This paper presents an application of recently proposed robust integral of the sign of the error (RISE) feedback control scheme for a three degrees-of-freedom (DOF) robot manipulator tracking problem. This method compensates for nonlinear disturbances and uncertainties in the dynamic model, and results in asymptotic trajectory tracking. To avoid selecting parameters of the RISE controller by time-consuming trial and error method, particle swarm optimization (PSO) algorithm is employed. The objective of the PSO algorithm is to find a set of parameters that minimizes the mean of root squared error as the fitness function. The proposed method attains tracking goal, without any chattering in control input. Indeed, the existence of a unique integral sign term in the RISE controller avoids the occurrence of chattering phenomenon that usually happens in sliding mode controllers. Numerical simulations demonstrate the effectiveness of the proposed control scheme.

In this paper a new RISE controller is gained to control chaos in a tracking task. The technique copes with the chattering phenomenon whilst works for different classes of nonlinear systems incorporating different relative degrees. This control strategy will be primarily implemented on a Duffing chaotic system. In order to assess performance of the controller, the technique will be implemented on a more complex system, so called Genesio-Tesi dynamic. The result will be finally compared with an optimal controller. The capability of the proposed feedback technique to control the chaos is verified through simulation study with respect to similar classic approaches.

Chaos control is an important subject in control theory. Chaos control usually confronts with some problems due to unavailability of states or losing the system characteristics during the modeling process. In this situation, using an appropriate observer in control strategy may overcome the problem. In this paper, states are estimated using an observer without having complete prior information from nonlinear term based on neural network. Simulation results verify performance of the proposed structure in estimating nonlinear term specifically for an online practical use.