Enhancing Iterative Learning Control With Fractional Power Update Law

The P-type update law has been the mainstream technique used in iterative learning control(ILC)systems,which resembles linear feedback control with asymptotical convergence.In recent years,finite-time control strategies such as terminal sliding mode control have been shown to be effective in ramping up convergence speed by introducing fractional power with feedback.In this paper,we show that such mechanism can equally ramp up the learning speed in ILC systems.We first propose a fractional power update rule for ILC of single-input-single-output linear systems.A nonlinear error dynamics is constructed along the iteration axis to illustrate the evolutionary converging process.Using the nonlinear mapping approach,fast convergence towards the limit cycles of tracking errors inherently existing in ILC systems is proven.The limit cycles are shown to be tunable to determine the steady states.Numerical simulations are provided to verify the theoretical results.

Price-Based Residential Demand Response Management in Smart Grids:A Reinforcement Learning-Based Approach

This paper studies price-based residential demand response management(PB-RDRM)in smart grids,in which non-dispatchable and dispatchable loads(including general loads and plug-in electric vehicles(PEVs))are both involved.The PB-RDRM is composed of a bi-level optimization problem,in which the upper-level dynamic retail pricing problem aims to maximize the profit of a utility company(UC)by selecting optimal retail prices(RPs),while the lower-level demand response(DR)problem expects to minimize the comprehensive cost of loads by coordinating their energy consumption behavior.The challenges here are mainly two-fold:1)the uncertainty of energy consumption and RPs;2)the flexible PEVs’temporally coupled constraints,which make it impossible to directly develop a model-based optimization algorithm to solve the PB-RDRM.To address these challenges,we first model the dynamic retail pricing problem as a Markovian decision process(MDP),and then employ a model-free reinforcement learning(RL)algorithm to learn the optimal dynamic RPs of UC according to the loads’responses.Our proposed RL-based DR algorithm is benchmarked against two model-based optimization approaches(i.e.,distributed dual decomposition-based(DDB)method and distributed primal-dual interior(PDI)-based method),which require exact load and electricity price models.The comparison results show that,compared with the benchmark solutions,our proposed algorithm can not only adaptively decide the RPs through on-line learning processes,but also achieve larger social welfare within an unknown electricity market environment.

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

In this paper,accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network,which enables a hyper-exponential convergence rate.Specifically,an inertial fast-slow dynamical system with vanishing damping is introduced,based on which the distributed saddle point algorithm is designed.The dual variables are updated in two time scales,i.e.,the fast manifold and the slow manifold.In the fast manifold,the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol.In the slow manifold,the updating of the Lagrangian multipliers is accelerated by inertial terms.Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation.The simulation of the application in the energy dispatch problem verifies the result,which demonstrates the fast convergence of the proposed saddle point dynamics.

Discretization behavior analysis of a switching control system from a unified mathematical approach

A useful unified analysis framework is proposed for exploring the intriguing behaviors of a second-order switching control system. Complex discretization behaviors of the switching control system are explored in detail, and some intrinsic relationships between the system periodic behaviors and their associated symbolic sequences are studied. Keywords Switching control - Chaos - Discretisation - Periodicity This work was supported by the Australian Research Council and the Hong Kong Research Grants Council for their financial supports, under the CERG Grants CityU 1018/01E, 1004/02E, and 1115/03E.

Recent progress on the study of distributed economic dispatch in smart grid:an overview

Designing an efficient distributed economic dispatch(DED)strategy for the smart grid(SG)in the presence of multiple generators plays a paramount role in obtaining various benefits of a new generation power syst em,such as easy implementation,low maintenance cos t,high energy efficiency,and strong robus tn ess agains t uncertainties.It has drawn a lot of interest from a wide variety of scientific disciplines,including power engineering,control theory,and applied mathematics.We present a state-of-the-art review of some theoretical advances toward DED in the SG,with a focus on the literature published since 2015.We systematically review the recent results on this topic and subsequently categorize them into distributed discrete-and continuous-time economic dispatches of the SG in the presence of multiple generators.After reviewing the literature,we briefly present some future research directions in DED for the SG,including the distributed security economic dispatch of the SG,distributed fast economic dispatch in the SG with practical constraints,efficient initialization-free DED in the SG,DED in the SG in the presence of smart energy storage batteries and flexible loads,and DED in the SG with artificial intelligence technologies.

Automatic Generation Control of Multi-area Power System with Network Constraints and Communication Delays

Newly proposed power system control methodologies combine economic dispatch(ED) and automatic generation control(AGC) to achieve the steady-state cost-optimal solution under stochastic operation conditions. However, a real power system is subjected to continuous demand disturbance and system constraints due to the input saturation, communication delays and unmeasurable feed-forward load disturbances. Therefore, optimizing the dynamic response under practical conditions is equally important. This paper proposes a state constrained distributed model predictive control(SCDMPC)scheme for the optimal frequency regulation of an interconnected power system under actual operation conditions, which exist due to the governor saturation, generation rate constraints(GRCs), communication delays, and unmeasured feed-forward load disturbances. In addition, it proposes an algorithm to handle the solution infeasibility within the SCDMPC scheme, when the input and state constraints are conflicting. The proposed SCDMPC scheme is then tested with numerical studies on a three-area interconnected network. The results show that the proposed scheme gives better control and cost performance for both steady state and dynamic state in comparison to the traditional distributed model predictive control(MPC) schemes.

Bridging the gap between complex networks and smart grids

It is well known that many real-world systems can be described by complex networks with the nodes and the edges representing the individuals and their communications,respectively.Based on recent advances in complex networks,this paper aims to provide some new methodologies to study some fundamental problems in smart grids.In particular,it summarises some results for network properties,distributed control and optimisation,and pinning control in complex networks and tries to reveal how these new technologies can be applied in smart grids.