Sliding mode control is a robust technique that is used to overcome difficulties such as parameter variations, unmodeled dynamics, external disturbances, and payload changes in the position-tracking problem regarding robots. However, the selection of the gains in the controller could produce bigger forces than are required to move the robots, which requires spending a large amount of energy. In the literature, several approaches were used to manage these features, but some proposals are complex and require tuning the gains. In this work, a sliding mode controller was designed and optimized in order to save energy in the position-tracking problem of a two-degree-of-freedom SCARA robot. The sliding mode controller gains were optimized usinga Bat algorithm to save energy by minimizing the forces. Finally, two controllers were designed and implemented in the simulation, and as a result, adequate controller gains were found that saved energy by minimizing the forces.

In this study, a modified linear technique is proposed for the controllability and observability of robotic arms, the modified linear technique consists of the following steps: a transformation is used to rewrite a nonlinear time-variant model as a linear time-variant model, this linear time-variant model is evaluated at origin to obtain a linear time-invariant model, and the rank condition tests the controllability and observability of the linear time-invariant model. The modified linear technique is better than the linearization technique because the modified linear technique does not use the Jacobian approximation, while the linearization technique needs the Jacobian approximation. The modified linear technique is better than the linear technique because the modified linear technique can be applied to robotic arms with rotational and prismatic joints, while the linear technique can only be applied to robotic arms with rotational joints.

In this note, the problem of tracking random references and rejecting random perturbations in a quadrotor, both generated by an auxiliary system named exosystem, is solved by extending the deterministic tracking problem to the area of stochastic processes. Besides, it is considered that only a part of the state vector of the quadrotor is available through measurements. As a consequence, the state vector of the plant must be estimated in order to close the control loop. On this basis, a controller to track random references and to reject random perturbations is developed by combining a Kalman filter to estimate the references and perturbations of an exosystem and an observer to estimate the states of a quadrotor. Besides, to obtain a more practical controller, the analysis is carried out in discrete time. Numerical simulations are used in a quadrotor to confirm the validity and effectiveness of the proposed control.