In this work, given a linear multivariable system, the problem of static state feedback realization of dynamic compensators is considered. Necessary and sufficient conditions for the existence of a static state feedback that realizes the dynamic compensator (square or full column rank compensator) are stated in structural terms, i. e., in terms of the zero-pole structure of the compensator, and the eigenvalues and the row image of the controllability matrix of the compensated system. Based on these conditions a formula is presented to find the state feedback matrices realizing a given compensator. It is also shown that the static state feedback realizing the compensator is unique if and only if the closed-loop system is controllable.
The control problem consists of stabilizing a control system while minimizing the norm of its transfer function. Several solutions to this problem are available. For systems in form, an optimal regulator can be obtained by solving two algebraic Riccati equations. For systems described by , either Wiener-Hopf optimization or projection results can be applied. The optimal regulator is then obtained using operations with proper stable rational matrices: inner-outer factorizations and stable projections. The aim of this paper is to compare the two approaches. It is well understood that the inner-outer factorization is equivalent to solving an algebraic Riccati equation. However, why are the stable projections not needed in the state-space approach? The difference between the two approaches derives from a different construction of doubly coprime, proper stable matrix fractions used to represent the plant. The transfer-function approach takes any doubly coprime fractions, while the state-space approach parameterizes such representations and those selected then obviate the need for stable projections.
This paper is concerned with synchronization of two coupled Hind-marsh-Rose (HR) neurons. Two synchronization criteria are derived by using nonlinear feedback control and linear feedback control, respectively. A synchronization criterion for FitzHugh-Nagumo (FHN) neurons is derived as the application of control method of this paper. Compared with some existing synchronization results for chaotic systems, the contribution of this paper is that feedback gains are only dependent on system parameters, rather than dependent on the norm bounds of state variables of uncontrolled and controlled HR neurons. The effectiveness of our results are demonstrated by two simulation examples.
In this paper, the feedback control for a class of bilinear control systems with a small parameter is proposed to guarantee the existence of limit cycle. We use the perturbation method of seeking in approximate solution as a finite Taylor expansion of the exact solution. This perturbation method is to exploit the "smallness" of the perturbation parameter ε to construct an approximate periodic solution. Furthermore, some simulation results are given to illustrate the existence of a limit cycle for this class of nonlinear control systems.