A state observer is a system that models a real system in order to provide an estimate of the internal state of the system. The design techniques and comparison of four different types of state observers are presented in this paper. The considered observers include Luenberger observer, Kalman observer, unknown input observer and sliding mode observer. The application of these observers to a Multiple Input Multiple Output (MIMO) DC servo motor model and the performance of observers is assessed. In order to evaluate the effectiveness of these schemes, the simulated results on the position of DC servo motor in terms of residuals including white noise disturbance and additive faults are compared.

This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.

This paper presents a new efficient approach to Economic Dispatch (ED) problems with smooth and non smooth cost functions using a Particle Swarm Optimization (PSO) technique. The practical ED problems have nonsmooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult using any mathematical approaches. In this paper a Modified random search technique is employed so that fastest convergence is obtained for the same results published in IEEE Proceedings so far. To show its efficiency and effectiveness, the proposed EPSO is applied to test ED problems, one with smooth cost functions and others with nonsmooth cost Functions considering valve-point loading effects. The experimental results show that the proposed EPSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.