Víctor Santibáñez

Position and motion control of robotic and mechanical systems have proven to be challenging tasks, primarily because of their multidimensional, coupled and highly nonlinear dynamics

In this work, a robust adaptive neural network controller is proposed for tracking control of the displacement and orientation, while keeping the pendulum in the upright position, of a two-wheeled inverted pendulum system. The proposed approach is designed to be compatible with commercial robotic platforms, which typically employ velocity control signals rather than torque or voltage inputs and provide limited access to inner control layers. Accordingly, the proposed control architecture consists of an inaccessible embedded velocity-control loop, which operates as a black-box subsystem, and an outer adaptive controller that generates the reference velocity commands for the embedded system. To this end, an adaptive neural network controller is designed to achieve the control objectives using acceleration signals, which are subsequently integrated before being applied to the system.Finally, in addition to the stability proof, the effectiveness of the proposed controller is validated through real-time experiments on the RAB-ITLAG-2 platform and compared with a model based, proportional derivative and neuro-fuzzy controllers. The numerical results, including the root mean square, standard deviation, and peak values of the tracking errors, demonstrate the superior performance of the proposed method in the presence of model uncertainties and external disturbances.

In this work, a velocity tracking control strategy is presented using passivity methodology for a permanent magnet synchronous machine with uniformly distributed windings, which is known as a brushless direct current (BLDC) motor. The trapezoidal components of back electromotive force (EMF) are here modeled by using a novel differentiable approximation. The proposed control scheme is constructed by employing only measurements of the shaft position and stator currents. The use of a second-order filter makes it possible to solve the velocity tracking control problem without measuring the actual rotor velocity. A formal stability analysis of the closed-loop system consisting of the motor, the controller, and the second-order filter is presented using partial stability theory. By applying partial stability, the relevant variables are shown to converge, while the behavior of the rest of the variables that are not relevant to the control objective can be disregarded, which makes the theoretical analysis simpler. A numerical simulation is constructed to verify the applicability of the control law. The results show that all the variables of the BLDC motor are below the nominal values of the machine. Therefore, the physical implementation of the controller is feasible.

In this paper, an adaptive control scheme composed of an estimated feed-forward compensation and a PD control law with three mutually independent estimators is proposed for the tracking of desired trajectories in joint space for a robotic arm. One of the estimators is used to identify inertial and geometrical parameters, while the others determine the two principal components of the friction phenomenon: the part whose magnitude is position-dependent but velocity-independent and the part whose magnitude is proportional to velocity. Next, the persistently exciting condition is satisfied for each regression matrix of the estimators in a way that is easier to prove than the classical structure. Then, uniform global asymptotic stability can be concluded for the tracking error, regardless of parametric convergence, by applying the direct Lyapunov theorem. This scheme has been applied experimentally for a robotic arm to verify the theoretical results. The experimental results yielded a better performance in both estimating the parameters and tracking, with a much simpler overall analysis than the alternatives consulted.

A novel trajectory tracking control approach for robot manipulators that uses adaptive neural network feedforward compensation plus a sectorial fuzzy controller is presented. We conduct simulation and real-time experiments comparing it with two previously published control schemes: a Proportional–Derivative (PD) plus feedforward compensation controller model, and a sectorial fuzzy control plus feedforward compensation model. The proposed controller shows a faster transient response and better steady-state angular error performance than its counterparts, and it maintains its tolerance to parameter deviation, a main characteristic of fuzzy controllers; furthermore, it excludes the need for knowledge of the robot manipulator model to achieve excellent results. A formal stability analysis of the proposed controller in a closed loop with the robot manipulator guarantees that position and velocity errors converge to zero and all signals are uniformly bounded.