Design of Adaptive Switching Controller for Robotic Manipulators with Disturbance

Two adaptive switching control strategies are proposed for the trajectory tracking problem of robotic manipulator in this paper. The first scheme is designed for the supremum of the bounded disturbance for robot manipulator being known; while the supremum is not known, the second scheme is proposed. Each proposed scheme consists of an adaptive switching law and a PD controller. Based on the Lyapunov stability theorem, it is shown that two new schemes can guarantee tracking performance of the robotic manipulator and be adapted to the alternating unknown loads. Simulations for two-link robotic manipulator are carried out and show that the two schemes can avoid the overlarge input torque, and the feasibility and validity of the proposed control schemes are proved

Putting artificial intelligence into wearable human-machine interfaces – towards a generic, self-improving controller

The standard approach to creating a machine learning based controller is to provide users with a number of gestures that they need to make; record multiple instances of each gesture using specific sensors; extract the relevant sensor data and pass it through a supervised learning algorithm until the algorithm can successfully identify the gestures; map each gesture to a control signal that performs a desired outcome. This approach is both inflexible and time consuming. The primary contribution of this research was to investigate a new approach to putting artificial intelligence into wearable human-machine interfaces by creating a Generic, Self-Improving Controller. It was shown to learn two user-defined static gestures with an accuracy of 100% in less than 10 samples per gesture; three in less than 20 samples per gesture; and four in less than 35 samples per gesture. Pre-defined dynamic gestures were more difficult to learn. It learnt two with an accuracy of 90% in less than 6,000 samples per gesture; and four with an accuracy of 70% after 50,000 samples per gesture. The research has resulted in a number of additional contributions: • The creation of a source-independent hardware data capture, processing, fusion and storage tool for standardising the capture and storage of historical copies of data captured from multiple different sensors. • An improved Attitude and Heading Reference System (AHRS) algorithm for calculating orientation quaternions that is five orders of magnitude more precise. • The reformulation of the regularised TD learning algorithm; the reformulation of the TD learning algorithm applied the artificial neural network back-propagation algorithm; and the combination of the reformulations into a new, regularised TD learning algorithm applied to the artificial neural network back-propagation algorithm. • The creation of a Generic, Self-Improving Predictor that can use different learning algorithms and a Flexible Artificial Neural Network.Open Acces

NEURAL NETWORK BASED REACTIVE CONTROL OF POINT ABSORBER WAVE ENERGY CONVERTERS

The main objective of this work is to develop a neural-network-based Reactive Control (RC) system for wave energy converters. The ability to maximize the power output of WEC while maintaining operation constraints, which can be physical or thermal, is crucial to the development of deployable control strategies. Having a control method that is robust, which means it handles uncertainty and noise very well, is one of the main performance criteria in evaluating the method. Therefore, this work starts by deriving an averaged WEC model to be simulated in MATLAB/Simulink. Additionally, the concepts of resistive loading control and reactive control (approximate conjugate control) are discussed. A solution to sea state estimation is developed and explained which poses a contribution the current WEC research. This novel technique uses recurrent neural networks (RNNs) with time-series data input to estimate the sea state in real-time. The technique fills the gap of estimating forces based on peak frequencies and also the problem of calculating sea states based on periodical averaged statistical analysis. To complete the methodology, an optimization technique using feed forward neural networks is improved to perform optimization that is proposed to optimize the power output with respect to the sea states. This is done by using the neural network as a cost function while using the physical limitations of the system as a constraint. The neural networks in this work are developed, trained and tested using MATLAB’s Deep Network Designer and Deep Learning Toolbox then imported as a Simulink block to complete the simulation. The results are evaluated for each of the section. First, initial logging of the performance metrics, such as mean power, is done prior to the addition of any neural networks. The accuracy and robustness of the sea state estimation RNN is then discussed. Finally, a comparison between traditional reactive Control optimized and reactive Control is conducted. To summarize the outcome, after experimenting with different datasets and architectures, the RNN is able to estimate sea states in real-time under different initial conditions

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book