Donglian Qi

Discrete-time adaptive iterative learning control for high-order nonlinear systems with unknown control directions

An adaptive iterative learning control method is proposed for a class of nonlinear strict-feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate the future states in the control design. Discrete Nussbaum gain method is utilised to deal with the lack of a priori knowledge of control directions. The proposed control algorithm guarantees the boundedness of all the signals in the controlled system. The tracking error converges to zero asymptotically along the iterative learning axis except for beginning states affected by random initial conditions. The effectiveness of the proposed control scheme is verified through numerical simulation.

Feedback nonlinear discrete-time systems

In this paper, we design an adaptive iterative learning control method for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. An n-step ahead predictor approach is employed to estimate future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with unknown control directions. The proposed control algorithm ensures that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by random initial conditions. A numerical simulation is carried out to demonstrate the efficacy of the presented control laws.

Iterative Learning Control Design with High-Order Internal Model for Permanent Magnet Linear Motor

In this paper, an iterative learning control algorithm was proposed for improving the permanent magnet linear motor (PMLM) velocity tracking performance under iteration-varying desired trajectories. A high-order internal model (HOIM) was utilized to describe the variation of desired trajectories in the iteration domain. By incorporating the HOIM into P-type ILC, the convergence of tracking error can be guaranteed. The rigorous proof was presented to show that the system error converge well. The simulation results indicate that the proposed high-order internal models based approach yields a good performance and achieves perfect tracking.

Distributed Estimation and Secondary Control of Autonomous Microgrid

This paper proposes a systematic approach to balance the active power between photovoltaic generators (PVs) and loads in autonomous microgrids. To achieve this, a distributed algorithm is designed to estimate the power difference between generation and consumption, then a finite-time consensus protocol is introduced to regulate the outputs of all the PVs in a cooperative and timely fashion, and the frequency deviation caused by active power unbalance can be compensated as well. In particular, the proposed distributed estimation and secondary control strategy is completely distributed and center-free in the sense that each PV and load are both self-organizing and global-awareness with only local communication, no centralized monitors are needed. Simulations on the standard IEEE 37-bus network are presented to verify the effectiveness of the proposed strategy.

A new game model for distributed optimization problems with directed communication topologies

Abstract In this paper, the distributed optimization problems of multi-agent systems with directed communication topologies are investigated by using the game theory. In particular, a new general non-cooperative game model, termed state based weakly acyclic game, is provided to solve the problem. Based on this approach, the desired global objective is achieved by designing local objective function for individual agent to make coordination decisions. It is worth noting that all the obtained equilibria are thus solutions to the proposed distributed optimization problems with directed and time-varying communication topologies. Simulations on consensus problem in multi-agent systems are provided to verify the validness of the proposed methodology.

A game theoretical formulation for distributed optimization problems

The focus of this paper is to develop a theoretical framework for analysis and design of distributed optimization problem in multi-agent systems by using the language of game theory and cooperative control methodology. In the framework, a piecewise-constant and binary-valued matrix in the cooperative control theory is introduced to describe the sensing/communication among agents and to cope with the practical situations where the information sharing may be in a distributed, dynamically changing and local manner. Based on information acquisition/communication model, state based ordinal potential game is designed to capture the optimal solution to distributed optimization problems in multi-agent systems by appropriately specifying local objective function for each individual decision maker. It is worth noting that the proposed analysis and design methodology has the advantages that the resulted equilibriums are capable of solving the distributed optimization problems even if the corresponding communication topologies is local, time-varying and intermittent. Meanwhile, the minimal requirement for the communication among the agents is provided to ensure the global objective is desirable under the new framework.

Recursive Model Predictive Control for Fast Varying Dynamic Systems

A well known drawback of model predictive control (MPC) is that it can only be adopted in slow dynamics, where the sample time is measured in seconds or minutes. The main reason leads to the problem is that the optimization problem included in MPC has to be computed online, and its iterative computational procedure requires long computational time. To shorten computational time, a recursive approach based on Iterative Learning Control (ILC) and Recursive Levenberg Marquardt Algorithm (RLMA) is proposed to solve the optimization problem in MPC. Then, recursive model predictive control (RMPC) is proposed to realize MPC for fast varying dynamic systems. Simulation results show the effectiveness of RMPC compared with conventional MPC.

Distributed Optimization and State Based Ordinal Potential Games

The focus of this paper is to develop a theoretical framework to analyze and address distributed optimization problem in multi-agent systems based on the cooperative control methodology and game theory. First the sensing/communication matrix is introduced and the minimal communication requirement among the agents is provided. Based on the matrix communication model, the state based ordinal potential game is designed to capture the optimal solution. It is worth noting that the proposed methodology can guarantee the distributed optimization problem converge to desired system level objective, even though the corresponding communication topologies may be local, time-varying and intermittent. Simulations on a multi-agent consensus problem are provided to verify the validness of the proposed methodology.

Output feedback adaptive iterative learning control for nonlinear discrete-time systems with unknown control directions

An adaptive iterative learning control method is developed for a class of high-order nonlinear output feedback discrete-time systems with random initial conditions and iteration-varying desired trajectories. The n-step ahead predictor approach is employed to estimate the future outputs. The discrete Nussbaum gain method is incorporated into the control design to deal with the lack of a priori knowledge of the control directions. The proposed control algorithm guarantees that the tracking error converges to zero asymptotically along the iterative learning axis except for the beginning outputs affected by the random initial conditions with all the signals in the controlled system bounded. A numerical simulation is carried out to demonstrate the effectiveness of the proposed control laws.

A Game Strategy for Power Flow Control of Distributed Generators in Smart Grids

We consider the distributed power control problem of distributed generators(DGs) in smart grid. In order to ensure the aggregated power output level to be desirable, a group of DGs with local and directed communications are expected to operate at the specified same ratio of their maximal available power output. To that end, the non-cooperative game is introduced and the DGs are modeled as self-interested game players. A new game model, termed state based weakly acyclic game, is developed to specify decision making architecture for each DGs, and at the point of the equilibrium of the game, the global objective of the power control problem can be achieved through autonomous DGs that are capable of making rational decisions to optimize their own payoff functions based on the local and directed information from other DGs. The validness of the proposed methodology is verified in simulation.