An adaptive discrete-time tracking controller for a direct current motor with controlled excitation flux is presented. A recurrent neural network is used to identify the plant model; this neural identifier is trained with an extended Kalman filter algorithm. Then, the discrete-time block-control and sliding-mode techniques are used to develop the trajectory tracking. This paper also includes the respective stability analysis for the whole closed-loop system. The effectiveness of the proposed control scheme is verified via real-time implementation.

In this article, the dynamics of a double-acting pneumatic actuator was experimentally studied. To understand the details of dynamics and provide experimental data for further modeling and numerical simulation of actuators, a special test bench was designed. The bench includes a high-resolution linear encoder as well as a dSPACE 1104 data acquisition board, that permits high-frequency sampling to identify important features in the measured data at different source pressures; it also incorporates two pressure sensors, a solenoid valve, a push button, and a measuring interface to automate the acquisition of data. High precision measurements of the time of displacement and position of the moving elements, piston rod or cylinder body, allowed us to calculate velocity and acceleration. We found that the velocity of the moving element is near constant in a major part of the stroke. The recording of the dynamics of upstream and downstream pressures in the pneumatic cylinder chambers at different source pressure levels permitted us to calculate the force exerting by the piston and to explain the kinematics of the moving element. The matching of the upstream and downstream pressure plots with the displacement of the moving element allowed us to establish the instant when the moving starts, and this way evaluates such an important feature as the static friction force between the piston and cylinder body. Various scenarios were used in the experiments, including the fixing of the piston rod, which allow the cylinder body to move or vice versa, the horizontal or vertical position of the pneumatic cylinder and the forward or backward stroke movement. The results obtained with the proposed experimental bench provide essential information on the dynamics of a double-acting pneumatic cylinder that can be included in the mathematical model of the cylinder and used in mobile robotics.

On this paper, a robust velocity controller applied to a three-phase squirrel-cage induction motor under variable load conditions is designed. The induction motor drives an induction generator representing the load which freely delivers the generated power to the utility grid. The closed-loop scheme is designed at αβ coordinate frame and is based on the linearization block control technique in combination with the super-twisting algorithm. The controlled output variables are the angular mechanical velocity and the square modulus of rotor flux linkages. The motor reference velocity is set up by a pulse train above the synchronous velocity and, consequently, the impelled induction machine operates in generator mode; meanwhile, the reference of rotor flux square modulus varies according to load condition of the induction motor. In order to estimate the non-measurable variables, both a rotor flux linkages observer and a load torque observer are designed. For the first one, a non-linear state observer using first order sliding modes is applied at αβ coordinate frame and, for the second one, a Luenberger reduced asymptotic observer is used. The validation of the robustness for the proposed velocity controller is performed in a real-time experiment using a work-bench.

In this work, a synchronization scheme for networks of complex systems is presented. The proposed synchronization scheme uses a control law obtained with some definitions from graph theory and solving the Model-Matching Problem for complex networks. In particular, Rössler, Chen, Lorenz and Lü chaotic systems are used as complex chaotic systems into complex networks. Particular cases with regular and irregular networks of six identical chaotic systems are implemented, with some well-known topologies as star and ring small-world, and tree topologies. Highlighting, the obtained control law is applied to synchronize an irregular network of six different chaotic systems in a tree topology. The usefulness and advantages of the proposed synchronization scheme are highlighted performing numerical simulations of the chaotic complex networks.