This paper proposes a hybrid control scheme for the synchronization of two chaotic Duffing oscillator system, subject to uncertainties and external disturbances. The novelty of this scheme is that the Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined to chaos synchronization with respect to extern...

This paper is a contribution to the theory of the infinitehorizon linear quadratic regulator (LQR) problem subject to inequality constraints on the inputs and states, extending an approach first proposed by Sznaier and Damborg [16]. A solution algorithm is presented, which requires solving a finite number of finite-dimensional positive definite quadratic programs. The constrained LQR outlined d...

A method of using magnetic torque rods to do 3axis spacecraft attitude control has been developed. The goal of this system is to achieve a nadir pointing accuracy on the order of 0.1 to 1.0 deg without the need for thrusters or wheels. The open-loop system is under-actuated because magnetic torque rods cannot torque about the local magnetic field direction. This direction moves in space as the ...

in this paper the active vibration control of a four-story shear frame instrumented with piezoelectric actuators is presented. the piezoelectric actuators are hosted on the columns in two manners and the produced controlling forces by actuators are considered in the equation of motion. the smart structure modeling and control design is carried out using matlab software in state space form. subs...

In previous lectures, we discussed the design of state feedback controllers using using eigenvalue (pole) placement algorithms. For single input systems, given a set of desired eigenvalues, the feedback gain to achieve this is unique (as long as the system is controllable). For multi-input systems, the feedback gain is not unique, so there is additional design freedom. How does one utilize this...

In this paper we describe a possible way to make reinforcement learning more applicable in the context of industrial manufacturing processes. We achieve this by formulating the optimization task in the linear quadratic regulation framework, for which a conventional control theoretic solution exist. By rewriting the Q-learning approach into a linear least squares approximation problem, we can ma...