Miguel A. Llama

Thanks to recent advances in artificial intelligence, interest in autonomous mobile systems has increased, and consequently, the development and validation of advanced control schemes for them has also seen a rise. This work introduces a two-layer neuro-adaptive compensation control scheme designed to address the trajectory tracking problem for an omnidirectional wheeled mobile robot equipped with four independent Mecanum wheels. The two-layer artificial neural network is used to compensate for the unknown dynamics of the mobile robot; the filtered error technique is used to obtain the weights of the artificial neural network. This approach does not require offline training. A key contribution of this approach is the integration of a novel auxiliary signal to provide robustness, particularly in non-ideal scenarios. This robust term effectively bounds the disturbance commonly encountered in such control approaches. A significant advantage of this approach is its independence from precise knowledge of plant parameters or the overall plant dynamics. Experimental results demonstrate the effectiveness of the proposed controller in achieving desired performance for the 4-wheeled omnidirectional mobile robot.

Numerous academic works have addressed the identification and control problem for complex dynamic systems. In recent decades, the use of control algorithms based on neural networks (NNs) has been highlighted, which have shown satisfactory results in the trajectory tracking control for a class of discrete-time nonlinear systems. The present work proposes an efficient learning law for discrete-time recurrent high order neural networks (RHONNs), using a training algorithm based on an unscented Kalman filter (UKF). This learning law is applied through a decentralized neural block control using UKF (DNBC-UKF), for trajectory tracking control of a class of discrete-time nonlinear systems. The proposed controller is experimentally evaluated by means of real-time tests in a two degrees of freedom (DOF) vertical direct-drive robotic manipulator against a decentralized neural block control using EKF (DNBC-EKF). The Lyapunov stability theory is used to show that the identification errors of RHONNs are semi-globally uniformly ultimately bounded (SGUUB), the RHONN weights remains bounded, and the closed-loop tracking errors go to zero.

This paper presents a neuro-adaptive control scheme dedicated to solve the motion trajectory tracking problem of robot manipulators under uncertain parameters and external disturbances. A two degrees of freedom direct-drive robot manipulator was taken as a case of study. The proposed controller is able to guarantee asymptotic convergence of the position and velocity tracking errors, and the weights of the artificial neural network are bounded. Artificial neural network weights are updated online using filtered error approach, adaptive laws and signed power function. This scheme does not require any offline training. The neuro-adaptive controller is experimentally validated and compared with a classical one-layer neuro-adaptive controller; the proposed scheme obtains better quantitative metrics related with the RMS values of the position and velocity errors, and a good robust behaviour against external disturbances.

This article presents a robust output-feedback control scheme for the stabilization of a class of nonlinear underactuated mechanical systems in the presence of coupled disturbances. The scheme is based on the so-called coupled sliding-mode control, i.e., no transformation into the normal form is required. The proposed output approach is composed of a super-twisting controller and a high-order sliding-mode observer that robustly stabilize the origin of the system asymptotically. All of the results are proven by Lyapunov approaches. The proposed approach is validated by means of simulations and experimental results on a four-degree-of-freedom underactuated crane.

In this paper an adaptive fuzzy controller is proposed to solve the trajectory tracking problem of the inverted pendulum on a cart system. The designed algorithm is featured by not using any knowledge of the dynamic model and incorporating a full-state feedback. The stability of the closed-loop system is proven via the Lyapunov theory, and boundedness of the solutions is guaranteed. The proposed controller is heuristically tuned and its performance is tested via simulation and real-time experimentation. For this reason, a tuning method is investigated via evolutionary algorithms: particle swarm optimization, firefly algorithm and differential evolution in order to optimize the performance and verify which technique produces better results. First, a model-based simulation is carried out to improve the parameter tuning of the fuzzy systems, and then the results are transferred to real-time experiments. The optimization procedure is presented as well as the experimental results, which are also discussed.

This paper deals with the design of sliding-mode controllers for the stabilization of a class of underactuated systems. The proposed method takes advantage of the mechanical properties of a class of underactuated systems instead of using nonlinear transformations. In order to design the controller, a series of sliding variables with relative degree one and two are introduced. The theoretical differences between these approaches are discussed and some simulation results show some practical differences.