Advanced Techniques and Technology of Computer-Aided Feedback Control

2.4.2. Structure of the partial state observer -- 2.4.3. Diagram of discrete state feedback with partial observer -- 2.5. Discrete state feedback with set point tracking -- 2.6. Block diagram of a digital control system -- 2.7. Computer-aided simulation of a servomechanism -- 2.7.1. Simulation of a speed servomechanism -- 2.7.2. Computer-aided simulation of a position servomechanism -- 2.8. Exercises and solutions -- 3. Multimedia Test Bench for Computer-aided Feedback Control -- 3.1. Context and interest -- 3.1.1. Context -- 3.1.2. Scientific/teaching interest -- 3.1.3. Platform presentation methodology -- 3.2. Hardware constituents of the platform -- 3.3. Design elements of the ServoSys software application -- 3.3.1. Fundamental elements -- 3.3.2. Elements of software programming -- 3.4. Design of the ServoSys software application -- 3.4.1. Architectural diagram of the software application -- 3.4.2. SFC of the ServoSys multimedia platform -- 3.5. Implementation of the ServoSys multimedia platform -- 3.5.1. Hardware implementation -- 3.5.2. Software implementation -- 3.6. Overall tests of the platform -- 3.6.1. Commissioning and procedures -- 3.6.2. Samples of results displayed on the Matlab/GUI panel -- 3.7. Exercises and solutions -- Part 2 Deterministic and Stochastic Optimal Digital Feedback Control -- 4. Deterministic Optimal Digital Feedback Control -- 4.1. Optimal control: context and historical background -- 4.1.1. Context -- 4.1.2. Historical background -- 4.2. General problem of discrete-time optimal control -- 4.2.1. Principle -- 4.2.2. Functional formulation -- 4.3. Linear quadratic regulator (LQR) -- 4.3.1. Definition, formulation and study methods -- 4.3.2. H-J-B equations -- 4.4. Translation in discrete time of continuous LQR problem -- 4.4.1. Discretization of state equation -- 4.4.2. Discretization of the cost function

4.4.3. Case study of a scalar LQR problem -- 4.5. Predictive optimal control -- 4.5.1. Basic principle -- 4.5.2. Recurrence equation of a process based on q-1 operator -- 4.5.3. General formulation of a prediction model -- 4.5.4. Solution and structure of predictive optimal control -- 4.6. Exercises and solutions -- 5. Stochastic Optimal Digital Feedback Control -- 5.1. Introduction to stochastic dynamic processes -- 5.2. Stochastic LQR -- 5.2.1. Formulation -- 5.2.2. Resolution of the stochastic H-J-B equation -- 5.2.3. Block diagram of stochastic LQR -- 5.2.4. Properties of stochastic LQR -- 5.3. Discrete Kalman filter -- 5.3.1. Scientific context and hypotheses -- 5.3.2. Notations -- 5.3.3. Closed-loop algorithmic diagram -- 5.4. Linear Quadratic Gaussian regulator -- 5.4.1. Context -- 5.4.2. Separation principle -- 5.4.3. Algorithmic diagram of LQG regulator -- 5.5. Exercises and solutions -- 6. Deployed Matlab/GUI Platform for the Design and Virtual Simulation of Stochastic Optimal Control Systems -- 6.1. Introduction to OPCODE (Optimal Control Design) platform -- 6.1.1. Scientific context -- 6.1.2. Detailed presentation methodology -- 6.2. Fundamental OPCODE design elements -- 6.2.1. Elements of deterministic optimal control -- 6.2.2. Elements of stochastic optimal control -- 6.3. Design of OPCODE using SFC -- 6.3.1. Architectural diagram -- 6.3.2. Implementation of SFC -- 6.4. Software implementation -- 6.5. Examples of OPCODE use -- 6.5.1. Design of deterministic optimal control systems -- 6.5.2. Design of stochastic optimal control systems -- 6.6. Production of deployed OPCODE. EXE application -- 6.6.1. Interest of Matlab/GUI application deployment -- 6.6.2. Deployment methodology -- 6.6.3. Tests of deployed OPCODE. EXE application -- 6.7. Exercises and solutions -- Part 3 Remotely Operated Feedback Control Systems via the Internet

