The position/force tracking control of Lagrangian mechanical systems with classical nonholonomic constraints is addressed in this paper. The main feature of this paper is that 1) control strategy is developed at the dynamic level and can deal with model uncertainties in the mechanical systems; 2) the proposed control law ensures the desired trajectory tracking of the configuration state of the ...

This paper presents the basic principle, design and simulation of a LQG optimal controller for a mobile cart. The optimal control is a significant branch of control theory, which combines the statistical process theory with the optimal control theory to solve many control problems in uncertain systems like astronavigation, satellite attitude and orbit, civil purpose and industrial processes. In...

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...

In this paper a class of semilinear elliptic optimal control problem with pointwise state and control constraints is studied. We show that sufficient second order optimality conditions for regularized problems with small regularization parameter can be obtained from a second order sufficient condition assumed for the unregularized problem. Moreover, error estimates with respect to the regulariz...

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...

We develop the method of stochastic modified equations (SME), in which stochastic gradient algorithms are approximated in the weak sense by continuous-time stochastic differential equations. We exploit the continuous formulation together with optimal control theory to derive novel adaptive hyper-parameter adjustment policies. Our algorithms have competitive performance with the added benefit of...

An analytical investigation of a half-car model with passenger dynamics, subjected to random road disturbance, is performed. Two diierent methods of deening the performance index for optimal controller design are proposed. Nondeterministic inputs are applied to simulate the road surface conditions more realistically. Results obtained illustrate that using an optimal state-feedback controller, w...

The controllability theory may at times lead to a control law as can be seen in the control theory literature. In this paper, a global control algorithm is proposed for nonlinear systems in the so-called linear-analytic form in the plane. The algorithm is motivated by a theorem by Hunt, which gives sufficient conditions for the global controllability of linearanalytic systems on a 2-dimensional...

In this contribution we discuss central and autonomous control of production networks. We provide a number of parameters that influence the comparative effectiveness of these two methods. Further, the results concerning stability of the production networks are discussed and it is shown, how the central planning methods, implemented into mathematical models with the help of optimal control theor...

Although adaptive control theory offers mathematical tools to achieve required system performance without excessive dependence on modeling, its application to safety-critical systems can be limited by the presence of unmodeled dynamics. This paper examines the robustness to unmodeled dynamics of a recently developedderivativefree adaptive control law. In contrast to derivative-based adaptive la...