In this architecture, the performance-based adaptive control law designates candidate controllers based on performance considerations, while the SO supervises this selection based upon online robust stability considerations.
The second contribution is the use of control theoretic techniques (PI controllers based on the Root Locus method) to model and design the feedback loops with desired dynamic performance.
In the robotic field there are classical solutions for constrained tasks with hard constraints which are often based on hybrid control [3] or impedance control [4] using central controllers based on global knowledge of direct and inverse kinematics or dynamics.
First we provide a general method to design degree 0 homogeneous controllers based on a homogeneous Control Lyapunov Function (CLF).
The proposed controllers, based on sliding mode control, have been shown to achieve state (hence output) boundedness.
The designed controllers based on the hierarchical control scheme can be divided into rotational controller and translational controller and their stability are validated by the Lyapunov stability theorem.
Here, we propose a method for reconstructing robust transcriptional controllers based on the structure of the controlled system instead.
These new control strategies are compared to other existing controllers based on tests upon an aeroelastic wind turbine simulator.
The transfer function obtained was then used to implement two components of the above mentioned concepts, namely controllers, based on the minimum variance criterion, and statistical control charts.
This control strategy is compared to PID (proportional-integral-derivative) and fuzzy controllers based on the TSR (Tip Speed Ratio) method.
Going even deeper, one can try to create new muscle-like actuators and controllers based on neural networks in an attempt to accurately copy biological function and control.
