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People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.

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In this paper, a parameter estimation for a class of second order nonlinear system is presented. The considered system, can be represented in such way that it is linear respect its parameters. Since the system output is given, only, for the second integrator a state estimation of unmeasured state variables is reconstructed via nonlinear observer based on the terminal sliding mode observer. Therefore, the main contribution of this paper deals with a full order nonlinear observer algorithm design to enhance parameter identification of a nonlinear second order system. Finally, to illustrate the theoretical performance of the proposed identification algorithm, an experimental result of a mechanical system is presented.

SUMMARYThe optimal control for a temperature system with time delay is considered. Experimental results of the control system are presented in this contribution. The integral term in the controller is approximated by a quadrature method. Experimental results obtained demonstrate the effectiveness of the approximation method. By a simple analysis in time domain, we demonstrate the robustness of the optimal controller. We compare the optimal control's performance with an industrial PID controller. This controller was robustly tuned. The experiments indicate the correct optimization of the plant when the optimal control was employed, despite limitations in the sensor, actuators, non-modeled dynamics, and uncertain parameters of the process.

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