multiple types of power sources, such as distributed generators and energy storage units, are re-energized and coordinated to pick up critical loads in parallel restored MGs. In addition, uncertainties associated with renewable energy generation and load demands are considered during the restoration process, thereby requiring a robust SSR scheme to increase the reliability of MG operations, The mathematical problem is modelled as a mixed-integer tri-level programming problem with inner-level binary variables. Thus, the extended column-and-constraint generation (EC&CG) algorithm is employed to solve the proposed robust model. The numerical results demonstrate the effective performance of the proposed approach and its robustness and advantages compared to existing models that do not consider uncertainties. [ABSTRACT FROM AUTHOR]

As a kind of reactive power compensation equipment, a synchronous condenser has advantages of strong overload capacity and effective suppression of commutation failures. It has been widely used in AC/DC power transmission systems. In this paper, the excitation control modes of the synchronous condenser during grid-integration at the speed-falling stage are studied with three contributions: (1) The necessity of using the exciter at the speed-falling stage and the associated excitation control characteristics are analysed. The dynamic behaviours with different mono-excitation-control modes are compared with identification of the merits and drawbacks. (2) In order to smooth the voltage rising curve during the stator voltage establishment at the speed-falling stage so as to enhance the reliability of starting the synchronous condenser, a sequential hybrid control scheme is proposed with PID controller for excitation at the initial and final stage, and for constant excitation at the middle stage. (3) In order to mitigate the oscillations during grid-integration, a control scheme of pre-insertion of an energy-absorption impedance (EAI) is proposed. The effectiveness and superiority of the proposed sequential hybrid excitation control mode with pre-insertion of an EAI for smoothing the voltage rising curve and mitigation of the oscillations are justified by the simulation results in PSCAD/EMTDC. • The dynamic behaviours with different mono-excitation-control modes are compared with identification of the merits and drawbacks; • A hybrid control scheme is proposed with PID controller for excitation at the initial and final stage and for constant excitation at the middle stage. [ABSTRACT FROM AUTHOR]

In this paper, different reliability simulation approaches of an improved DC current flow controller (CFC) integrated voltage-sourced converter (VSC) based DC grids are investigated with the following contributions: (1) In order to achieve bidirectional power flow control and fault current blocking capability, an improved DC CFC topology with a set of reversed switches is analysed to enhance the controllable path and control margins; (2) Two different reliability simulation approaches of the improved DC CFC integrated meshed multi-terminal DC (M2TDC) grid are analysed. In Approach 1, the VSCs and their AC sides are modelled without simplification. In Approach 2, the VSCs and their AC sides are represented by ideal DC voltage/current sources to simplify the system modelling; (3) Synthesizing the merits of both approaches while avoiding the deficiencies, a hybrid reliability simulation approach is proposed with high efficiency of simulation while maintaining the accuracy; (4) In order to further enhance the simulation efficiency, a different reliability simulation approach of the ideal DC current source, namely improved ideal DC current source, is proposed. Simulation systems are established in PSCAD/EMTDC for verification. [ABSTRACT FROM AUTHOR]

• A RMPC-based MG scheduling method is proposed. • The MGs formation model and scheduling model is optimized simultaneously. • The nonlinear model is linearized and transformed into a MILP programming problem. • The efficiency of proposed method is validated using the IEEE 37-node test system. One of the requirements for a resilient distribution system with distributed generators (DGs) is to restore as many critical loads as possible during a power supply outage of the main power grid. During the outage period, the distribution network is divided into microgrids (MGs) and continuously provides power to loads. However, the uncertainty of renewable DGs' output, which cannot be predicted perfectly, increases the difficulty of optimal operation of MGs. In this paper, an MG scheduling method is proposed based on robust model predictive control (RMPC) for the resilience enhancement of distribution networks. The DGs, energy storage devices, and switches are optimally coordinated to maximize load restoration. First, an RMPC-based two-layer MG scheduling model considering the worst-case scenario is established to schedule the structure of the MGs and the output of the DGs simultaneously. Furthermore, the original model is transformed into a single-layer programming problem using strong duality theory. The solution of the RMPC-based model can guarantee the reliability and feasibility of MGs within the predefined uncertainty sets of the DGs. The effectiveness of the proposed method is validated with the modified IEEE 37-node distribution test system. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of decentralized control with positivity constraint for impulsive interconnected switched positive systems (IISPSs) with mixed time-varying delays coexisting in the state term and the interconnection term under mode-dependent minimum dwell time (MDMDT) switching. Firstly, a positivity criterion of IISPSs is given from the aspect of sufficiency and necessity. Next, through adopting a new discretized linear copositive Lyapunov-Krasovskii functional approach, a sufficient stability criterion of IISPSs under MDMDT constraint is presented. Then, utilizing the method of matrix decomposition, for the mode-dependent controller gains, a valid decentralized controller design scheme in the mode-dependent piece-wise form with formulated linear programming (LP) condition is given for IISPSs under MDMDT constraint. Furthermore, the proposed decentralized control design can degenerate into the one for the case that mixed delay does not lie in IISPSs. At last, three examples are presented to verify the validity of decentralized control design., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 International Society of Automation. Published by Elsevier Ltd. All rights reserved.)

