Online-Learning Deep Neuro-Adaptive Dynamic Inversion Controller for Model Free Control

• URL: http://arxiv.org/abs/2107.10383v1
• Date: Wed, 21 Jul 2021 22:46:03 GMT
• Title: Online-Learning Deep Neuro-Adaptive Dynamic Inversion Controller for Model Free Control
• Authors: Nathan Lutes and K. Krishnamurthy and Venkata Sriram Siddhardh Nadendla and S. N. Balakrishnan
• Abstract summary: A neuro-adaptive controller is implemented featuring a deep neural network trained on a new weight update law. The controller is able to learn the nonlinear plant quickly and displays good performance in the tracking control problem.
• Score: 1.3764085113103217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive methods are popular within the control literature due to the flexibility and forgiveness they offer in the area of modelling. Neural network adaptive control is favorable specifically for the powerful nature of the machine learning algorithm to approximate unknown functions and for the ability to relax certain constraints within traditional adaptive control. Deep neural networks are large framework networks with vastly superior approximation characteristics than their shallow counterparts. However, implementing a deep neural network can be difficult due to size specific complications such as vanishing/exploding gradients in training. In this paper, a neuro-adaptive controller is implemented featuring a deep neural network trained on a new weight update law that escapes the vanishing/exploding gradient problem by only incorporating the sign of the gradient. The type of controller designed is an adaptive dynamic inversion controller utilizing a modified state observer in a secondary estimation loop to train the network. The deep neural network learns the entire plant model on-line, creating a controller that is completely model free. The controller design is tested in simulation on a 2 link planar robot arm. The controller is able to learn the nonlinear plant quickly and displays good performance in the tracking control problem.

Related papers

• Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch [72.26822499434446]
  Auto-Train-Once (ATO) is an innovative network pruning algorithm designed to automatically reduce the computational and storage costs of DNNs. We provide a comprehensive convergence analysis as well as extensive experiments, and the results show that our approach achieves state-of-the-art performance across various model architectures.
  arXiv  Detail & Related papers  (2024-03-21T02:33:37Z)
• Neural Control: Concurrent System Identification and Control Learning with Neural ODE [13.727727205587804]
  We propose a neural ODE based method for controlling unknown dynamical systems, denoted as Neural Control (NC) Our model concurrently learns system dynamics as well as optimal controls that guides towards target states. Our experiments demonstrate the effectiveness of our model for learning optimal control of unknown dynamical systems.
  arXiv  Detail & Related papers  (2024-01-03T17:05:17Z)
• Rational Neural Network Controllers [0.0]
  Recent work has demonstrated the effectiveness of neural networks in control systems (known as neural feedback loops) One of the big challenges of this approach is that neural networks have been shown to be sensitive to adversarial attacks. This paper considers rational neural networks and presents novel rational activation functions, which can be used effectively in robustness problems for neural feedback loops.
  arXiv  Detail & Related papers  (2023-07-12T16:35:41Z)
• Closed-form control with spike coding networks [1.1470070927586016]
  Efficient and robust control using spiking neural networks (SNNs) is still an open problem. We extend neuroscience theory of Spike Coding Networks (SCNs) by incorporating closed-form optimal estimation and control. We demonstrate robust spiking control of simulated spring-mass-damper and cart-pole systems.
  arXiv  Detail & Related papers  (2022-12-25T10:32:20Z)
• Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling [72.84259710412293]
  Some ODE solvers called adaptive can adapt their evaluation strategy depending on the complexity of the problem at hand. This paper describes a simple set of experiments to show why adaptive solvers cannot be seamlessly leveraged as a black-box for dynamical systems modelling.
  arXiv  Detail & Related papers  (2022-11-13T17:48:04Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• Deep Q-learning: a robust control approach [4.125187280299247]
  We formulate an uncertain linear time-invariant model by means of the neural tangent kernel to describe learning. We show the instability of learning and analyze the agent's behavior in frequency-domain. Numerical simulations in different OpenAI Gym environments suggest that the $mathcalH_infty$ controlled learning performs slightly better than Double deep Q-learning.
  arXiv  Detail & Related papers  (2022-01-21T09:47:34Z)
• Provable Regret Bounds for Deep Online Learning and Control [77.77295247296041]
  We show that any loss functions can be adapted to optimize the parameters of a neural network such that it competes with the best net in hindsight. As an application of these results in the online setting, we obtain provable bounds for online control controllers.
  arXiv  Detail & Related papers  (2021-10-15T02:13:48Z)
• Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion [95.1825179206694]
  We present a framework that synthesizes robust controllers for a quadruped robot. A high-level controller learns to choose from a set of primitives in response to changes in the environment. A low-level controller that utilizes an established control method to robustly execute the primitives.
  arXiv  Detail & Related papers  (2020-09-21T16:49:26Z)
• Dynamic Modeling and Adaptive Controlling in GPS-Intelligent Buoy (GIB) Systems Based on Neural-Fuzzy Networks [0.0]
  In this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. The numerical and obtained consequences demonstrate that the control system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.
  arXiv  Detail & Related papers  (2020-04-03T17:28:53Z)
• Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems [91.43582419264763]
  We study the problem of system identification and adaptive control in partially observable linear dynamical systems. We present the first model estimation method with finite-time guarantees in both open and closed-loop system identification. We show that AdaptOn is the first algorithm that achieves $textpolylogleft(Tright)$ regret in adaptive control of unknown partially observable linear dynamical systems.
  arXiv  Detail & Related papers  (2020-03-25T06:00:33Z)

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.