Comparison of Model Predictive Control and Proximal Policy Optimization for a 1-DOF Helicopter System
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Abstract (arXiv)
This study conducts a comparative analysis of Model Predictive Control (MPC) and Proximal Policy Optimization (PPO), a Deep Reinforcement Learning (DRL) algorithm, applied to a 1-Degree of Freedom (DOF) Quanser Aero 2 system. Classical control techniques such as MPC and Linear Quadratic Regulator (LQR) are widely used due to their theoretical foundation and practical effectiveness. However, with advancements in computational techniques and machine learning, DRL approaches like PPO have gained traction in solving optimal control problems through environment interaction. This paper systematically evaluates the dynamic response characteristics of PPO and MPC, comparing their performance, computational resource consumption, and implementation complexity. Experimental results show that while LQR achieves the best steady-state accuracy, PPO excels in rise-time and adaptability, making it a promising approach for applications requiring rapid response and adaptability. Additionally, we have established a baseline for future RL-related research on this specific testbed. We also discuss the strengths and limitations of each control strategy, providing recommendations for selecting appropriate controllers for real-world scenarios.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 5 of its 28 references that are in the map. First posted on arXiv 28 Aug 2024.
Builds on
5 of 28 references.
References · 5
- 2017Proximal Policy Optimization Algorithmscited by 14,441
- 2017Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Controlcited by 50
- 2019Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Controlcited by 19
- 2024Python-Based Reinforcement Learning on Simulink Modelscited by 8
- 2020Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardwarecited by 5
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Citing papers per year: 2025: 2; 2026: 4.
6 papers.
- 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learningcited by 6
- 2025Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Controlcited by 2
- 2026Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Taskscited by 1
- 2026Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter Systemcited by 0
- 2026Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimizationcited by 0
- 2026Safe Reinforcement Learning using Ideas from Model Predictive Controlcited by 0
- 2026Safe Reinforcement Learning using Ideas from Model Predictive Control8 Jul 2026
- 2026Sample-Efficient Pareto Front Modeling for Energy-Aware Reinforcement Learning Using Bayesian Optimization3 Jul 2026
- 2026Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System3 Jul 2026
- 2026Chebyshev Policies and the Mountain Car Problem: Reinforcement Learning for Low-Dimensional Control Tasks21 May 2026
- 2025Multi-Objective Reinforcement Learning for Energy-Efficient Industrial Control12 May 2025
- 2025The Crucial Role of Problem Formulation in Real-World Reinforcement Learning26 Mar 2025