This article addresses the data-based optimal switching and control codesign for discrete-time nonlinear switched systems via a two-stage approximate dynamic programming (ADP) algorithm. Through offline policy improvement and policy evaluation, the proposed algorithm iteratively determines the optimal hybrid control policy using system input/output data. Moreover, a strict proof of the convergence is given for the two-stage ADP algorithm. Admissibility, an essential property of the hybrid control policy must be ensured for practical application. To this end, the properties of the hybrid control policies are analyzed and an admissibility criterion is obtained. To realize the proposed Q -learning algorithm, an actor-critic neural network (NN) structure that employs multiple NNs to approximate the Q -functions and control policies for different subsystems is adopted. By applying the proposed admissibility criterion, the obtained hybrid control policy is guaranteed to be admissible. Finally, two numerical simulations verify the effectiveness of the proposed algorithm.

The paper studies the finite-time stability (FTS) of equilibrium points (EPs) in fractional-order nonlinear systems (FONSs). Classifying the EPs into initial EP and finite-time EP innovatively, equations of the EPs in FONSs are investigated comprehensively, and a unified definition depicting the EPs in the FONSs is proposed. Furthermore, sufficient conditions of the FTS of EPs in FONSs are given. The proposed results are verified with an illustrative example. Thus, different from existing works that declare non-existence of finite-time stable equilibria in FONSs, the existence of finite-time stable EPs in FONSs is confirmed in this paper., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

In this article, an adaptive finite-time tracking control scheme is developed for a category of uncertain nonlinear systems with asymmetric time-varying full-state constraints and actuator failures. First, in the control design process, the original constrained nonlinear system is transformed into an equivalent "unconstrained" one by using the uniform barrier function (UBF). Then, by introducing a new coordinate transformation and incorporating it into each recursive step of adaptive finite-time control design based on the backstepping technique, more general state constraints can be handled. In addition, since the nonlinear function in the system is unknown, neural network is employed to approximate it. Considering singularity, the virtual control signal is designed as a piecewise function to guarantee the performance of the system within a finite time. The developed finite-time control method ensures that all signals in the closed-loop system are bounded, and the output tracking error converges to a small neighborhood of the origin. At last, the simulation example illustrates the feasibility and superiority of the presented control method.

The problem of neural adaptive distributed formation control is investigated for quadrotor multiple unmanned aerial vehicles (UAVs) subject to unmodeled dynamics and disturbance. The quadrotor UAV system is divided into two parts: the position subsystem and the attitude subsystem. A virtual position controller based on backstepping is designed to address the coupling constraints and generate two command signals for the attitude subsystem. By establishing the communication mechanism between the UAVs and the virtual leader, a distributed formation scheme, which uses the UAVs' local information and makes each UAV update its position and velocity according to the information of neighboring UAVs, is proposed to form the required formation flight. By designing a neural adaptive sliding mode controller (SMC) for multi-UAVs, the compound uncertainties (including nonlinearities, unmodeled dynamics, and external disturbances) are compensated for to guarantee good tracking performance. The Lyapunov theory is used to prove that the tracking error of each UAV converges to an adjustable neighborhood of zero. Finally, the simulation results demonstrate the effectiveness of the proposed scheme.

It is challenging to synchronize switched time-delay systems when some modes are uncontrolled and the dwell time (DT) of controlled mode is very small. Therefore, in this article, global exponential synchronization almost surely (GES a.s.) in a cluster of switched neural networks (NNs) with hybrid delays (time-varying delay and infinite-time distributed delay) is investigated, where transition probability (TP)-based random mode-dependent average DT (MDADT) switching is considered. A novel mode-dependent pinning event-triggered controller with nonidentical deception attacks is proposed to save the communication resource and derive less conservative results. The two necessary and restrictive conditions in existing papers that the value of the Lyapunov-Krasovskii functional (LKF) before switching instants should be smaller than that after corresponding instant and the DT of each switching mode is restricted by the sampling intervals of the event trigger are moved. Sufficient conditions in terms of linear matrix inequalities (LMIs) are given to guarantee the GES a.s., even though both synchronizing and nonsynchronizing modes coexist and maybe the minimum DT of synchronizing modes is very small. Numerical examples, including image encryption, are provided to demonstrate the merits of the new technique.

