this paper describes an optimal design for multivariable pi controller with a high gain structure for an irregular system by genetic algorithm. pi controllers with a high gain structure leads to the asymptotic decomposition of the fast and slow modes in the closed loop system that have unique characteristics. the slow modes are asymptotically uncontrollable and unobservable; therefore, they hav...

this paper concerns a study on the optimal control for nonlinear systems. an appropriate alternative in order to alleviate the nonlinearity of a system is the exact linearization approach. in this fashion, the nonlinear system has been linearized using input-output feedback linearization (iofl). then, by utilizing the well developed optimal control theory of linear systems, the compensated nonl...

This paper describes an interactive, optimization-based multivariable control system design package which is currently under development at the University of California, Berkeley. The package will combine a number of subroutines from the Imperial College Multivariable Design System and the Kingston Polytechnic SLICE library with DELIGHT, the University of California, Berkeley, general purpose, ...

This paper deals with an iterative learning control law for multivariable systems. The desired inputs are supposed to be known and periodic. The principle of the control is to make outputs as close as possible to desired inputs at each new period. After the design of multivariable repetitive controller, we give the stability condition of the algorithm and some simulation results.