TY - BOOK ID - 8285422 TI - Stochastic distribution control system design : a convex optimization approach AU - Guo, Lei. AU - Wang, Hong. PY - 2010 SN - 1849960291 9786612837982 1849960305 1282837982 PB - London : Springer-Verlag, DB - UniCat KW - Automatic control. KW - Stochastic control theory. KW - System design. KW - Automatic control KW - System design KW - Stochastic control theory KW - Engineering & Applied Sciences KW - Mathematics KW - Mechanical Engineering KW - Applied Mathematics KW - Mechanical Engineering - General KW - Mathematical Statistics KW - Physical Sciences & Mathematics KW - Stochastic processes. KW - Control engineering KW - Control equipment KW - Random processes KW - Engineering. KW - Chemical engineering. KW - Computer mathematics. KW - Probabilities. KW - Applied mathematics. KW - Engineering mathematics. KW - Control engineering. KW - Manufacturing industries. KW - Machines. KW - Tools. KW - Appl.Mathematics/Computational Methods of Engineering. KW - Computational Mathematics and Numerical Analysis. KW - Probability Theory and Stochastic Processes. KW - Control. KW - Industrial Chemistry/Chemical Engineering. KW - Manufacturing, Machines, Tools. KW - Hand tools KW - Handtools KW - Hardware KW - Implements, utensils, etc. KW - Machinery KW - Machines KW - Manufactures KW - Power (Mechanics) KW - Technology KW - Mechanical engineering KW - Motors KW - Power transmission KW - Industries KW - Control theory KW - Engineering instruments KW - Automation KW - Programmable controllers KW - Engineering KW - Engineering analysis KW - Mathematical analysis KW - Probability KW - Statistical inference KW - Combinations KW - Chance KW - Least squares KW - Mathematical statistics KW - Risk KW - Computer mathematics KW - Discrete mathematics KW - Electronic data processing KW - Chemistry, Industrial KW - Engineering, Chemical KW - Industrial chemistry KW - Chemistry, Technical KW - Metallurgy KW - Construction KW - Industrial arts KW - Curious devices KW - Probabilities UR - https://www.unicat.be/uniCat?func=search&query=sysid:8285422 AB - 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 engineering, flame-distribution control in energy generation and combustion engines, steel and film production, papermaking and general quality data distribution control for various industries. SDC is different from well-developed forms of stochastic control like minimum-variance and linear-quadratic-Gaussian control, in which the aim is limited to the design of controllers for the output mean and variances. An important recent development in SDC-related problems is the establishment of intelligent SDC models and the intensive use of linear-matrix-inequality-based (LMI-based) convex optimization methods. Within this theoretical framework, control parameter determination can be designed and stability and robustness of closed-loop systems can be analyzed. Stochastic Distribution Control System Design describes the new framework of SDC system design and provides a comprehensive description of the modelling of controller design tools and their real-time implementation. The book starts with a review of current research on SDC and moves on to some basic techniques for modelling and controller design of SDC systems. This is followed by a description of controller design for fixed-control-structure SDC systems, PDF control for general input- and output-represented systems, filtering designs, and fault detection and diagnosis (FDD) for SDC systems. Many new LMI techniques being developed for SDC systems are shown to have independent theoretical significance for robust control and FDD problems. This monograph will be of interest to academic researchers in statistical, robust and process control, and FDD, process and quality control engineers working in industry and as a reference for graduate control students.  . ER -