With the continuous integration of more and more distributed photovoltaic (PV), distribution networks are facing serious voltage issues. While centralized battery energy storage (BES) can improve bus voltage profile through power compensation, it often suffers from economic operation problems and low flexibility. Hence, this paper proposes a flexible voltage regulation (VR) method that integrates planning and control of distributed BES. A collaborative planning scheme for BES is first designed to minimize its total capacity configuration. Then, a capacity-based proportional compensation mechanism is used to ensure uniform control state of PV or BES units. Afterwards, a VR algorithm is designed for the crucial bus with the most severe violations, which derives power control references for PV and BES based on sensitivity analysis. By integrating planning and control of BES, the VR constraint is considered in planning to achieve sufficient regulation effects, while the proportional compensation control prevents overuse issues. Finally, the effectiveness is verified by a case study in a real grid scene.

Although information interaction is essential for achieving group goals of distributed systems, the information interaction consumes lots of communication resources, and also leads to the privacy disclosure of individual information. Therefore, how to design an efficient and secure distributed information interaction algorithm naturally becomes a crucial issue. In this article, we propose a differential privacy consensus algorithm based on an event-triggered communication mechanism for current sharing and voltage regulation in DC Microgrids. The main idea is to, at each event-triggered instant, first release some sporadic current data, and then mask the data with additive Laplace noises with an invariant variance. By virtue of a stochastic approximation technique, time-varying control gains are designed to compensate for the adverse effect of the stationary noises. As a result, not only the execution efficiency of the network is improved, but the privacy of initial currents can be well protected. The advantage of the proposed algorithm lies in its ability to prevent the gradual leakage of privacy caused by noise attenuation. That is, it provides stronger privacy protection than the commonly adopted approach using exponentially decaying noise. Furthermore, we carry out rigorous convergence analysis for current sharing and average bus voltage regulation, and also evaluate the level of differential privacy achieved. Finally, the effectiveness of the proposed algorithms is validated by simulation results from a detailed switch-level microgrid model.

To mitigate bus voltage violations in active distribution networks, this article proposes a local coordinated voltage regulation (VR) algorithm involving distributed photovoltaic (PV) and battery energy storage system (BESS). First, a sequential coordination mechanism of reactive power compensation (RPC) of PV followed by active power compensation (APC) of BESS is designed. Then, the available maximum RPC capacity and the required minimum APC capacity are computed in turn based on the exact VR requirement of a selected crucial bus. Afterward, a capacity-based proportional RPC-APC control is adopted to keep each PV or BESS in a consistent controllable state, thereby preventing repeated parameter updates and extensive iterations. The algorithm only requires control of local PV and BESS on the buses experiencing voltage issues, which leads to a fairer sharing of the VR burden. Finally, the effectiveness is validated through the case study in a real grid scenario.

In this paper, the distributed adaptive consensus control problem for nonlinear network systems with unmatched unknown parameters is investigated. The communication channels among subsystems are directed and suffer from malicious attacks. Besides, only part of subsystems can access the reference states. To depict different kinds of attacks, a unified attack model is established from the viewpoint of attacked subsystems. Then, an event-based communication switching mechanism is proposed for subsystems to establish new communication channels, such that attack effects on these channels can be mitigated actively. Noting that malicious attacks and switching communication make transmitted states discrete, it is also a tough issue to design controllers by adopt the backstepping technique. To handle this problem and mitigate residual attack effects, a continuous virtual controller is designed by introducing normalization terms in the adaptive laws. Then, a distributed adaptive control scheme is proposed to guarantee that consensus errors are globally uniformly bounded under arbitrary switching dwell-time and malicious communication attacks. Experimental results are provided to validate the effectiveness of the proposed control scheme. Note to Practitioners—This paper is motivated by the secure consensus control problem for nonlinear network systems under malicious communication attacks. In the practice, the Frequency-Hopping Spread Spectrum (FHSS) technique has been used to defend malicious attacks. However, the switched communication induced by the FHSS technique may destroy the consensus performance. Moreover, how to economize the switching resource and improve the active defense ability of control schemes against malicious attack is still a tough issue. Motivated by these points, an active distributed adaptive consensus control scheme is proposed to defend malicious attacks, which contains an event-based switching mechanism and a novel secure controller. The event-triggered switching mechanism is designed with attack-sensitive functions to determine the switching time instants, such that the switching resources can be utilized more efficiently. By introducing a unified attack model, the attack-defense ability of the secure controller against different kinds of attacks can be improved and analyzed mathematically. Moreover, this controller can also be applied to the switched topologies with arbitrary switching dwell-time. In conclusion, this proposed distributed adaptive control scheme can greatly reduce the effects of malicious attacks and switched piecewise states on the consensus performance and controller design, such that engineers can pay more focuses on selecting suitable control parameters to adjust consensus performance. However, to enable above advantages, some bounded biases are involved in the consensus performance. In the future work, more accuracy attack-sensitive functions, optimization algorithms for parameters, switched secure controllers and unknown nonlinear functions satisfying Lipschitz condition can be considered to decrease above bounded biases and improve the consensus performance under malicious attacks.

