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Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
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  1. Home
  3. Reservoir Computing For System Identification And Model Predictive Control.
  1. Home
  3. Reservoir Computing For System Identification And Model Predictive Control.

Related Experiment Video

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Reservoir computing for system identification and model predictive control.

Jan P Williams1, J Nathan Kutz2, Krithika Manohar1

  • 1Department of Mechanical Engineering, University of Washington, Seattle, Washington, 98195, USA.

Neural Networks : the Official Journal of the International Neural Network Society
|May 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Echo state networks (ESNs) effectively replace complex system models in model predictive control (MPC). ESNs offer superior performance, faster training, and greater efficiency compared to LSTMs for data-driven control tasks.

Keywords:
Data-driven controlEcho state networkModel predictive controlSurrogate modeling,

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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Dynamical Systems

Background:

  • Model predictive control (MPC) requires accurate and efficient dynamical models for real-time operation.
  • Complex systems often lack precise models or have computationally intensive ones, necessitating data-driven surrogate models.
  • Echo state networks (ESNs) are recurrent neural networks suitable for approximating complex system dynamics.

Purpose of the Study:

  • To evaluate Echo State Networks (ESNs) as data-driven surrogate models for system dynamics in Model Predictive Control (MPC).
  • To compare the performance of ESN-based MPC against other architectures, specifically Long Short-Term Memory (LSTM) networks.
  • To assess the efficiency and sample requirements of ESNs in the context of MPC.

Main Methods:

  • Implementing MPC with ESNs as surrogate models for system dynamics.
  • Benchmarking performance on control tasks including the Lorenz system and fluid flow.
  • Comparing ESN-based MPC against LSTM-based MPC in terms of control objective achievement, cost, and variability.
  • Analyzing training time and sample efficiency of ESNs versus LSTMs.

Main Results:

  • ESN-based MPC consistently achieved control objectives on challenging benchmarks where LSTM-based MPC often failed.
  • ESN-based MPC reduced average control cost by up to 10% and decreased variability by up to 85% compared to LSTM-based MPC.
  • ESNs demonstrated superior sample efficiency and were over an order of magnitude faster to train than LSTMs.

Conclusions:

  • ESNs are accurate and efficient data-driven surrogate models for MPC in complex systems with unknown dynamics.
  • ESNs outperform LSTMs in terms of control performance, cost reduction, and training efficiency.
  • ESNs enable scalable data-driven MPC, particularly when training data is limited.