Direct Biologically-Inspired Sliding-Mode Control of a Rotorcraft System

In the context of nonlinearity and uncertainty in the control systems, this work presents a novel control strategy based on a biologically-inspired learning robust controller for the attitude stabilization of a Vertical-Take-Off and Landing (VTOL) rotorcraft. Initially, the attitude's VTOL model based on quaternion formalism is detailed to subsequently develop the corresponding control law. Such controller consists of a Brain Emotional Learning-Based Intelligent Control (BELBIC) mechanism, combined with a Super-Twisting Sliding-Mode Control (STSMC) law. By using this architecture, the nonlinear sliding-mode equivalent control term is estimated via the BELBIC model, which allows the controller not to be fully dependent on the system model. Furthermore, closed loop stability is verified through Lyapunov's theory. Finally, for validation and assessment purposes of the overall control strategy, an extensive numerical simulation stage is conducted.