Stochastic Distribution Control System Design A Convex Optimization Approach /

Stochastic distribution control (SDC) systems are widely seen in practical industrial processes, the aim of the controller design being generation of output probability density functions for non-Gaussian systems. Examples of SDC processes are: particle-size-distribution control in chemical engineeri...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Guo, Lei (Συγγραφέας), Wang, Hong (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: London : Springer London, 2010.
Σειρά:Advances in Industrial Control,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Developments in Stochastic Distribution Control Systems
  • Developments in Stochastic Distribution Control Systems
  • Structural Controller Design for Stochastic Distribution Control Systems
  • Proportional Integral Derivative Control for Continuous-time Stochastic Systems
  • Constrained Continuous-time Proportional Integral Derivative Control Based on Convex Algorithms
  • Constrained Discrete-time Proportional Integral Control Based on Convex Algorithms
  • Two-step Intelligent Optimization Modeling and Control for Stochastic Distribution Control Systems
  • Adaptive Tracking Stochastic Distribution Control for Two-step Neural Network Models
  • Constrained Adaptive Proportional Integral Tracking Control for Two-step Neural Network Models with Delays
  • Constrained Proportional Integral Tracking Control for Takagi-Sugeno Fuzzy Model
  • Statistical Tracking Control – Driven by Output Statistical Information Set
  • Multiple-objective Statistical Tracking Control Based on Linear Matrix Inequalities
  • Adaptive Statistical Tracking Control Based on Two-step Neural Networks with Time Delays
  • Fault Detection and Diagnosis for Stochastic Distribution Control Systems
  • Optimal Continuous-time Fault Detection Filtering
  • Optimal Discrete-time Fault Detection and Diagnosis Filtering
  • Conclusions
  • Summary and Potential Applications.