advanced MIMO control systems.
Control systems can vary from simple linear single input single output (SISO) systems to complex non-linear multi-input multi output (MIMO) systems. While simple systems can easily be controlled by the application of PID controllers, nonlinear MIMO systems offer a greater challenge.
- you can place a few highlights here with bullets
- like this and this
- or rename this left section with another topic
Revolutionizing control systems with reinforcement learning.
Traditionally, a lot of effort is put into decoupling inputs and linearizing these nonlinear MIMO systems in order to make a well-functioning control system. However, reinforcement learning offers a different approach where the algorithm learns the optimal behavior by itself. The challenge is to get high performance in terms of stability and or reaction speed in these non-linear MIMO control systems.
We can use reinforcement learning techniques to (1) model the entire nonlinear MIMO control system when this system is too complex and non-linearizable or (2) tune settings in the traditional control system to squeeze out the last bit of performance.