Robust Data-Driven Passivity-Based Control of Underactuated Systems via Neural Approximators and Bayesian Inference

Keyphrases

• Controller 100%
• Bayesian Inference 100%
• Control of Underactuated Systems 100%
• Neural Approximators 100%
• Passivity-based Control 100%
• Neural Network 50%
• Model Uncertainty 50%
• Model Measurement 50%
• Probability Distribution 50%
• Control Law 50%
• Data-driven Approach 50%
• Robustness Improvement 50%
• Lyapunov Function 50%
• Swing-up 50%
• Neural Nets 50%
• Real System 50%
• Control Task 50%
• Cart-pole 50%
• Bayesian Approach 50%
• Parametrized 50%
• Noise Measurement 50%
• Improvement Methods 50%
• Controller Robustness 50%
• Simple Pendulum 50%
• Pendulum System 50%
• Underactuated Robotic System 50%
• Passivity Theory 50%
• Gradient-based 50%

Engineering

• Approximators 100%
• Underactuated System 100%
• Robotic System 100%
• Model Uncertainty 100%
• Real System 100%
• Measurement Noise 100%
• Control Law 100%
• Bayesian Approach 100%
• Simple Pendulum 100%
• Pendulum System 100%
• Lyapunov Function 100%
• Neural Net 100%

Computer Science

• Neural Network 100%
• Robotics 50%
• Driven Approach 50%
• Model Uncertainty 50%
• Bayesian Approach 50%
• Probability 50%
• Measurement Noise 50%
• Lyapunov Function 50%