An adaptive deep reinforcement learning approach for MIMO PID control of mobile robots

Ignacio Carlucho, Mariano De Paula, Gerardo G. Acosta

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Intelligent control systems are being developed for the control of plants with complex dynamics. However, the simplicity of the PID (proportional–integrative–derivative) controller makes it still widely used in industrial applications and robotics. This paper proposes an intelligent control system based on a deep reinforcement learning approach for self-adaptive multiple PID controllers for mobile robots. The proposed hybrid control strategy uses an actor–critic structure and it only receives low-level dynamic information as input and simultaneously estimates the multiple parameters or gains of the PID controllers. The proposed approach was tested in several simulated environments and in a real time robotic platform showing the feasibility of the approach for the low-level control of mobile robots. From the simulation and experimental results, our proposed approach demonstrated that it can be of aid by providing with behavior that can compensate or even adapt to changes in the uncertain environments providing a model free unsupervised solution. Also, a comparative study against other adaptive methods for multiple PIDs tuning is presented, showing a successful performance of the approach.
Original languageEnglish
Pages (from-to)280-294
Number of pages15
JournalISA Transactions
Early online date19 Feb 2020
Publication statusPublished - 1 Jul 2020

Keywords / Materials (for Non-textual outputs)

  • Reinforcement learning
  • Adaptive control
  • Policy gradient
  • Mobile robots
  • Multi-platforms


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