The absolute positioning accuracy of an industrial robot arm is vital for advancing manufacturing-related applications like automatic assembly, which can be improved via the data-driven approaches to robot arm calibration. Existing data-driven calibrators have illustrated their efficiency in addressing the issue of robot arm calibration. However, they mostly are single learning models that can be easily affected by the insufficient representation of the solution space, therefore, suffering from the calibration accuracy loss. To address this issue, this study proposes a calibrator fuzzy ensemble (CFE) with twofold ideas: 1) implementing eight data-driven calibrators relying on different sophisticated machine learning algorithms for an industrial robot arm, which guarantees the accuracy of individual base models and 2) innovatively developing a fuzzy ensemble of the obtained eight diversified calibrators to obtain impressively high calibration accuracy for an industrial robot arm. Extensive experiments on an ABB IRB120 industrial robot implemented with MATLAB demonstrate that compared with state-of-the-art calibrators, CFE decreases the maximum error at 8.59%. Hence, it has great potential for real applications.

This article is concerned with resilient consensus control for a heterogeneous multiagent system (HMAS) in the presence of malicious attacks on sensors. Most existing strategies are dependent on compensation principle resulting in bounded consensus error. To address this issue, a multiround attack detection and isolation (MR-ADI) algorithm is presented, and, with this algorithm, a resilient isolation-based control strategy is developed to achieve output synchronization. In detail, via output regulator equations, heterogeneous followers are transformed into a virtual layer with same output matrices. With the transformation information from neighbors, a distributed attack monitor is constructed for generating feature signals. With the help of such signals from monitors, a centralized MR-ADI algorithm precisely locates paralyzed followers via a supervisory center, and a distributed MR-ADI algorithm is further proposed only with local and neighbor information. In theory, it is proven that the convergence of output consensus error is ensured. Simulation examples are presented to demonstrate the availability of our theoretical results.

An event-triggered output feedback control approach is proposed via a disturbance observer and adaptive dynamic programming (ADP). The solution starts by constructing a nonlinear disturbance observer, which only depends on the measurement of system output. A state observer is then developed based on approximation information of system dynamics via neural networks. In order to avoid continuous transmission and reduce the communication burden in the closed-loop system, an event-triggered mechanism is introduced such that the control signal is updated only at a specific instant when a triggered condition is violated. By virtue of the disturbance observer and state observer, an output-feedback ADP control approach then is developed, where only a critic network is employed to estimate the value function. Based on the Lyapunov stability theory, the stability of the closed-loop system is rigorously analyzed, and the effectiveness of the proposed control approach is verified by two simulation examples.

For a class of nontriangular nonlinear systems in presence of unknown disturbances, we propose a predictor-based neural dynamic surface control (PNDSC) strategy in this paper. This nontriangular system is transformed via the mean value theorem, and a predictor is then constructed. To avoid an algebraic loop problem, partial state vectors are employed as input signals of neural networks (NNs) for approximating unknown dynamics, and compensation items are designed to compensate for approximation errors from NNs. Different from the traditional NDSC, the PNDSC in this paper utilizes prediction errors to update learning parameters for improving NNs’ learning behaviors with overlarge adaptive gains. On the basis of improved NNs’ approximation behaviors, a predictor-based NNs disturbance observer (PNNDO) is constructed for compensation for external disturbances and approximation errors from NNs. Furthermore, with predictors, a normalization method of weights is developed to reduce the number of online learning parameters. On the basis of the aforementioned result, measurement noises are taken into account in our predictor-based neural control strategy. We employ predictor states, rather than measurement information paralyzed by noises, in design of our control strategy. This reduces high-frequency oscillations in control input. A Lyapunov-based stability analysis shows that all signals are ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed control strategy is verified by a numerical example and a permanent magnet brushless DC motor system.

Information exchanges among interacting agents play a significant role in guaranteeing successful completion of the desired coordinated control tasks for a multi-agent system (MAS). Furthermore, these information exchanges are often performed over some open and resource-constrained communication networks, thereby making security and resource efficiency vitally important for various multi-agent coordinated control problems. This paper addresses a secure dynamic event-trigger-based resilient consensus control problem for an MAS in the presence of both sensor and actuator attacks. First, a distributed adaptive compensator is introduced for prediction of unavailable system states. Due to the existence of sensor and actuator attack signals, a secure dynamic event-triggered mechanism is then proposed, and a resilient control strategy is further devised for the paralyzed MAS. It is theoretically proved that the controlled MAS is asymptotically stable and asymptotic consensus is eventually achieved among the coordinated agents regardless of the attacks and constrained resources. Finally, three examples are provided to illustrate the effectiveness of the proposed strategy.