7. Elements of Remotely Operated Feedback Control Systems via the Internet -- 7.1. Problem statement -- 7.2. Infrastructural topologies -- 7.2.1. Basic topology -- 7.2.2. Advanced topologies -- 7.3. Remotely operated laboratories via the Internet -- 7.3.1. Comparison between classical and remotely operated laboratories -- 7.3.2. Infrastructures on the server side of a remotely operated laboratory -- 7.3.3. Criteria for the creation of a remotely operated laboratory -- 7.4. Exercises and solutions -- 8. Remotely Operated Automation Laboratory via the Internet -- 8.1. Introduction to remotely operated automation laboratory -- 8.1.1. Creation context -- 8.1.2. Didactic context -- 8.1.3. Specifications -- 8.2. Design and implementation of the experimental system -- 8.2.1. Descriptive diagrams -- 8.2.2. Dynamic model of the real power lighting system -- 8.2.3. Dynamic model of the PID controller for power lighting -- 8.2.4. MMMI-aided Labview application -- 8.3. Topology of the remotely operated automation laboratory -- 8.3.1. Hardware infrastructure -- 8.3.2. Specialized infrastructure on the server side -- 8.3.3. Infrastructure on the remote operator side -- 8.4. Use of a remotely operated laboratory via the Internet -- 8.4.1. Procedure instruction sheet -- 8.4.2. Samples of test results obtained with REOPAULAB -- 8.5. Exercises and solutions -- Appendices -- Appendix 1: Table of z-transforms -- T0: Sampling period -- Appendix 2: Matlab Elements Used in this Book -- Appendix 3: Discretization of Transfer Functions -- A3.1. Discretization of transfer functions of dynamic processes -- A3.2. Discretization of transfer functions of analog controllers -- Bibliography -- Index -- Other titles from iSTE in Systems and Industrial Engineering - Robotics -- EULA 
Abstract: Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Introduction -- I.1. Architecture of computer-aided control systems -- I.2. Dynamic processes to be controlled -- I.3. Multifunction data acquisition (MDAQ) interface -- I.3.1. Input/output buses -- I.3.2. Unified software structure -- I.3.3. Real-time programming operational diagram -- I.3.4. MDAQ interface driver -- I.3.5. A/D and D/A conversions in an instrumentation program -- I.3.6. Further practical information on the MDAQ interface -- I.4. Multimedia PC -- I.5. Remote access stations -- I.6. Organization of the book -- Part 1 Advanced Elements and Test Bench of Computer-aided Feedback Control -- 1. Canonical Discrete State Models of Dynamic Processes -- 1.1. Interest and construction of canonical state models -- 1.2. Canonical realizations of a transfer function G(z) -- 1.2.1. Jordan canonical realization -- 1.2.2. Controllable canonical realization -- 1.2.3. Observable canonical realization -- 1.3. Canonical transformations of discrete state models -- 1.3.1. Jordan canonical transformation -- 1.3.2. Controllable canonical transformation -- 1.3.3. Observable canonical transformation -- 1.3.4. Kalman canonical transformation -- 1.4. Canonical decomposition diagram -- 1.5. Discretization and canonical transformations using Matlab -- 1.6. Exercises and solutions -- 2. Design and Simulation of Digital State Feedback Control Systems -- 2.1. Principle of digital state feedback control -- 2.2. Calculation of the gain K using pole placement -- 2.3. State feedback with complete order observer -- 2.3.1. Problem statement -- 2.3.2. Structure of the complete or full state observer -- 2.3.3. Synthesis diagram of the state feedback with complete observer -- 2.4. Discrete state feedback with partial observer -- 2.4.1. Problem statement

2.4.2. Structure of the partial state observer -- 2.4.3. Diagram of discrete state feedback with partial observer -- 2.5. Discrete state feedback with set point tracking -- 2.6. Block diagram of a digital control system -- 2.7. Computer-aided simulation of a servomechanism -- 2.7.1. Simulation of a speed servomechanism -- 2.7.2. Computer-aided simulation of a position servomechanism -- 2.8. Exercises and solutions -- 3. Multimedia Test Bench for Computer-aided Feedback Control -- 3.1. Context and interest -- 3.1.1. Context -- 3.1.2. Scientific/teaching interest -- 3.1.3. Platform presentation methodology -- 3.2. Hardware constituents of the platform -- 3.3. Design elements of the ServoSys software application -- 3.3.1. Fundamental elements -- 3.3.2. Elements of software programming -- 3.4. Design of the ServoSys software application -- 3.4.1. Architectural diagram of the software application -- 3.4.2. SFC of the ServoSys multimedia platform -- 3.5. Implementation of the ServoSys multimedia platform -- 3.5.1. Hardware implementation -- 3.5.2. Software implementation -- 3.6. Overall tests of the platform -- 3.6.1. Commissioning and procedures -- 3.6.2. Samples of results displayed on the Matlab/GUI panel -- 3.7. Exercises and solutions -- Part 2 Deterministic and Stochastic Optimal Digital Feedback Control -- 4. Deterministic Optimal Digital Feedback Control -- 4.1. Optimal control: context and historical background -- 4.1.1. Context -- 4.1.2. Historical background -- 4.2. General problem of discrete-time optimal control -- 4.2.1. Principle -- 4.2.2. Functional formulation -- 4.3. Linear quadratic regulator (LQR) -- 4.3.1. Definition, formulation and study methods -- 4.3.2. H-J-B equations -- 4.4. Translation in discrete time of continuous LQR problem -- 4.4.1. Discretization of state equation -- 4.4.2. Discretization of the cost function

