The construction of control barrier functions (CBFs) based on differentiable optimization ensures the invariance of dynamic systems, which is suitable for collision-free motion planning of robotic manipulators. Yet this class of differentiable optimization algorithms suffers from relatively low calculation performance, especially in scenarios with diverse and complex obstacles. This paper proposes a CBF controller based on an improved differentiable optimization for efficient reactive motion planning of robots. Specifically, the safety control problem for robots is formulated as a quadratic programming problem subject to CBF constraint, referring to CBF and its gradient. Then, the minimum non-negative distance is introduced to construct the differentiable CBFs, which is essentially a convex optimal problem, and can be solved through Gilbert-Johnson–Keerthi (GJK) algorithm to enhance real-time performance. In the mean-time, the gradient of CBF can be calculated by the equivalence between the convex optimal problem and Karush–Kuhn–Tucker (KKT) conditions, which uses the block matrix inverse technique together with Schur complement to compute the inverse of KKT differential matrix and the resultant differential of CBF. Further, the CBF-based quadratic program is employed to derive the control law for efficient motion planning of robots. It is shown that the combination of GJK algorithm and KKT conditions reduces the computational cost of the proposed differential optimization algorithm for motion planning of robots. Finally, simulation studies and comparisons are conducted to validate the effectiveness and superiority of the proposed method for safety control of mobile robots and manipulators over the baseline, particularly in environments with complex-shaped obstacles.

Renewable energy is incorporated to the energy supply for heterogeneous networks (HetNets) to mitigate the high energy consumption and meet carbon-neutral targets. This paper investigates resource management in HetNets with hybrid energy supply, where base stations are powered by renewable energy and power grid, and can share the harvested renewable energy with each other. The objective is to maximize the long-term average utility of the network defined as a weighted sum of system data rate and power grid energy cost. In view of the difference of dynamics characteristics between the radio and energy resources, the problem is formulated by describing energy resources over the large timescale relative to the small one for radio resources. To address the problem without prior knowledge, a two-timescale deep reinforcement learning (DRL)-based optimization framework is proposed, which is structured with two layers of decision-making, including the energy scheduling layer over the large timescale for renewable energy sharing among BSs and power grid energy consumption, and the power control layer over the small timescale for transmit power, respectively. To enhance the performance of the cross-layer collaborative optimization, in the energy scheduling layer an improved soft actor-critic with two-timescale update rule (TTUR-SAC) algorithm is designed with a novel strategy of differentiating the training frequencies of actor and critic networks, and in the power control layer the successive convex approximation method is leveraged to solve the non-convex subproblem for the transmit power. Simulation results demonstrate that under various system configurations our proposed solution outperforms the typical baseline methods.

The widespread integration of renewable energy sources poses challenges to the stability of power systems, necessitating a focus on uncertainty quantification to analyze the inherent variability and its impacts on power system reliability and stability. This paper propose a Koopman-based simulation method to evaluate the effect of continuous-time stochastic disturbance on power system operation, which results from the widespread utilization of renewable generation. First, the continuous-time stochastic disturbance is approximated as an Ito process model, then the Karhunen-Loève expansion(KLE) method is used to spectrally decompose the random process so that the Latin hypercube sampling (LHS) method can be applied on the continuous-time variables. Second, the random parameters in the KLE are involved in the system states to formulate an augment states matrix. EDMD method is then used with the augment states to approximate the Koopman operator, which translates the nonlinear system into a high dimensional linear system. Finally, a simulation carried out on the IEEE 10-machine, 39-bus New England power system validates the proposed Koopman operator based simulation method.

Reconfigurable intelligent surface (RIS) is a radical and cost-efficient technology to improve energy efficiency and mitigate interference in the heterogeneous network. In this paper, the resource allocation problem of sub-channels, transmit power and RIS coefficients is investigated in the RIS-aided heterogeneous network. To solve the formulated mixed integer nonlinear programming problem, a two-step centralized resource allocation algorithm and a two-step distributed resource allocation algorithm are proposed based on the alternating optimization method. In the centralized algorithm, the sub-channel allocation, transmit power and RIS coefficients are optimized by the macro base station solely, where the non-convex power optimization problem is transformed into a convex one based on the convex approximation method. In the distributed algorithm, which aims to alleviate the computational burden of the macro base station, the sub-channel allocation and transmit power are optimized by using the cooperation of all the small base stations and the macro base station. Finally, numerical results are presented to demonstrate the convergence of the proposed algorithms and the effectiveness of the sub-channel transfer. More importantly, it is shown that the centralized algorithm can achieve the higher total throughput, while the distributed algorithm greatly decreases the resource allocation time.

This paper investigates a simultaneous wireless information and power transfer-enabled hybrid-powered multicell communication network with a nonlinear energy harvesting model. In this multicell environment, information interaction and energy trading are carried out among base stations (BSs), and each BS powered by hybrid sources simultaneously provides information/energy to its user equipments (UEs) over the downlinks. A novel global utility function is proposed by comprehensively considering the incomes from information and energy transmission, energy trading, and the costs from the electricity companies. To maximize this goal, a nonconvex problem that jointly optimizing energy procurement, power allocation, and power splitting ratios is formulated to deal with the imbalance between energy supply and demand at BSs. Considering the nonconvexity of the problem and the strong coupling among the optimization variables, the formulated problem is difficult to solve directly with conventional convex optimization methods. To overcome these obstacles, a two-step solution combining alternating optimization, distributed optimization, and successive convex approximation is designed, in which the BS layer scheme and the UE layer scheme are performed alternately. Different from the existing centralized schemes, the proposed distributed scheme makes local optimal decisions independently at each BS only resorting to the information of neighbor BSs, which brings great advantages in reducing signaling and computational overheads. Moreover, the convergence and advantages of the proposed distributed scheme are verified by rigorous analyses and simulations.

This work considers a wireless powered communication network (WPCN), in which wireless nodes store the energy from an energy access point in their batteries for subsequent data transmission. An online energy consumption optimization strategy is proposed for adaptively determining the beamforming vector, data routing, network operation mode and transmitted power based only on the current state of WPCN. In most existing results, the energy/data transmission of WPCNs is based on the ideal battery models and the energy storage efficiencies therein are always assumed to be non-zero constants. Since the energy storage efficiency of batteries may be affected by the ambient environment or aging in real-time, this work considers a WPCN with a time-varying energy storage efficiencies sequence and correspondingly develops an improved Lyapunov optimization strategy to offset the impact of the time-varying energy storage efficiencies. More importantly, a distributed strategy is proposed to optimize the cooperation of wireless nodes over unrestricted numbers of hops, and thus the energy access point does not require channel state information of all data links and the data backlog queues of all nodes during the solving process. Accordingly, the computational burden at the energy access point is greatly reduced due to the use of this distributed strategy. Under this strategy, the time-averaged expected energy consumption of WPCN can be within a bounded gap of the minimum energy required to maintain stability of the network. Finally, the theoretical analysis is further corroborated by simulation results.