This paper presents a new control scheme for a class of nonlinear systems. In the proposed method, an adaptive neural network observer with Rise feedback controller are applied to realize sensorless control scheme. This observer is tuned online and no exact information of nonlinear term of plant is required. So, this characteristic can compensate mismodeling phenomena. Also, a new controller called Robust Integral of the Sign of the Error (Rise) is considered to realize control purpose. This controller is inspired from 2nd order sliding mode while it can control system with different relative degree. Also, its chattering is acceptable in comparison with sliding mode strategy. This observer based control scheme is considered for modified Duffing chaotic system. The modified Duffing system is derived from Metamorphic shape-changing Underwater autonomous vehicle (MUV). The chaotic behavior of modified Duffing system has a negative impact on MUV performance. Therefore, the controlling of this system can be important. In order to assess the performance of the proposed method, this strategy is compared with observer-based sliding mode control. The comparison results confirm the advantages of proposed method. Manuscript profile

This paper presents a new observer-based control scheme for a class of nonlinear systems. In the proposed method, nonlinear observer and twisting algorithm controller are employed to realize a sensor-less control strategy for complex systems which makes use of non measurable process information instead of installing as many sensors as possible. Due to lack of availability of the complex system states, controlling of them will be faced with undesirable performance. This deficiency can be solved with adding observer in the control strategy. In order to estimate unavailable states, an adaptive neural network observer is considered in the present article. This observer is tuned online and no exact information of the nonlinear function in the observed system is required. Also, to realize control purpose, 2nd order sliding mode controller called twisting algorithm is located in the close loop structure. This control strategy is implemented on the modified Duffing chaotic system and the simulation results confirm the capability of this method. Manuscript profile

This paper addresses a nonlinear observer based control scheme to synchronize chaotic systems subject to uncertainties and external disturbances. It is assumed that the dynamic of slave system is not completely known. In order to compensate for the system perturbation resulting from parameter variations and mismodeling phenomena, an adaptive neural network observer is employed to handle this problem. A nonlinear observer for a class of nonlinear systems is proposed based on a generalized dynamic recurrent neural network. The weights of the proposed neural network in the observer are tuned on-line with no off-line learning phase required. Also, no exact information of the nonlinear term of the system is required and this important characteristic compensates considerable part of uncertainty. To realize control purpose, two controllers are considered. At first, PID controller is combined with proposed observer and then 2nd order sliding mode controller called twisting algorithm is applied to synchronize systems. This method is implemented on the Duffing chaotic systems and simulation results confirm the effectiveness of the proposed method. Manuscript profile