book

Co-design Approaches to Dependable Networked Control Systems

by Daniel Simon, Ye-Qiong Song, Christophe Aubrun

February 2010

Intermediate to advanced

336 pages

8h 38m

English

Wiley

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Cover
Title Page
Copyright
Foreword
Introduction and Problem Statement
I.1. Networked control systems and control design challengesI.2. Control design: from continuous time to networked implementationI.3. Timing parameter assignmentI.4. Control and task/message schedulingI.5. Diagnosis and fault tolerance in NCSI.6. Co-design approachesI.7. Outline of the bookI.8. Bibliography
Chapter 1: Preliminary Notions and State of the Art
1.1. Overview1.2. Preliminary notions on real-time scheduling1.2.1. Some basic results on classic task model scheduling1.2.1.1. Fixed priority scheduling1.2.1.2. EDF scheduling1.2.1.3. Discussion1.2.2. (m, k)-firm model1.3. Control aware computing1.3.1. Off-line approaches1.3.2. Quality of Service and flexible scheduling1.4. Feedback-scheduling basics1.4.1. Control of the computing resource1.4.1.1. Control structure1.4.1.2. Sensors and actuators1.4.1.3. Control design and implementation1.4.2. Examples1.4.2.1. Feedback scheduling a web server1.4.2.2. Optimal control-based feedback scheduling1.4.2.3. Feasibility: feedback-scheduler implementation for robot control1.4.2.3.1. Plant modeling and control structure1.4.2.3.2. Scheduling controller design1.4.2.3.3. Implementation of the feedback scheduler1.5. Fault diagnosis of NCS with network-induced effects1.5.1. Fault diagnosis of NCS with network-induced time delays1.5.1.1. Low-pass post-filtering1.5.1.2. Structure matrix of network-induced time delay1.5.1.3. Robust deadbeat fault filter1.5.1.4. Other work1.5.2. Fault diagnosis of NCS with packet losses1.5.2.1. Deterministic packet losses1.5.2.2. Stochastic packet losses1.5.3. Fault diagnosis of NCS with limited communication1.5.4. Fault-tolerant control of NCS1.6. Summary1.7. Bibliography
Chapter 2: Computing-aware Control
2.1. Overview2.2. Robust control w.r.t. computing and networking-induced latencies2.2.1. Introduction2.2.2. What happens when delays appear?2.2.2.1. Initial conditions2.2.2.2. Infinite dimensional systems2.2.3. Delay models2.2.4. Stability analysis of TDS using Lyapunov theory2.2.4.1. The second method2.2.4.2. The Lyapunov–Razumikhin approach2.2.4.3. The Lyapunov–Krasovskii approach2.2.5. Summary: time-delay systems and networking2.3. Weakly hard constraints2.3.1. Problem definition2.3.2. Notion of accelerable control2.3.3. Design of accelerable controllers2.3.4. Accelerable LQR design for LTI systems2.3.5. Kalman filtering and accelerability2.3.6. Robustifying feedback scheduling using weakly hard scheduling concepts2.3.7. Application to the attitude control of a quadrotor2.4. LPV adaptive variable sampling2.4.1. A polytopic discrete-plant model2.4.2. Performance specification2.4.3. LPV/H∞ control design2.4.4. Experimental assessment2.5. Summary2.6. Bibliography
Chapter 3: QoC-aware Dynamic Network QoS Adaptation
3.1. Overview3.2. Dynamic CAN message priority allocation according to the control application needs3.2.1. Context of the study3.2.1.1. The considered process control application3.2.1.2. Control performance evaluation3.2.1.3. The implementation through a network3.2.1.3.1. Structure3.2.1.3.2. Choice of the sampling period3.2.1.3.3. Considering the network CAN3.2.1.4. Evaluation of the influence of the network on the behavior of the process control application3.2.1.4.1. Dedicated network CAN3.2.1.4.2. Shared network CAN: considering static priorities and showing their inadequacy3.2.1.5. Idea of hybrid