In this paper a novel approach to automatic model-based control is proposed. Process tomography is used as a sensing device in the control system. The feedback control system is designed to regulate the concentration distribution of a substance in a fluid flow in a pipe.

where c > 0 and the supremum is taken over all admissible controls v satisfying vt 2 [ 0; 1] for all t up to = inf f t > 0 j Xt = 2 (`0; `1) g with 0 < 0 < 1 and `0 < 0 < `1 given and fixed. The following control v is proved to be optimal: ’Pull as hard as possible’ that is v t = 0 if Xt < g (St) , and ’push as hard as possible’ that is v t = 1 if Xt > g (St) , where s 7! g (s) is a switching c...

Applying the Tubular Neighborhood Theorem, we give a short and new proof of the Pontryagin Maximum Principle on a smooth manifold. The idea is as follows. Given a control system on a manifold M , we embed it into an open subset of some Rn, and extend the control system to the open set. Then, we apply the Pontryagin Maximum Principle on Rn to the extended system and project the consequence to M ...

The paper extends quadratic optimal control theory to weakly regular linear systems, a rather broad class of infinite-dimensional systems with unbounded control and observation operators. We assume that the system is stable (in a sense to be defined) and that the associated Popov function is bounded from below. We study the properties of the optimally controlled system, of the optimal cost oper...

This paper proposes a new control method applied to an underwater vehicle equipped with a robot manipulator. This control method is based on force control to stabilize the platform when the manipulator works in free or constrained space. The torque produced by the arm on the platform is estimated with a force sensor installed between the base of the manipulator and the vehicle. This allows corr...

In this paper we propose an iterative learning algorithm based on observer and linear quadratic performance function. We calculate the initial control value for the iteractive learning algorithm based on the estimation of the states, which guarantees the efficient asymptotic tracking of any desired trajectories. Furthermore, with Linear quadratic optimal control theory, we obtain the optimized ...

A new torque control strategy for brushless motors with minimum torque ripple and minimum copper losses while maintaining the phase currents under magnetic (or current) saturation is presented. The optimal control { a nonlinear map from desired torque and position to the motor's phase currents { is the closed form solution to a quadratic programming problem. Experimental results demonstrate vir...

We give an overview of the shooting technique for solving deterministic optimal control problems. This approach allows to reduce locally these problems to a finite dimensional equation. We first recall the basic idea, in the case of unconstrained or control constrained problems, and show the link with second-order optimality conditions and the analysis or discretization errors. Then we focus on...

This paper considers robust reduced order H∞ observer-based control for stochastic systems. The approach is divided into two steps. First, we search for a linear control law to ensure H∞ specification. Then, the obtained linear combination of the state is used to solve an “unbiasedness” (decoupling) condition on the drift part of the closed-loop system. Finally, deterministic functional filteri...

We present a new stabilizing control scheme for linear discrete-time systems with input and state constraints. Essentially, we seek a controller that is able to steer all initial states within a controlled invariant set towards the origin without violating the constraints. The control law builds on a predictive control scheme. We show that a prediction horizon of n steps, where n denotes the di...