Robust Decentralized Voltage Tracker of Islanded Multi-DG AC Microgrids Using Invariant Ellipsoids

This chapter develops a robust decentralized voltage tracker for islanded MGs. The proposed controller is robust against the plug and play operation of the MG, loads, and line parameter uncertainties. The problem is solved in the framework of linear matrix inequality (LMI). The proposed robust control represents the load changes and the parameter variations of lines connecting the DGs as a norm-bounded uncertainty. The proposed controller utilizes local measurements from DGs (i.e., it is totally decentralized). Control decentralization is accomplished by decomposing the global system into subsystems. The effect of the rest of the system on a specific subsystem is considered as a disturbance to minimize (disturbance rejection control). The controller is designed by the invariant-sets (approximated by the invariant ellipsoids). Different time-domain simulations are carried out as connecting and disconnected one or more DGs, connecting and disconnecting local loads DGs and transmission line parameters variation.

Convexification of Permutation-Invariant Sets and an Application to Sparse Principal Component Analysis

We develop techniques to convexify a set that is invariant under permutation and/or change of sign of variables and discuss applications of these results. First, we convexify the intersection of the unit ball of a permutation and sign-invariant norm with a cardinality constraint. This gives a nonlinear formulation for the feasible set of sparse principal component analysis (PCA) and an alternative proof of the K-support norm. Second, we characterize the convex hull of sets of matrices defined by constraining their singular values. As a consequence, we generalize an earlier result that characterizes the convex hull of rank-constrained matrices whose spectral norm is below a given threshold. Third, we derive convex and concave envelopes of various permutation-invariant nonlinear functions and their level sets over hypercubes, with congruent bounds on all variables. Finally, we develop new relaxations for the exterior product of sparse vectors. Using these relaxations for sparse PCA, we show that our relaxation closes 98% of the gap left by a classical semidefinite programming relaxation for instances where the covariance matrices are of dimension up to 50 × 50.

Stability analysis method and application of multi-agent systems from the perspective of hybrid systems

Many multi-agent systems (MASs) can be regarded as hybrid systems that contain continuous variables and discrete events exhibiting both continuous and discrete behavior. An MAS can accomplish complex tasks through communication, coordination, and cooperation among different agents. The complex, adaptive and dynamic characteristics of MASs can affect their stability that is critical for MAS performance. In order to analyze the stability of MASs, we propose a stability analysis method based on invariant sets and Lyapunov’s stability theory. As a typical MAS, an unmanned ground vehicle formation is used to evaluate the proposed method. We design discrete modes and control polices for the MAS composed of unmanned ground vehicles to guarantee that the agents can cooperate with each other to reliably achieve a final assignment. Meanwhile, the stability analysis is given according to the definition of MAS stability. Simulation results illustrate the feasibility and effectiveness of the proposed method.

Predicate Transformer Semantics for Hybrid Systems

We present a semantic framework for the deductive verification of hybrid systems with Isabelle/HOL. It supports reasoning about the temporal evolutions of hybrid programs in the style of differential dynamic logic modelled by flows or invariant sets for vector fields. We introduce the semantic foundations of this framework and summarise their Isabelle formalisation as well as the resulting verification components. A series of simple examples shows our approach at work.

Performance Analysis for Discrete-Time Linear Systems with Saturated Linear Feedback: a Nonlinear Saturation-Dependent Gain-Scheduling Approach

In this paper, we propose a new technique for the performance analysis of discrete-time linear systems controlled by a saturated linear control law. Two performance indices, the computation of invariant sets and the L2 performance, are considered. The main contributions of the paper are the following: i) a new linear parameter varying system framework is presented to model the saturated system, ii) a nonlinear saturation-dependent auxiliary feedback matrix is considered, iii) new sufficient conditions for the performance analysis are proposed. It is shown that the conditions can be expressed as a set of linear matrix inequalities. Furthermore, it is shown that the conditions are guaranteed to be less conservative than existing solutions in the literature. Three numerical examples are presented to illustrate the effectiveness of the proposed method. <br>

Performance Analysis for Discrete-Time Linear Systems with Saturated Linear Feedback: a Nonlinear Saturation-Dependent Gain-Scheduling Approach

In this paper, we propose a new technique for the performance analysis of discrete-time linear systems controlled by a saturated linear control law. Two performance indices, the computation of invariant sets and the L2 performance, are considered. The main contributions of the paper are the following: i) a new linear parameter varying system framework is presented to model the saturated system, ii) a nonlinear saturation-dependent auxiliary feedback matrix is considered, iii) new sufficient conditions for the performance analysis are proposed. It is shown that the conditions can be expressed as a set of linear matrix inequalities. Furthermore, it is shown that the conditions are guaranteed to be less conservative than existing solutions in the literature. Three numerical examples are presented to illustrate the effectiveness of the proposed method. <br>