Neural Adaptive Control of a Robot Joint Using Secondary Encoders

Using industrial robots for machining applications in flexible manufacturing processes lacks a high accuracy. The main reason for the deviation is the flexibility of the gearbox. Secondary Encoders (SE) as an additional, high precision angle sensor offer a huge potential of detecting gearbox deviations. This paper aims to use SE to reduce gearbox compliances with a feed forward, adaptive neural control. The control network is trained with a second network for system identification. The presented algorithm is capable of online application and optimizes the robot accuracy in a nonlinear simulation

Integrated Neural Adaptive Control for In-pipe Robot Locomotion

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P2

Robust Adaptive Control via Neural Linearization and Compensation

We propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven

Neural self-tuning adaptive control of non-minimum phase system

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity, if not unstable, closed-loop behavior. Therefore, a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response

Discrete-time weight updates in neural-adaptive control

Abstract Typical neural-adaptive control approaches update neural-network weights as though they were adaptive parameters in a continuous-time adaptive control. However, requiring fast digital rates usually restricts the size of the neural network. In this paper we analyze a deltarule update for the weights, applied at a relatively slow digital rate. We show that digital weight update causes the neural network to estimate a discrete-time model of the system, assuming that state feedback is still applied in continuous time. A Lyapunov analysis shows uniformly ultimately bounded signals. Furthermore, slowing the update frequency and using the extra computational time to increase the size/accuracy of the neural network results in better performance. Experimental results achieving link tracking of a two-link flexible-joint robot verify the improved performance

Afferents integration and neural adaptive control of breathing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references.The respiratory regulatory system is one of the most extensively studied homeostatic systems in the body. Despite its deceptively mundane physiological function, the mechanism underlying the robust control of the motor act of breathing in the face of constantly changing internal and external challenges throughout one's life is still poorly understood. Traditionally, control of breathing has been studied with a highly reductionist approach, with specific stimulus-response relationships being taken to reflect distinct feedback/feedforward control laws. It is assumed that the overall respiratory response could be described as the linear sum of all unitary stimulus-response relationships under a Sherringtonian framework. Such a divide-and-conquer approach has proven useful in predicting the independent effects of specific chemical and mechanical inputs. However, it has limited predictive power for the respiratory response in realistic disease states when multiple factors come into play. Instead, vast amounts of evidence have revealed the existence of complex interactions of various afferent-efferent signals in defining the overall respiratory response. This thesis aims to explore the nonlinear interaction of afferents in respiratory control. In a series of computational simulations, it was shown that the respiratory response in humans during muscular exercise under a variety of pulmonary gas exchange defects is consistent with an optimal interaction of mechanical and chemical afferents. This provides a new understanding on the impacts of pulmonary gas exchange on the adaptive control of the exercise respiratory response. Furthermore, from a series of in-vivo neurophysiology experiments in rats, it was discovered that certain respiratory neurons in the dorsolateral pons in the rat brainstem integrate central and peripheral chemoreceptor afferent signals in a hypoadditive manner. Such nonlinear interaction evidences classical (Pavlovian) conditioning of chemoreceptor inputs that modulate the respiratory rhythm and motor output. These findings demonstrate a powerful gain modulation function for control of breathing by the lower brain. The computational and experimental studies in this thesis reveal a form of associative learning important for adaptive control of respiratory regulation, at both behavioral and neuronal levels. Our results shed new light for future experimental and theoretical elucidation of the mechanism of respiratory control from an integrative modeling perspective.by Chung Tin.Ph.D

Adaptive control strategies for flexible robotic arm

The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response

Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

In this work a practical study evaluates two parametric modelling approaches -- linear and non-linear (neural) -- for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and different types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.http://www.sciencedirect.com/science/article/B6V2H-3Y51H01-2/1/50fbcda6652e0853352a54ab0d31ca2