Abstract—In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly perturbed systems (MSPS) are discussed. The uniqueness and the asymptotic structure of the solution to the generalized cross–coupled multiparameter algebraic Riccati equations (GCMARE) are newly established without the nonsingularity assumptions for the fast state matrices. The main contribution of this paper is that a construction of high–order approximations to a strategy that guarantees a desired performance level on the basis of a new iterative technique is proposed. As a result, it is shown that the high–order accuracy strategy improves the performance. I
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
Abstract. In this paper, we study the Pareto optimal strategy for multiparameter singu-larly perturb...
Abstract. A recursive method for the solution of the Riccati equations that yield the matrix gains r...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
Abstract — The linear quadratic Nash games for infi-nite horizon multiparameter singularly perturbed...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
Abstract. In this paper, we study the Pareto optimal strategy for multiparameter singu-larly perturb...
Abstract. A recursive method for the solution of the Riccati equations that yield the matrix gains r...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
In this paper, the linear quadratic Nash games for infinite horizon nonstandard multiparameter singu...
Abstract — The linear quadratic Nash games for infi-nite horizon multiparameter singularly perturbed...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this brief, linear quadratic infinite-horizon Nash games for general multiparameter singularly pe...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
In this paper, the linear quadratic Nash games for infinite horizon multiparameter singularly pertur...
Abstract. In this paper, we study the Pareto optimal strategy for multiparameter singu-larly perturb...
Abstract. A recursive method for the solution of the Riccati equations that yield the matrix gains r...