No abstract

A robust nonlinear controller based on an improved feedback linearization technique with state observer in presence of uncertainties and external disturbances is developed for a class of nonlinear systems. First, by combining classical feedback linearization approach with a robust control term and a fuzzy logic system, we design and study an efficient controller for such systems. Second, we propose an optimized extended Kalman filter for the observation of the states. The parameters to be optimized are the covariance matrices Q and G, which play an important role in the extended Kalman filter performances. This optimization is insured by the particle swarm optimization algorithm. The Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is simulated on a nonlinear inverted-pendulum system. Simulation results demonstrate the robustness and effectiveness of the proposed scheme and exhibit a more superior performance than its conventional counterpart.

A novel type2-fuzzy adaptive filter is presented, which uses the concepts of type2-fuzzy logic, for electrocardiogram signals denoising. Type2-fuzzy adaptive filter is an information processor where both numerical and linguistic information are used as input-output pairs and fuzzy if-then rules, respectively. The proposed approach is based on an iterative procedure to achieve acceptable information extraction in the case where the statistical characteristics of the input-output signals are unknown. The proposed filter is presented as a dual-layered feedback system. Each layer has different function, the first layer being the type2-fuzzy autoregressive filter model. The second layer being responsible for training the membership function parameters. The second layer adjusts the type2-fuzzy adaptive filter parameters by using a teaching learning-based optimization algorithm (TLBO), which will allow the reaching of the required signal reconstruction by decreasing the criterion function. The proposed filter is validated and evaluated through some experimentations using the MIT-BIH ECGs databases where various artifacts were added to the ECGs signals; these included real and artificial noise. For comparison purposes, both model and non-modelbased methods recently published are used. Furthermore, the effect of the proposed filter on the malformation of diagnostic features of the ECG was studied and compared with several benchmark schemes. The results show that the proposed method performs better denoising for all noise power levels and for a different criteria viewpoint.

No abstract

No abstract