Bilayer of nickel and nickel oxide were deposited on glass substrates using RF magnetron sputtering technique. The magnetic properties of the prepared thin films were carried out at room temperature in both parallel and perpendicular magnetic field to the sample. The Preisach model was applied to provide a mathematical model of the magnetic hysteresis loop in the case of parallel geometry, along the easy axis of the bi-layer NiO / Ni. Good agreement was obtained between the theoretical and experimental results.

In this paper, an adaptive tracking controller for a piezo-actuated stage is designed considering the LUGRE model. The adaptation algorithm of the parameterized hysteretic function is used to achieve a displacement-tracking objective under the mechanical parameters uncertainties. The Lyapunov theory is used to derive an adaptive law for the system stability. The effectiveness of the proposed controller is validated considering real-time simulation. The validation results of the proposed controller presents good performances and robustness under an external load-disturbance and parameter uncertainties.

In this paper, an adaptive tracking controller for a piezo-actuated stage is designed considering the LUGRE model. The adaptation algorithm of the parameterized hysteretic function is used to achieve a displacement-tracking objective under the mechanical parameters uncertainties. The Lyapunov theory is used to derive an adaptive law for the system stability. The effectiveness of the proposed controller is validated considering real-time simulation. The validation results of the proposed controller presents good performances and robustness under an external load-disturbance and parameter uncertainties.

In this paper, an adaptive tracking controller for a piezo-actuated stage is designed considering the LUGRE model. The adaptation algorithm of the parameterized hysteretic function is used to achieve a displacement-tracking objective under the mechanical parameters uncertainties. The Lyapunov theory is used to derive an adaptive law for the system stability. The effectiveness of the proposed controller is validated considering real-time simulation. The validation results of the proposed controller presents good performances and robustness under an external load-disturbance and parameter uncertainties.

In this paper, an adaptive tracking controller for a piezo-actuated stage is designed considering the LUGRE model. The adaptation algorithm of the parameterized hysteretic function is used to achieve a displacement-tracking objective under the mechanical parameters uncertainties. The Lyapunov theory is used to derive an adaptive law for the system stability. The effectiveness of the proposed controller is validated considering real-time simulation. The validation results of the proposed controller presents good performances and robustness under an external load-disturbance and parameter uncertainties.

A robust nonlinear controller based on an improved feedback linearization technique with state observer is developed for a class of nonlinear systems with uncertainties and external disturbances. First, by combining classical feedback linearization approach with a discontinuous control and a fuzzy logic system, we design and study a robust controller for uncertain nonlinear systems. Second, we propose an optimized extended Kalman filter (EKF) for the observation of the states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the EKF performances. The particle swarm optimization algorithm insures this optimization. Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is applied on a two-link robot system under Matlab environment. Simulation results have confirmed the effectiveness of the proposed approach against uncertainties and external disturbances; and exhibited a more superior performance than the non-improved control actions.

A robust nonlinear controller based on an improved feedback linearization technique with state observer is developed for a class of nonlinear systems with uncertainties and external disturbances. First, by combining classical feedback linearization approach with a discontinuous control and a fuzzy logic system, we design and study a robust controller for uncertain nonlinear systems. Second, we propose an optimized extended Kalman filter (EKF) for the observation of the states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the EKF performances. The particle swarm optimization algorithm insures this optimization. Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is applied on a two-link robot system under Matlab environment. Simulation results have confirmed the effectiveness of the proposed approach against uncertainties and external disturbances; and exhibited a more superior performance than the non-improved control actions.

A robust nonlinear controller based on an improved feedback linearization technique with state observer is developed for a class of nonlinear systems with uncertainties and external disturbances. First, by combining classical feedback linearization approach with a discontinuous control and a fuzzy logic system, we design and study a robust controller for uncertain nonlinear systems. Second, we propose an optimized extended Kalman filter (EKF) for the observation of the states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the EKF performances. The particle swarm optimization algorithm insures this optimization. Lyapunov synthesis approach is used to prove the stability of the whole control loop. The proposed approach is applied on a two-link robot system under Matlab environment. Simulation results have confirmed the effectiveness of the proposed approach against uncertainties and external disturbances; and exhibited a more superior performance than the non-improved control actions.