In this study, the sampled-data consensus problem is investigated for a class of heterogeneous multiagent systems (MASs) in which each agent is described by a second-order switched nonlinear system. Owing to the heterogeneity and the occurrence of dynamic switching in the MASs, the sampled-data consensus protocol design problem is challenging. In this study, two periodic sampled-data consensus protocols and an event-triggered consensus protocol are developed. Here, we first propose a new periodic sampled-data consensus protocol that involves the local objective trajectory interaction among agents. The protocol is then improved by applying the finite-time control and sliding-mode control techniques. Notably, the improved protocol can be implemented without the transmission of constructed auxiliary dynamical variables, which is a major feature of the present study. It is shown that complete consensus of the underlying MASs can be achieved by the two proposed protocols with only sampled-data measurements. To further reduce the communication load, we introduce an event-triggered mechanism to obtain a new protocol. Finally, the effectiveness of the given schemes is demonstrated by considering a numerical example.

This study is concerned with the problem of the mixed l 1 /l - fault detection (FD) observer for delayed two-dimensional (2D) positive systems (PSs). The necessary and sufficient conditions (NSCs) are derived under which the residual error system is asymptotically stable (AS) and has the prescribed performance level. The conservatism is greatly reduced compared to the existing results. A new performance analysis method and the mixed l 1 /l - FD observer design are presented. Firstly, the calculation of l 1 /l - index for delayed 2D PSs is proposed by establishing the equivalence between the delayed system and the higher dimensional delay-free system in sense of l 1 and l - indexes. Secondly, NSCs are developed such that the delayed 2D PS is AS with a desired mixed l 1 /l - performance. Thirdly, the sufficient conditions of the observer design are further developed based on linear programming. An iterative algorithm is formulated to minimize and maximize the impact of system disturbances and faults on the output signal, respectively. Finally, we present two examples to verify the superiority of the obtained results., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

In this study, an adaptive fuzzy decentralized dynamic surface control (DSC) problem is investigated for switched large-scale nonlinear systems with deferred asymmetric and time-varying full-state constraints. Due to the existence of additional general nonlinearities, complicated output interconnections, and full-state constraints, it is difficult to address the above control problem using existing methods. Fuzzy-logic systems are, therefore, utilized to approximate the unknown nonlinear functions, and the DSC technique is adopted to overcome the "curse of dimensionality" problem. A novel fuzzy adaptive decentralized controller design is presented using the proposed convex combination technique. Furthermore, it is proven that under the proposed controller and state-dependent switching law, all states of the closed-loop system are bounded and deferred asymmetric, and the time-varying full-state constraints are strictly obeyed. The simulation results are presented to demonstrate the effectiveness of the proposed method.

In this article, a decentralized adaptive neural network (NN) event-triggered sensor failure compensation control issue is investigated for nonlinear switched large-scale systems. Due to the presence of unknown control coefficients, output interactions, sensor faults, and arbitrary switchings, previous works cannot solve the investigated issue. First, to estimate unmeasured states, a novel observer is designed. Then, NNs are utilized for identifying both interconnected terms and unstructured uncertainties. A novel fault compensation mechanism is proposed to circumvent the obstacle caused by sensor faults, and a Nussbaum-type function is introduced to tackle unknown control coefficients. A novel switching threshold strategy is developed to balance communication constraints and system performance. Based on the common Lyapunov function (CLF) method, an event-triggered decentralized control scheme is proposed to guarantee that all closed-loop signals are bounded even if sensors undergo failures. It is shown that the Zeno behavior is avoided. Finally, simulation results are presented to show the validity of the proposed strategy.

This study considers an adaptive neural-network (NN) periodic event-triggered control (PETC) problem for switched nonlinear systems (SNSs). In the system, only the system output is available at sampling instants. A novel adaptive law and a state observer are constructed by using only the sampled system output. A new output-feedback adaptive NN PETC strategy is developed to reduce the usage of communication resources; it includes a controller that only uses event-sampling information and an event-triggering mechanism (ETM) that is only intermittently monitored at sampling instants. The proposed adaptive NN PETC strategy does not need restrictions on nonlinear functions reported in some previous studies. It is proven that all states of the closed-loop system (CLS) are semiglobally uniformly ultimately bounded (SGUUB) under arbitrary switchings by choosing an allowable sampling period. Finally, the proposed scheme is applied to a continuous stirred tank reactor (CSTR) system and a numerical example to verify its effectiveness.

This article is concerned with the problem of adaptive neural controller design for multi-input/multi-output nonlinear systems with input-saturations and disturbances. In the proposed design mechanism, we will take advantage of hyperbolic tangent functions to smooth the sharp corners of the input saturations and use Young's inequality to handle the nonlinear terms derived from the deducing process, and meanwhile apply the intelligent algorithm to estimate the unknown nonlinearity via neural networks. Furthermore, the backstepping technique is used to complete the design of the controller and Lyapunov stability theory is employed to show that the whole closed-loop system is semi-global uniformly ultimately bounded and the tracking error is bounded subject to the small neighborhood of the origin. Finally, as a practical application of the researched design scheme, adaptive neural controller for a continuous stirred tank reactor is constructed., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 ISA. Published by Elsevier Ltd. All rights reserved.)