DiffLoop: Tuning PID controllers by differentiating through the feedback loop

• URL: http://arxiv.org/abs/2106.10516v1
• Date: Sat, 19 Jun 2021 15:26:46 GMT
• Title: DiffLoop: Tuning PID controllers by differentiating through the feedback loop
• Authors: Athindran Ramesh Kumar, Peter J. Ramadge
• Abstract summary: This paper investigates PID tuning and anti-windup measures. In particular, we use a cost function and generate gradients to improve controller performance.
• Score: 8.477619837043214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Since most industrial control applications use PID controllers, PID tuning and anti-windup measures are significant problems. This paper investigates tuning the feedback gains of a PID controller via back-calculation and automatic differentiation tools. In particular, we episodically use a cost function to generate gradients and perform gradient descent to improve controller performance. We provide a theoretical framework for analyzing this non-convex optimization and establish a relationship between back-calculation and disturbance feedback policies. We include numerical experiments on linear systems with actuator saturation to show the efficacy of this approach.

Related papers

• Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution [51.83951489847344]
  In robotics applications, smooth control signals are commonly preferred to reduce system wear and energy efficiency. In this work, we aim to bridge this performance gap by growing discrete action spaces from coarse to fine control resolution. Our work indicates that an adaptive control resolution in combination with value decomposition yields simple critic-only algorithms that yield surprisingly strong performance on continuous control tasks.
  arXiv  Detail & Related papers  (2024-04-05T17:58:37Z)
• Semi-Supervised Coupled Thin-Plate Spline Model for Rotation Correction and Beyond [84.56978780892783]
  We propose CoupledTPS, which iteratively couples multiple TPS with limited control points into a more flexible and powerful transformation. In light of the laborious annotation cost, we develop a semi-supervised learning scheme to improve warping quality by exploiting unlabeled data. Experiments demonstrate the superiority and universality of CoupledTPS over the existing state-of-the-art solutions for rotation correction.
  arXiv  Detail & Related papers  (2024-01-24T13:03:28Z)
• Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control [0.0]
  Proportional-Integrator-Derivative (PID) controller is used in a wide range of industrial and experimental processes. Due to the uncertainty of model parameters and external disturbances, real systems such as Quadrotors need more robust and reliable PID controllers. In this research, a self-tuning PID controller using a Reinforcement-Learning-based Neural Network has been investigated.
  arXiv  Detail & Related papers  (2023-07-03T19:35:52Z)
• Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning [0.38073142980732994]
  We introduce domain randomization during the training phase of a low-level waypoint guidance controller based on Soft Actor-Critic. We show that, by introducing a certain degree of uncertainty in quadrotor dynamics during training, we can obtain a controller that is capable to perform the proposed task using a larger variation of quadrotor parameters.
  arXiv  Detail & Related papers  (2022-10-06T14:58:19Z)
• Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning [6.5158195776494]
  We tackle the controller tuning problem using a novel derivative-free reinforcement learning framework. We conduct numerical experiments on two concrete examples from autonomous driving, namely, adaptive cruise control with PID controller and trajectory tracking with MPC controller. Experimental results show that the proposed method outperforms popular baselines and highlight its strong potential for controller tuning.
  arXiv  Detail & Related papers  (2022-09-11T13:01:14Z)
• On Controller Tuning with Time-Varying Bayesian Optimization [74.57758188038375]
  We will use time-varying optimization (TVBO) to tune controllers online in changing environments using appropriate prior knowledge on the control objective and its changes. We propose a novel TVBO strategy using Uncertainty-Injection (UI), which incorporates the assumption of incremental and lasting changes. Our model outperforms the state-of-the-art method in TVBO, exhibiting reduced regret and fewer unstable parameter configurations.
  arXiv  Detail & Related papers  (2022-07-22T14:54:13Z)
• Regret-optimal Estimation and Control [52.28457815067461]
  We show that the regret-optimal estimator and regret-optimal controller can be derived in state-space form. We propose regret-optimal analogs of Model-Predictive Control (MPC) and the Extended KalmanFilter (EKF) for systems with nonlinear dynamics.
  arXiv  Detail & Related papers  (2021-06-22T23:14:21Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• Adaptive Optimal Trajectory Tracking Control Applied to a Large-Scale Ball-on-Plate System [0.0]
  We propose an ADP-based optimal trajectory tracking controller for a large-scale ball-on-plate system. Our proposed method incorporates an approximated reference trajectory instead of using setpoint tracking and allows to automatically compensate for constant offset terms. Our experimental results show that this tracking mechanism significantly reduces the control cost compared to setpoint controllers.
  arXiv  Detail & Related papers  (2020-10-26T11:22:03Z)
• Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem [3.131740922192114]
  We focus on the interpretability of DRL control methods. In particular, we view linear fixed-structure controllers as shallow neural networks embedded in the actor-critic framework.
  arXiv  Detail & Related papers  (2020-05-10T01:05:26Z)
• Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework [108.4560749465701]
  The Pontryagin Differentiable Programming methodology establishes a unified framework to solve a broad class of learning and control tasks. We investigate three learning modes of the PDP: inverse reinforcement learning, system identification, and control/planning. We demonstrate the capability of the PDP in each learning mode on different high-dimensional systems, including multi-link robot arm, 6-DoF maneuvering quadrotor, and 6-DoF rocket powered landing.
  arXiv  Detail & Related papers  (2019-12-30T15:35:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.