4.4.3. Case study of a scalar LQR problem -- 4.5. Predictive optimal control -- 4.5.1. Basic principle -- 4.5.2. Recurrence equation of a process based on q-1 operator -- 4.5.3. General formulation of a prediction model -- 4.5.4. Solution and structure of predictive optimal control -- 4.6. Exercises and solutions -- 5. Stochastic Optimal Digital Feedback Control -- 5.1. Introduction to stochastic dynamic processes -- 5.2. Stochastic LQR -- 5.2.1. Formulation -- 5.2.2. Resolution of the stochastic H-J-B equation -- 5.2.3. Block diagram of stochastic LQR -- 5.2.4. Properties of stochastic LQR -- 5.3. Discrete Kalman filter -- 5.3.1. Scientific context and hypotheses -- 5.3.2. Notations -- 5.3.3. Closed-loop algorithmic diagram -- 5.4. Linear Quadratic Gaussian regulator -- 5.4.1. Context -- 5.4.2. Separation principle -- 5.4.3. Algorithmic diagram of LQG regulator -- 5.5. Exercises and solutions -- 6. Deployed Matlab/GUI Platform for the Design and Virtual Simulation of Stochastic Optimal Control Systems -- 6.1. Introduction to OPCODE (Optimal Control Design) platform -- 6.1.1. Scientific context -- 6.1.2. Detailed presentation methodology -- 6.2. Fundamental OPCODE design elements -- 6.2.1. Elements of deterministic optimal control -- 6.2.2. Elements of stochastic optimal control -- 6.3. Design of OPCODE using SFC -- 6.3.1. Architectural diagram -- 6.3.2. Implementation of SFC -- 6.4. Software implementation -- 6.5. Examples of OPCODE use -- 6.5.1. Design of deterministic optimal control systems -- 6.5.2. Design of stochastic optimal control systems -- 6.6. Production of deployed OPCODE. EXE application -- 6.6.1. Interest of Matlab/GUI application deployment -- 6.6.2. Deployment methodology -- 6.6.3. Tests of deployed OPCODE. EXE application -- 6.7. Exercises and solutions -- Part 3 Remotely Operated Feedback Control Systems via the Internet

7. Elements of Remotely Operated Feedback Control Systems via the Internet -- 7.1. Problem statement -- 7.2. Infrastructural topologies -- 7.2.1. Basic topology -- 7.2.2. Advanced topologies -- 7.3. Remotely operated laboratories via the Internet -- 7.3.1. Comparison between classical and remotely operated laboratories -- 7.3.2. Infrastructures on the server side of a remotely operated laboratory -- 7.3.3. Criteria for the creation of a remotely operated laboratory -- 7.4. Exercises and solutions -- 8. Remotely Operated Automation Laboratory via the Internet -- 8.1. Introduction to remotely operated automation laboratory -- 8.1.1. Creation context -- 8.1.2. Didactic context -- 8.1.3. Specifications -- 8.2. Design and implementation of the experimental system -- 8.2.1. Descriptive diagrams -- 8.2.2. Dynamic model of the real power lighting system -- 8.2.3. Dynamic model of the PID controller for power lighting -- 8.2.4. MMMI-aided Labview application -- 8.3. Topology of the remotely operated automation laboratory -- 8.3.1. Hardware infrastructure -- 8.3.2. Specialized infrastructure on the server side -- 8.3.3. Infrastructure on the remote operator side -- 8.4. Use of a remotely operated laboratory via the Internet -- 8.4.1. Procedure instruction sheet -- 8.4.2. Samples of test results obtained with REOPAULAB -- 8.5. Exercises and solutions -- Appendices -- Appendix 1: Table of z-transforms -- T0: Sampling period -- Appendix 2: Matlab Elements Used in this Book -- Appendix 3: Discretization of Transfer Functions -- A3.1. Discretization of transfer functions of dynamic processes -- A3.2. Discretization of transfer functions of analog controllers -- Bibliography -- Index -- Other titles from iSTE in Systems and Industrial Engineering - Robotics -- EULA