Family Of Least Squares Estimates Research Articles

Stochastic adaptive control for continuous-time linear systems with quadratic cost

An adaptive control problem is formulated and solved for a completely observed, continuous-time, linear stochastic system with an ergodic quadratic cost criterion. The linear transformationsA of the state,B of the control, andC of the noise are assumed to be unknown. Assuming only thatA is stable and that the pair (A, C) is controllable and using a diminishing excitation control that is asymptotically negligible for an ergodic, quadratic cost criterion it is shown that a family of least-squares estimates is strongly consistent. Furthermore, an adaptive control is given using switchings that is self-optimizing for an ergodic, quadratic cost criterion.

Adaptive Boundary and Point Control of Linear Stochastic Distributed Parameter Systems

An adaptive control problem for the boundary or the point control of a linear stochastic distributed parameter system is formulated and solved in this paper. The distributed parameter system is modeled by an evolution equation with an infinitesimal generator for an analytic semigroup. Since there is boundary or point control, the linear transformation for the control in the state equation is also an unbounded operator. The unknown parameters in the model appear affinely in both the infinitesimal generator of the semigroup and the linear transformation of the control. Strong consistency is verified for a family of least squares estimates of the unknown parameters. An Itô formula is established for smooth functions of the solution of this linear stochastic distributed parameter system with boundary or point control. The certainty equivalence adaptive control is shown to be self-tuning by using the continuity of th solution of a stationary Riccati equation as a function of parameters in a uniform operator topology. For a quadratic cost functional of the state and the control, the certainty equivalence control is shown to be self-optimizing; that is, the family of average costs converges to the optimal ergodic cost. Some examples of stochastic parabolic problems with boundary control and a structurally damped plate with random loading and point control are described that satisfy the assumptions for the adaptive control problem solved in this paper.

An Approach to Adaptive Boundary Control of Linear Stochastic Distributed Parameter Systems

The linear distributed parameter system for the adaptive control problem is modelled by an evolution equation with an infinitesimal generator for an analytic semi group and the noise in the system is a cylindrical white noise. The unknown parameters appear affinely in both the infinitesimal generator of the semigroup and the unbounded linear transformation of the control. A family of least squares estimates of these parameters is shown to be strongly consistent. The certainty equivalence adaptive control for an ergodic quadratic cost functional is shown to be self-tuning and self-optimizing.

Control theory methods for consistency in some least squares identification problems

The consistency of a family of least-squares estimates of some unknown parameters of a continuous-time linear stochastic system is verified. The unknown parameters appear affinely in the linear transformations of the state and the control. The method of verification of consistency associates a family of control problems to the identification problem, and the asymptotic behavior of the solutions of a family of algebraic Riccati equations from the control problems implies a persistent excitation property for the identification problem. >

Adaptive control of linear stochastic evolution systems

An adaptive control problem for some linear stochastic evolution systems in Hilbert spaces is formulated and solved in this paper. The solution includes showing the strong consistency of a family of least squares estimates of the unknown parameters and the convergence of the average quadratic costs with a control based on these estimates to the optimal average cost. The unknown parameters in the model appear affinely in the infinitesimal generator of the C 0 semigroup that defines the evolution system. A recursive equation is given for a family of least squares estimates and the bounded linear operator solution of the stationary Riccati equation is shown to be a continuous function of the unknown parameters in the uniform operator topology