Huaguang Zhang

In this paper, the inverse optimal approach is employed to design distributed consensus protocols that guarantee consensus and global optimality with respect to some quadratic performance indexes for identical linear systems on a directed graph. The inverse optimal theory is developed by introducing the notion of partial stability. As a result, the necessary and sufficient conditions for inverse optimality are proposed. By means of the developed inverse optimal theory, the necessary and sufficient conditions are established for globally optimal cooperative control problems on directed graphs. Basic optimal cooperative design procedures are given based on asymptotic properties of the resulting optimal distributed consensus protocols, and the multiagent systems can reach desired consensus performance (convergence rate and damping rate) asymptotically. Finally, two examples are given to illustrate the effectiveness of the proposed methods.

In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.

Fuzzy logic methodology has been proven effective in dealing with complex nonlinear systems containing uncertainties that are otherwise difficult to model. Technology based on this methodology has bee

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

This paper is concerned with the problem of developing an advanced strategy to reduce the conservatism in stability analysis and control synthesis of continuous-time Takagi-Sugeno (T-S) fuzzy systems. A novel augmented multi-indexed matrix approach is proposed to implement new right-hand-side slack variables technique for the homogenous polynomial setting. Combining with the Finsler lemma with homogenous-matrix Lagrange multipliers, convergent linear-matrix-inequality (LMI) relaxations for stability analysis are proposed by using the generalization of the Polya theorem for the case of positive polynomials with matrix-valued coefficients. A new type of state-feedback controller, namely, the homogeneous polynomially nonquadratic control law (HPNQCL), is developed to conceive less-conservative stabilization conditions. The obtained stability and stabilization conditions are further relaxed by using the proposed right-hand-side slack variables technique. Moreover, the advantages over the existing control schemes are certificated in theory. Three numerical examples are also provided to illustrate the effectiveness of the proposed methods.