Control and Dynamic Systems V28

Preface

Algorithms and Computational Techniques in Stochastic Optimal Control

I. Introduction

II. Review of Stochastic Optimal Control

III. Modeling, Optimal Control, Estimation, Identification, and Design of Stochastic Systems

IV. Conclusions

References

Computational Techniques for the Matrix Pseudoinverse in Minimum Variance Reduced-Order Filtering and Control

I. Introduction

II. Counterexamples to Two Questionable Pseudoinverse Algorithms

III. Proper Calculation of Matrix Pseudoinverse by Validated Software: A Status Review

IV. A Survey of Pseudoinverse Applications

V Dependence of Minimum Variance Reduced-Order Filtering on the Pseudoinverse

VI. Summary

VII. Appendix A: Theoretical Basis for the Pseudoinverse of a Continuous Linear Transformation

VIII. Appendix B: Longhand Calculation of a Matrix Pseudoinverse

IX. Appendix C: An Application of the Pseudoinverse in Minimum Energy Optimal Control

References

Decomposition Technique in Multiobjective Discrete-Time Dynamic Problems: The Envelope Approach

I. Introduction

II. Basic Theorem of the Envelope Approach

III. Multiobjective Dynamic Programming Method Using the Envelope Approach

IV. Hierarchical Generating Methods

V. Conclusions

References

Algorithms and Computational Techniques in Robotic Systems

I. Introduction

II. Manipulator Dynamics and Control Methods

III. Stability Analysis and Simulation

IV. Physical Modeling of Flexible Manipulators

V Control Schemes and Simulation

VI. Discussion and Conclusion

References

A Methodology Based on Reduced Complexity Algorithm for System Applications Using Microprocessors

I. Introduction

II. Linear Equalizer

III. Branch-and-Bound Algorithm

IV Hardware and Software Descriptions

V Numerical Results and Conclusions

References

Algorithms for Image-Based Trackers of Maneuvering Targets

I. Introduction

II. Current Approaches to Image-Based Target Tracking

III. Comparative Performance of an Image-Based System

IV. Orientation Information in Prediction

V. Conclusions

References

Modeling and Simplification of Linear and Nonlinear Systems

I. Introduction

II. Problem Statement

III. Projection Methods

IV An Optimal Modeling Algorithm

V Modeling of Nonlinear Systems from State Information

VI. Modeling of Nonlinear Discrete-Time Systems from Input-Output Data

References

Index