Sunday 02 February 2025
The Lucas-Nulle inverted pendulum is a classic problem in control theory, where a cart and pole system must be stabilized using a controller that can balance the pole’s movement while keeping the cart within a certain range. In this latest breakthrough, researchers have developed an innovative approach to solve this complex task using reinforcement learning (RL), a type of artificial intelligence.
The RL algorithm is designed to learn from experience by trial and error, making adjustments as it goes along. The system consists of two main components: an actor network that generates control signals for the cart and pole, and a critic network that evaluates the performance of these actions. Through repeated interactions with the environment, the actor learns to adapt its behavior to achieve optimal results.
One of the key challenges in solving this problem is dealing with the lack of state information available from sensors. The researchers tackled this by developing a novel digital phase-locked loop (PLL) algorithm that estimates the linear and angular speeds of the pole without requiring any knowledge of the system’s parameters. This approach allows the controller to operate independently, without relying on external feedback.
The experiment was conducted using an educational hardware device from Lucas-Nulle, which provides a real-world test bed for the RL controller. The results show that the controller is able to successfully stabilize the pendulum in the upper equilibrium position, even with random initial conditions and without any prior knowledge of the system’s dynamics.
The significance of this work lies not only in its technical achievement but also in its potential applications. The developed RL algorithm can be applied to a wide range of control problems that involve complex systems with limited sensor information. This breakthrough has far-reaching implications for the development of autonomous systems, robotics, and other fields where precise control is crucial.
In summary, this innovative approach combines reinforcement learning with digital phase-locked loops to solve the classic Lucas-Nulle inverted pendulum problem. The results demonstrate the potential of RL in solving complex control problems, paving the way for further research and applications in various domains.
Cite this article: “Solving the Lucas-Nulle Inverted Pendulum Problem with Reinforcement Learning”, The Science Archive, 2025.
Reinforcement Learning, Lucas-Nulle Inverted Pendulum, Control Theory, Artificial Intelligence, Digital Phase-Locked Loop, Autonomous Systems, Robotics, Complex Systems, Sensor Information, Optimization.