priority schemes: general considerations3.2.1.5.1. The identifier (ID) field and the scheduling execution3.2.1.5.2. Cohabitation of flows with constant needs and flows of process control applications (variable needs)3.2.1.5.3. Toward making dynamic priorities3.2.2. Three hybrid priority schemes3.2.2.1. hp scheme3.2.2.2. (hp+sts) scheme3.2.2.3. (hp+dts) scheme3.2.2.3.1. Main ideas3.2.2.3.2. Algorithm for computing the dynamic priority at any sampling instant tk3.2.3. Study of the three schemes based on hybrid priorities3.2.3.1. Study conditions3.2.3.2. hp scheme3.2.3.3. (hp+sts) scheme3.2.3.4. (hp+dts) scheme3.2.4. QoC visualization3.2.5. Comment3.3. Bandwidth allocation control for switched Ethernet networks3.3.1. NCS performance analysis3.3.2. NCS modeling3.3.2.1. Introduction3.3.2.2. Network modeling3.3.2.3. System modeling3.3.2.4. Controller modeling3.3.3. Network adaptation mechanism3.3.4. Example3.3.4.1. Maximum delay computation3.3.4.2. Results3.4. Conclusion3.5. Bibliography
Chapter 4: Plant-state-based Feedback Scheduling
4.1. Overview4.2. Adaptive scheduling and varying sampling robust control4.2.1. Extended elastic tasks controller4.2.2. Case study4.3. MPC-based integrated control and scheduling4.3.1. Resource constrained systems4.3.2. Optimal integrated control and scheduling of resource constrained systems4.4. A convex optimization approach to feedback scheduling4.4.1. Problem formulation4.4.2. Cost function definition and approximation4.4.2.1. Cost function definition4.4.2.2. Introductory example: quadrotor attitude control4.4.3. Optimal sampling period selection4.4.3.1. Problem formulation4.4.3.2. Problem solving4.4.3.3. Feedback-scheduling algorithm deployment4.4.4. Application to the attitude control of a quadrotor4.5. Control and real-time scheduling co-design via a LPV approach4.5.1. A LPV feedback scheduler sensible to the plant’s closed-loop performances4.5.2. Application to a robot-arm control4.5.2.1. Performance evaluation of the control tasks in view of optimal resource distribution4.5.2.1.1. Discussion4.5.2.2. Simulation with TrueTime4.5.2.3. Feasibility and possible extensions4.6. Summary4.7. Bibliography
Chapter 5: Overload Management Through Selective Data Dropping
5.1. Introduction5.1.1. System architecture5.1.2. Problem statement5.2. Scheduling under (m, k)-firm constraint5.2.1. Dynamic scheduling policy under (m,k)-firm constraints5.2.2. Static scheduling policy under (m,k)-firm constraints and schedulability issue5.2.3. Static scheduling under (m, k)-constraints and mechanical words5.2.4. Sufficient condition for schedulability assessment under (m,k)-pattern defined by a mechanical word5.2.5. Systematic dropping policy in control applications5.3. Stability analysis of a multidimensional system5.3.1. Generic model5.3.2. Example of multidimensional system5.3.2.1. Sampling period definition5.3.2.2. Controller parameters5.3.3. Stability condition5.4. Optimized control and scheduling co-design5.4.1. Optimal control and individual cost function5.4.2. Global optimization5.4.3. Case study5.4.3.1. Plants and controllers5.4.3.2. Scheduling parameters5.4.3.3. Optimal controller5.4.3.4. Simulation scenario5.4.3.5. Simulation results for hard real-time constraints5.4.3.6. Simulation results for (m, k)-firm constraints5.5. Plant-state-triggered control and scheduling adaptation and optimization5.5.1. Closed-loop stability of switching systems5.5.2. On-line plant state detection5.5.3. Global optimization of control tasks taking into account the plant state5.5.4. Case study5.5.4.1. Simulation scenario5.5.4.2. Observed performance5.5.4.3. Summary5.6. Conclusions5.7. Bibliography

