In this paper, an approach of trajectory tracking is proposed. The approach is based on two controls. Firstly, a quasi sliding mode control is proposed of the angular velocity in aim to converge the angle error to zero in short time with asymptotic stability. Secondly, a global sliding mode control for linear velocity is proposed, in order to bring the position error to zero and ensure the asymptotic stability by using the Lyapunov theory. Finally, the proposed control shows the performance of the algorithm, and the simulation results show good convergence for circular, sinusoidal and specific trajectories.

In this paper, an approach of trajectory tracking is proposed. The approach is based on two controls. Firstly, a quasi sliding mode control is proposed of the angular velocity in aim to converge the angle error to zero in short time with asymptotic stability. Secondly, a global sliding mode control for linear velocity is proposed, in order to bring the position error to zero and ensure the asymptotic stability by using the Lyapunov theory. Finally, the proposed control shows the performance of the algorithm, and the simulation results show good convergence for circular, sinusoidal and specific trajectories.

In this paper, an approach of trajectory tracking is proposed. The approach is based on two controls. Firstly, a quasi sliding mode control is proposed of the angular velocity in aim to converge the angle error to zero in short time with asymptotic stability. Secondly, a global sliding mode control for linear velocity is proposed, in order to bring the position error to zero and ensure the asymptotic stability by using the Lyapunov theory. Finally, the proposed control shows the performance of the algorithm, and the simulation results show good convergence for circular, sinusoidal and specific trajectories.

In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.

In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.

In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.

In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.