Topic
Feedback linearization
About: Feedback linearization is a research topic. Over the lifetime, 8654 publications have been published within this topic receiving 163686 citations.
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01 Jan 1996
TL;DR: In this paper, the authors propose a joint space control task space control for rigid manipulators and flexible manipulators with elastic joints and flexible links, as well as modeling and structural properties feedback linearization nonlinear feedback control.
Abstract: Part 1 Rigid manipulators: modelling and identification joint space control task space control motion and force control. Part 2 Flexible manipulators: elastic joints flexible links. Part 3 Mobile robots: modelling and structural properties feedback linearization nonlinear feedback control. Appendix: control background.
1,119 citations
Book•
01 Jan 1996TL;DR: In this paper, the authors propose robust regulation and adaptive tracking for output feedback and output output tracking with adaptive observer and adaptive feedback linearization, and robust regulation for robust output feedback with adaptive tracking.
Abstract: STATE FEEDBACK. Feedback Linearization. Adaptive Feedback Linearization. Output Tracking. OUTPUT FEEDBACK. Adaptive Observers. Stabilization and Exponential Tracking. Robust Regulation and Adaptive Tracking.
925 citations
Book•
02 Feb 2000
TL;DR: In this paper, the authors present an approach for detecting models and controllers from data using a multilayer perceptron (MLP) model and a linear model of the control system.
Abstract: 1. Introduction.- 1.1 Background.- 1.1.1 Inferring Models and Controllers from Data.- 1.1.2 Why Use Neural Networks?.- 1.2 Introduction to Multilayer Perceptron Networks.- 1.2.1 The Neuron.- 1.2.2 The Multilayer Perceptron.- 1.2.3 Choice of Neural Network Architecture.- 1.2.4 Models of Dynamic Systems.- 1.2.5 Recurrent Networks.- 1.2.6 Other Neural Network Architectures.- 2. System Identification with Neural Networks.- 2.1 Introduction to System Identification.- 2.1.1 The Procedure.- 2.2 Model Structure Selection.- 2.2.1 Some Linear Model Structures.- 2.2.2 Nonlinear Model Structures Based on Neural Networks.- 2.2.3 A Few Remarks on Stability.- 2.2.4 Terminology.- 2.2.5 Selecting the Lag Space.- 2.2.6 Section Summary.- 2.3 Experiment.- 2.3.1 When is a Linear Model Insufficient?.- 2.3.2 Issues in Experiment Design.- 2.3.3 Preparing the Data for Modelling.- 2.3.4 Section Summary.- 2.4 Determination of the Weights.- 2.4.1 The Prediction Error Method.- 2.4.2 Regularization and the Concept of Generalization.- 2.4.3 Remarks on Implementation.- 2.4.4 Section Summary.- 2.5 Validation.- 2.5.1 Looking for Correlations.- 2.5.2 Estimation of the Average Generalization Error.- 2.5.3 Visualization of the Predictions.- 2.5.4 Section Summary.- 2.6 Going Backwards in the Procedure.- 2.6.1 Training the Network Again.- 2.6.2 Finding the Optimal Network Architecture.- 2.6.3 Redoing the Experiment.- 2.6.4 Section Summary.- 2.7 Recapitulation of System Identification.- 3. Control with Neural Networks.- 3.1 Introduction to Neural-Network-based Control.- 3.1.1 The Benchmark System.- 3.2 Direct Inverse Control.- 3.2.1 General Training.- 3.2.2 Direct Inverse Control of the Benchmark System.- 3.2.3 Specialized Training.- 3.2.4 Specialized Training and Direct Inverse Control of the Benchmark System.- 3.2.5 Section Summary.- 3.3 Internal Model Control (IMC).- 3.3.1 Internal Model Control with Neural Networks.- 3.3.2 Section Summary.- 3.4 Feedback Linearization.- 3.4.1 The Basic Principle of Feedback Linearization.- 3.4.2 Feedback Linearization Using Neural Network Models..- 3.4.3 Feedback Linearization of the Benchmark System.- 3.4.4 Section Summary.- 3.5 Feedforward Control.- 3.5.1 Feedforward for Optimizing an Existing Control System.- 3.5.2 Feedforward Control of the Benchmark System.- 3.5.3 Section Summary.- 3.6 Optimal Control.- 3.6.1 Training of an Optimal Controller.- 3.6.2 Optimal Control of the Benchmark System.- 3.6.3 Section Summary.- 3.7 Controllers Based on Instantaneous Linearization.- 3.7.1 Instantaneous Linearization.- 3.7.2 Applying Instantaneous Linearization to Control.- 3.7.3 Approximate Pole Placement Design.- 3.7.4 Pole Placement Control of the Benchmark System.- 3.7.5 Approximate Minimum Variance Design.- 3.7.6 Section Summary.- 3.8 Predictive Control.- 3.8.1 Nonlinear Predictive Control (NPC).- 3.8.2 NPC Applied to the Benchmark System.- 3.8.3 Approximate Predictive Control (APC).- 3.8.4 APC applied to the Benchmark System.- 3.8.5 Extensions to the Predictive Controller.- 3.8.6 Section Summary.- 3.9 Recapitulation of Control Design Methods.- 4. Case Studies.- 4.1 The Sunspot Benchmark.- 4.1.1 Modelling with a Fully Connected Network.- 4.1.2 Pruning of the Network Architecture.- 4.1.3 Section Summary.- 4.2 Modelling of a Hydraulic Actuator.- 4.2.1 Estimation of a Linear Model.- 4.2.2 Neural Network Modelling of the Actuator.- 4.2.3 Section Summary.- 4.3 Pneumatic Servomechanism.- 4.3.1 Identification of the Pneumatic Servomechanism.- 4.3.2 Nonlinear Predictive Control of the Servo.- 4.3.3 Approximate Predictive Control of the Servo.- 4.3.4 Section Summary.- 4.4 Control of Water Level in a Conic Tank.- 4.4.1 Linear Analysis and Control.- 4.4.2 Direct Inverse Control of the Water Level.- 4.4.3 Section Summary.- References.
923 citations
TL;DR: In this article, the emergence of robust designs for nonlinear 'interval' plants is pointed out, and some of these tools can be made adaptive and applied to nonlinear systems with unknown parameters.
Abstract: It is argued that, for a cautious design, a nonlinear analysis is needed to reveal when and why linear tools fail. Emerging nonlinear tools that can be used to overcome the limitations of nonlinear designs are discussed. It is shown that some of these tools can be made adaptive and applied to nonlinear systems with unknown parameters. The emergence of robust designs for nonlinear 'interval' plants is pointed out. >
852 citations
TL;DR: Sliding-mode and feedback linearization techniques along with large-signal phase plane analysis are presented as methods to analyze, control, and stabilize automotive converters/systems operating with CPLs.
Abstract: Power electronic converters and electric motor drives are being put into use at an increasingly rapid rate in advanced automobiles. However, the new advanced automotive electrical systems employ multivoltage level hybrid ac and dc as well as electromechanical systems that have unique characteristics, dynamics, and stability problems that are not well understood due to the nonlinearity and time dependency of converters and because of their constant power characteristics. The purpose of this paper is to present an assessment of the negative impedance instability concept of the constant power loads (CPLs) in automotive power systems. The main focus of this paper is to analyze and propose design criteria of controllers for automotive converters/systems operating with CPLs. The proposed method is to devise a new comprehensive approach to the applications of power electronic converters and motor drives in advanced automotive systems. Sliding-mode and feedback linearization techniques along with large-signal phase plane analysis are presented as methods to analyze, control, and stabilize automotive converters/systems with CPLs
813 citations