Adaptive dynamic output feedback neural network control of uncertain MIMO nonlinear systems with prescribed...

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Robotics

Background:

• Designing controllers for uncertain nonlinear systems is challenging.
• Multi-input/multi-output (MIMO) systems require sophisticated control strategies.
• Ensuring both performance and stability is crucial in dynamic systems.

Purpose of the Study:

• To design an adaptive dynamic output feedback neural network controller.
• To guarantee prescribed performance bounds and signal boundedness for uncertain nonlinear systems.
• To address challenges in controlling complex, uncertain MIMO systems.

Main Methods:

• Development of an adaptive dynamic output feedback neural network controller.
• Utilizing a switching-type controller with guaranteed continuity.
• Proving necessary and sufficient conditions for boundedness of augmented closed-loop system states.

Main Results:

• The designed controller guarantees prescribed performance bounds on system output.
• All closed-loop signals are proven to be bounded.
• Continuity of the switching controller ensures existence and uniqueness of solutions.

Conclusions:

• The proposed controller effectively manages uncertain nonlinear MIMO systems.
• The approach simplifies control design by focusing on boundedness properties.
• Simulations demonstrate the controller's efficacy on a robotic manipulator.