چکیده مقاله
This study presents a comparative investigation of Model Predictive Control MPC and Reinforcement Learning RL strategies for the swing up, stabilization, and obstacle avoidance of a double inverted pendulum DIP mounted on a cart The MPC framework employs a direct orthogonal collocation method, incorporating both state tracking and energy based cost functions to ensure efficient convergence under strict input constraints An energy based formulation significantly improves the feasibility of swing up maneuvers while maintaining stability during set point transitions and satisfying obstacle avoidance constraints The RL approach implements the Soft Actor Critic SAC algorithm within a modified OpenAI Gym environment, leveraging entropy regularization for improved exploration While MPC consistently achieves swing up and obstacle avoidance with high accuracy, the RL method effectively stabilizes the pendulum but exhibits limited performance in swing up tasks, indicating the need for task specific reward design and parameter tuning Simulation results highlight the trade offs between model based and model free control in terms of computational cost, robustness, and learning efficiency, offering valuable insights for advanced nonlinear control in robotics and automation
کلیدواژهها
نویسندگان
Masoud Pourghavam
Mechanical Engineering, University of Tehran
شیوه ارجاع
Pourghavam, Masoud,1404,Energy-Based Model Predictive Control and Reinforcement Learning for Double Inverted Pendulum Stabilization and Obstacle Avoidance,The 7th international conference on artificial intelligence and its future prospects in electrical, computer, mechanical and telecommunication engineering sciences,Mashhad
ارائهشده در
مجموعه مقالات هفتمین کنفرانس بین المللی هوش مصنوعی و چشم انداز آینده آن در علوم مهندسی برق ، کامپیوتر ، مکانیک و مخابرات21 مرداد 1404 · مشهد