Quanser inverted pendulum manual

Product info sheet. See it in action. Simulink Courseware. Research Papers. The Linear Motion Control Lab is one of the most popular, flexible and modular solutions for teaching controls. The following additional components are required to complete your workstation, and are sold separately:.

Find a Distributor Stay Connected! Courseware Manuals Whitepapers Videos. Request for Information Request A Demo. There have already been a host of examples showing how RL can be used to balance and swing-up pendulum systems.

While using RL for control system applications has already shown some benefit, it is still in its infancy and will become a more powerful and easy-to-use technique in the next few years. Find a Distributor Stay Connected! Courseware Manuals Whitepapers Videos. Request for Information Request A Demo. Figure 1: Reinforcement learning components in control system applications. Here is a brief description of the components involved: Environment is everything that exists outside the agent.

This includes the plant but also any other effects such as measurement noise, disturbances, filtering, and so on. Policy uses observations and reward signal from the environment and based on these, applies action to the environment.

Different policies are akin to different types of control methods, e. Reinforcement Learning Algorithm: Updates the policy based on the observations and reward signals, e.

Reward is a function used by RL to know when the system is approaching its objective or task, e. It is similar to a cost function used in Linear Quadratic Regulator LQR based control except a reward function tries to maximize the value i. Observations are the set of measured signals e.

Actions are the set of the control signals e. R einforcement Learning Workflow The steps to design a reinforcement learning agent can be summarized as follows: The workflow will be applied to the QUBE-Servo 2 Inverted Pendulum application in the next section.

Reward and Stop Signals The qr reward subsystem contains the reward and stop signals. Observation and Action Signals For the rotary inverted pendulum there are four observation signals — the angular position and velocity of the rotary arm and the pendulum link, i.

Here are some of the most important aspects in training an agent that will successfully balance the inverted pendulum: Reset function that randomizes the initial position of inverted pendulum. This is needed for the RL agent to be able to balance the pendulum within a large enough range, i. Stop signals are used to stop a training episode when a system, for instance, exceeds certain limitations of the physical hardware.

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