Xinli Shi

A compound control strategy is proposed for frequency regulation of source-grid-load systems in which power sources, power grids, and loads are all participating in the process. Here, power sources are conventional thermal generators, including new energy power generations, and loads are composed of energy storage units (ESUs) and grid-friendly appliances (GFAs). The proposed control scheme includes two levels of operations, with the upper level to be a model predictive control (MPC) for generators and the lower level to be a distributed leader-following consensus control strategy for multiple ESUs. For new energy power generations, the power outputs are restricted on a constant value during a sampling period based on a predicted generating curve. GFAs respond to the system frequency by regulating their active power consumption. Simulations on a single power system and three interconnected area power systems are provided to verify the effectiveness of the proposed compound control strategy.

Biodegradable metals are promising candidates for bone defect repair. With an evidence-based approach, this study investigated and analyzed the performance and degradation properties of biodegradable metals in animal models for bone defect repair to explore their potential clinical translation. Animal studies on bone defect repair with biodegradable metals in comparison with other traditional biomaterials were reviewed. Data was carefully collected after identification of population, intervention, comparison, outcome, and study design (PICOS), and following the inclusion criteria of biodegradable metals in animal studies. 30 publications on pure Mg, Mg alloys, pure Zn and Zn alloys were finally included after extraction from a collected database of 2543 publications. A qualitative systematic review and a quantitative meta-analysis were performed. Given the heterogeneity in animal model, anatomical site and critical size defect (CSD), biodegradable metals exhibited mixed effects on bone defect repair and degradation in animal studies in comparison with traditional non-degradable metals, biodegradable polymers, bioceramics, and autogenous bone grafts. The results indicated that there were limitations in the experimental design of the included studies, and quality of the evidence presented by the studies was very low. To enhance clinical translation of biodegradable metals, evidence-based research with data validity is needed. Future studies should adopt standardized experimental protocols in investigating the effects of biodegradable metals on bone defect repair with animal models.

In this paper, some efficient criteria for finite-time consensus of a class of nonsmooth opinion dynamics over a digraph are established. The lower and upper bounds on the finite settling time are obtained based respectively on the maximal and minimal cut capacity of the digraph. By using tools of the nonsmooth theory and algebraic graph theory, the Carathéodory and Filippov solutions of nonsmooth opinion dynamics are analyzed and compared in detail. In the sense of Filippov solutions, the dynamic consensus is demonstrated without a leader and the finite-time bipartite consensus is also investigated in a signed digraph correspondingly. To achieve a predetermined consensus, a leader agent is introduced to the considered agent networks. As an application, the nonsmooth compartmental dynamics in the presence of a leader is embedded in the proposed continuous-time protocol to solve the distributed optimization problems over an unbalanced digraph. The convergence to the optimal solution by using the proposed distributed algorithm is guaranteed with appropriately selected parameters. To verify the effectiveness of the proposed protocols, three numerical examples are performed.

In this paper, we study a special class of distributed convex optimization problems-distributed parametric consensus optimization problem (DPCOP), for which a two-stage optimization method including primal decomposition and distributed consensus is provided. Different from traditional distributed optimization problems driving all the local states to a common value, DPCOP aims to solve a system-wide problem with partial common parameters shared amongst local agents in a distributed way. To relax the restriction on the topology, a distributed projected subgradient method is applied in distributed consensus stage to achieve the consensus of local estimated parameters, while the subgradients can be obtained by solving a multiparametric problem locally. For a special class of DPCOPs, a discrete-time distributed algorithm with exponential rate of convergence is provided. Furthermore, the proposed two-stage optimization method is applied to a distributed model predictive consensus problem in order to reach an optimal output consensus at equilibrium points for all agents. The stability analysis for the proposed algorithm is further given. Two case studies on a heterogenous multiagent system with high-order integrator dynamics are provided to verify the effectiveness of proposed methods.