When models are inaccurate, the performance of model-based control will degrade. For linear quadratic control, an event-triggered learning framework is proposed that automatically detects inaccurate and triggers a new process model when needed. This achieved by analyzing probability distribution cost designing trigger leverages Chernoff bounds. In particular, whenever empirically observed signa...

A control problem for a linear stochastic system with an arbitrary fractional Brownian motion and a cost functional that is quadratic in the system state and the control is solved. An optimal control is given explicitly as the sum of a prediction of the future fractional Brownian motion and the well known linear feedback control for the associated deterministic linear-quadratic control problem....

Self-triggered control is a recently proposed paradigm that abandons the more traditional periodic timetriggered execution of control tasks with the objective of reducing the utilization of communication resources, while still guaranteeing desirable closed-loop behavior. In this paper, we introduce a selftriggered strategy based on performance levels described by a quadratic discounted cost. Th...

The use of piecewise quadratic cost functions is extended from stability analysis of piecewise linear systems to performance analysis and optimal control. Lower bounds on the optimal control cost are obtained by semi-definite programming based on the Bellman inequality. This also gives an approximation to the optimal control law. An upper bound to the optimal cost is obtained by another convex ...

In this paper we investigate the finite horizon linear quadratic regulation (LQR) problem for a linear continuous time system with time-varying delay in control input and a quadratic criterion. We assume that the time-varying delay is of a known upper bound, then the LQR problem is transformed into the optimal control problem for systems with multiple input channels, each of which has single co...