This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertainties, and external disturbances. First, a radial basis function NN method is utilized to tackle the unknown input saturations, dead zones, and model uncertainties. Then, based on the backstepping approach, two adaptive NN boundary controllers with update laws are employed to stabilize the like-position loop subsystem and like-posture loop subsystem, respectively. With the introduced control laws, the uniform ultimate boundedness of the deflection and angle tracking errors for the flexible manipulator are guaranteed. Finally, the control performance of the developed control technique is examined by a numerical example.

Due to the fast development of distributed energy resources and demand-side response management, agents in electricity markets are becoming more proactive, which boosts the development of peer-to-peer (P2P) market mechanisms. However, to our knowledge, none of the existing works considers clearing both energy and reserve via a P2P market mechanism in order to compensate for the uncertainty originating from renewable generation and allocate the reserve cost induced by uncertainty fairly. In this paper, a novel P2P joint energy and reserve market is proposed, where each agent can negotiate with neighboring agents to determine the quantities and prices of traded energy and reserve. We model the renewable generation uncertainty by versatile distribution and determine the required reserve based on a chance-constrained optimization approach. Then, a fully decentralized P2P market based on consensus alternating direction method of multipliers (ADMM) theory is proposed. In addition, to further lower the social cost, we exploit the correlation and complementarity among uncertainties and design a renewable community-based market, where all renewable agents share uncertainty information to community manager for calculating total required reserve. Finally, simulation results show the convergence performance, fairness and scalability of our market mechanism.

This study focuses on the adaptive fuzzy predefined-time attitude tracking control problem for rigid spacecraft with inertia uncertainties, external disturbances, and state constraints. In control design, fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and a quadratic-fraction barrier Lyapunov function (QFBLF) is introduced to ensure that the predefined state constraints are not violated. Compared with the existing dynamic surface control (DSC) approaches, a novel predefined-time filter and an adaptive fuzzy predefined-time controller are presented, such that the filter error and attitude tracking error can converge to a small region in predefined time simultaneously, where the minimum upper bound of settling time can be exactly preset by tuning one control parameter. Comparative simulation results are carried out to verify the superiority and efficacy of the presented strategy.

In DC microgrids, load power sharing and bus voltage regulation are two common control objectives. In this paper, a consensus-based algorithm is presented to achieve proportional power sharing and regulation of weighted geometric mean of bus voltages in DC microgrids with ZIP (constant impedance, constant current, and constant power) loads simultaneously. By using the virtue of the Laplacian matrices of undirected connected graphs, a lemma is derived to assist the stability analysis of the DC microgrids. Thus, a sufficient condition that stabilizes the system with ZIP loads is established. In addition, with the help of a distributed voltage regulation error estimator, the tuning of the bus voltages can be realized without the requirement on initial voltage conditions. Through Lyapunov synthesis, the large-signal stability of the closed-loop system is theoretically ensured for a wide range of load conditions. Finally, simulation studies are performed to validate the merits of the proposed consensus-based algorithm.

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in ‘lazy’ worker-server communications, which justifies the term L azily A ggregated Q uantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds . Empirically, extensive experiments using realistic data corroborate a significant communication reduction compared with state-of-the-art gradient- and stochastic gradient-based algorithms.

Direct current (dc) microgrids have been widely used in many critical applications. Such systems avoid unnecessary ac/dc conversions and can simplify control design. To achieve high-performance control of such system, advanced control algorithm needs to be designed. This paper presents a novel decentralized output constrained control algorithm for single-bus dc microgrids. The control objectives are to realize high-performance control of dc bus voltage, user-defined load sharing, and circulating current minimization. Unlike conventional control algorithms, the control algorithm can guarantee not only convergence but also bounded transient tracking error. By transforming the constrained system into an unconstrained system, the transient response of dc bus voltage can always stay within user-defined, time-varying bounds. Convergence of the transformed system can meet any transient performance requirement of the original system. Through proper control effort distribution, overall load demand can be shared among the distributed generators (DGs) according to predefined percentages. Accurate load sharing can indirectly minimize the harmful circulating currents among the DGs. Switch-level simulation and hardware experimentation with both single-bus and multiple-bus dc microgrids demonstrate the effectiveness of the proposed control design.