Chapter 6: Fault Detection and Isolation, Fault Tolerant Control
6.1. Introduction6.2. FDI and FTC6.2.1. Introduction to diagnosis6.2.2. Quantitative model-based residuals6.2.2.1. Parity relations6.2.2.2. Observers bank6.2.3. Example6.2.3.1. The system-residual generation6.2.3.2. Observer-based residuals6.2.4. Diagnostic summary6.2.5. Introduction to FTC6.3. Networked-induced effects6.3.1. Example of network-induced drawbacks6.3.2. Modeling data dropouts6.3.3. Modeling network delays6.4. Pragmatic solutions6.4.1. Data synchronization6.4.1.1. Clock synchronization6.4.1.2. Data reconstruction6.4.1.3. Example6.4.2. Data loss and diagnostic blocking6.5. Advanced techniques6.5.1. Residual generation with transmission delay6.5.2. Adaptive thresholding6.5.2.1. Optimization-based approach for threshold selection6.5.2.2. Network calculus-based thresholding6.5.3. Fault isolation filter design in the presence of packet dropouts6.5.4. Estimation and diagnosis with data loss6.5.4.1. Problem formulation6.5.4.2. Kalman filter with partial data loss6.6. Conclusion and perspectives6.7. Bibliography
Chapter 7: Implementation: Control and Diagnosis for an Unmanned Aerial Vehicle
7.1. Introduction7.2. The quadrotor model, control and diagnosis7.2.1. The system7.2.2. The physical system model7.2.2.1. Introduction to quaternions7.2.2.2. The quadrotor model7.2.2.3. The inertial measurement unit (IMU) model7.2.3. The attitude control7.2.3.1. Nonlinear control7.2.3.2. Linear quadratic control7.2.4. The attitude observer7.2.4.1. Nonlinear observer7.2.4.2. Extended Kalman filter7.2.4.3. Simulation results7.2.5. The quadrotor diagnosis7.2.5.1. Sensor diagnosis7.2.5.2. Actuator diagnosis7.3. Simulation of the network7.3.1. Architecture of the networked control system7.3.2. Network design7.3.3. Tool implemented in the network simulation7.4. Hardware in the loop architecture7.4.1. The ORCCAD approach7.4.2. Quadrotor simulation setup7.5. Experiments and results7.5.1. Basic attitude control7.5.2. Packet loss7.5.2.1. Pragmatic solution7.5.2.2. (m, k)-firm solutions7.5.2.3. Dynamic priorities7.5.2.4. Extended Kalman filter7.5.3. Hardware-in-the loop experiment7.5.3.1. Basic scenario7.5.3.2. Packet loss7.5.3.3. Sensor failure7.6. Summary7.7. Bibliography
Glossary and Acronyms
List of Authors
Index

Content preview from Co-design Approaches to Dependable Networked Control Systems

Introduction and Problem Statement 1

Networked control systems (NCS) are feedback control systems wherein the control loops are closed over a shared network. Control and feedback signals are transmitted among the system’s components as information flows through a network, as depicted in figure I.1. A fully featured NCS is made up of four kinds of components to close the control loops: sensors to collect information on the controlled plant’s state, controllers to provide decisions and commands, actuators to apply the control signals and communication networks to enable communications between the NCS components.

Figure I.1. Typical NCS architecture

Compared with conventional point-to-point control systems, the advantages of NCS are lighter wiring, lower installation costs, and greater abilities in diagnosis, reconfigurability and maintenance. Furthermore, the technologies used in computer and industrial networks, both wired and wireless, have progressed rapidly providing high bandwidth, quality of service (QoS) guarantees and low communication costs. Because of these distinctive benefits, typical application of these systems nowadays ranges over various fields of industry and services. X-by-wire automotive systems, coordinated control of swarms of mobile robotics and advanced aircraft on-board control and housekeeping are examples of NCS usage in the field of embedded systems. Large-scale ...

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Co-design Approaches to Dependable Networked Control Systems

by Daniel Simon, Ye-Qiong Song, Christophe Aubrun

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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