Friedreich's ataxia (FRDA) is the most common inherited ataxia that causes progressive damage of nervous systems and performance deterioration of physical movements. FRDA baseline data analysis plays a crucial role in advancing the disease research, where the main obstacle comes from the baseline data collection primarily due to the degenerative symptoms of the FRDA patients. Inspired by the nowadays popular collaborative filtering (CF) method, a new FRDA baseline data collection algorithm is proposed in this article, with which the patients (or their families) are only required to provide certain reliable baseline data acquired from home and the uncertain/missing parts of the data can then be predicted with acceptable accuracy by utilizing existing patient information. The framework of the proposed algorithm is constructed based on a novel hybrid model combining the merits of model- and memory-based CF methods, thereby facilitating the baseline data collection with improved prediction accuracy. The proposed hybrid algorithm exhibits the following two main features: when a patient does not have neighbors sharing similar baseline data, the model-based CF component is activated to employ certain clustering method to find similar neighbors based on their attributes; and in the case that a patient does have neighbors, a novel similarity measure, which accounts for more statistical characteristics by integrating rating habits and degree of co-rated items, is developed in the memory-based component of the algorithm in order to adjust initial similarities between the patients. To evaluate the advantages of the proposed algorithm, the Scale for the Assessment and Rating of Ataxia is selected from the European FRDA Consortium for Translational Studies database. Experimental results demonstrate that our proposed hybrid CF approach is superior to other conventional approaches.

An observer-based containment control issue is addressed for a class of uncertain nonlinear multiagent systems with a directed topology via the active disturbance rejection control and backstepping techniques. A kind of nonlinear extended state observers (ESOs) based on fractional power functions is developed, and the estimations of extended states are utilized to compensate uncertain dynamics in real time. Compared with linear ESOs, the advantages of the ESOs in this paper lie in peaking reduction and better tolerance of measurement noise for the closed-loop system. Moreover, tracking differentiators are employed to avoid the explosion of complexity caused by repeated differentiations of nonlinear functions. It is proven that the containment errors of the followers converge to small neighborhoods of the origin and they are adjustable by suitable choice of parameters. Finally, two simulation examples, both practical and numerical ones, are shown to demonstrate the effectiveness of the proposed control approach.

The stochastic nature of intermittent energy resources has brought significant challenges to the optimal operation of the hybrid energy systems. This article proposes a probabilistic multiobjective evolutionary algorithm based on decomposition (MOEA/D) method with two-step risk-based decision-making strategy to tackle this problem. A scenario-based technique is first utilized to generate a stochastic model of the hybrid energy system. Those scenarios divide the feasible domain into several regions. Then, based on the MOEA/D framework, a probabilistic penalty-based boundary intersection (PBI) with gradient descent differential evolution (GDDE) algorithm is proposed to search the optimal scheme from these regions under different uncertainty budgets. To ensure reliable and low risk operation of the hybrid energy system, the Markov inequality is employed to deduce a proper interval of the uncertainty budget. Further, a fuzzy grid technique is proposed to choose the best scheme for real-world applications. The experimental results confirm that the probabilistic adjustable parameters can properly control the uncertainty budget and lower the risk probability. Further, it is also shown that the proposed MOEA/D-GDDE can significantly enhance the optimization efficiency.

This paper addresses an adaptive event-triggered consensus control problem for a class of second-order nonlinear multiagent systems (MASs) in an undirected communication topology. A novel adaptive distributed event-triggered consensus control scheme is presented for the MAS with unknown functions based on the definition of an auxiliary state, and the coefficient of the triggered function can be regulated adaptively with dependence on the auxiliary state error to ensure not only the control performance but also the efficiency of the network interactions. Furthermore, two self-triggered algorithms are developed for two cases, known functions and unknown ones, by the current state and information at the previous event time instant instead of the requirement for continuous monitoring auxiliary state errors. In theory, the stability of the resulting closed-loop system is rigorously investigated, and it is proven that all signals in the closed-loop system are bounded and the Zeno behavior is ruled out. Finally, two simulation examples, both real-time and numerical ones, are provided to verify the theoretical claims.

A voltage regulation scheme considering communication security is proposed for photovoltaic (PV) power system. The scheme is a two-level regulation to, respectively, reduce overall voltage deviation (VDE) and voltages difference (VDI). First, the evaluation indexes of VDE and VDI are built. Then, primary regulation through a powers compensation scheme is used. Considering communication topology change and delay under upper bound, secondary regulation through consensus protocol is developed. In addition, communication packet-loss and large delay are solved by predictive compensation. Finally, effectiveness of the proposed method is verified by simulation in MATLAB.