Foreword |
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xiii | |
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Introduction and Problem Statement |
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1 | (1) |
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Networked control systems and control design challenges |
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2 | (2) |
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Control design: from continuous time to networked implementation |
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4 | (2) |
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Timing parameter assignment |
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6 | (2) |
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Control and task/message scheduling |
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8 | (2) |
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Diagnosis and fault tolerance in NCS |
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10 | (1) |
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11 | (1) |
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12 | (3) |
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15 | (4) |
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Preliminary Notions and State of the Art |
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19 | (44) |
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19 | (1) |
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Preliminary notions on real-time scheduling |
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20 | (6) |
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Some basic results on classic task model scheduling |
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21 | (1) |
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Fixed priority scheduling |
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22 | (1) |
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23 | (1) |
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23 | (1) |
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24 | (2) |
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26 | (4) |
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27 | (1) |
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Quality of Service and flexible scheduling |
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28 | (2) |
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Feedback-scheduling basics |
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30 | (13) |
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Control of the computing resource |
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32 | (1) |
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32 | (1) |
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32 | (1) |
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Control design and implementation |
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33 | (2) |
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35 | (1) |
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Feedback scheduling a web server |
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35 | (1) |
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Optimal control-based feedback scheduling |
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36 | (3) |
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Feasibility: feedback-scheduler implementation for robot control |
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39 | (4) |
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Fault diagnosis of NCS with network-induced effects |
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43 | (10) |
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Fault diagnosis of NCS with network-induced time delays |
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44 | (1) |
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44 | (2) |
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Structure matrix of network-induced time delay |
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46 | (1) |
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Robust deadbeat fault filter |
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47 | (2) |
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49 | (1) |
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Fault diagnosis of NCS with packet losses |
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50 | (1) |
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Deterministic packet losses |
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50 | (1) |
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50 | (1) |
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Fault diagnosis of NCS with limited communication |
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51 | (1) |
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Fault-tolerant control of NCS |
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52 | (1) |
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53 | (1) |
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53 | (10) |
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63 | (42) |
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63 | (2) |
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Robust control w.r.t. computing and networking-induced latencies |
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65 | (11) |
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65 | (2) |
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What happens when delays appear? |
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67 | (1) |
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67 | (1) |
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Infinite dimensional systems |
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68 | (2) |
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70 | (1) |
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Stability analysis of TDS using Lyapunov theory |
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71 | (1) |
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71 | (1) |
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The Lyapunov-Razumikhin approach |
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72 | (1) |
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The Lyapunov-Krasovskii approach |
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73 | (2) |
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Summary: time-delay systems and networking |
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75 | (1) |
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76 | (13) |
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77 | (2) |
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Notion of accelerable control |
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79 | (1) |
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Design of accelerable controllers |
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79 | (1) |
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Accelerable LQR design for LTI systems |
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80 | (2) |
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Kalman filtering and accelerability |
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82 | (1) |
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Robustifying feedback scheduling using weakly hard scheduling concepts |
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83 | (2) |
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Application to the attitude control of a quadrotor |
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85 | (4) |
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LPV adaptive variable sampling |
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89 | (9) |
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A polytopic discrete-plant model |
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90 | (2) |
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Performance specification |
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92 | (1) |
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93 | (1) |
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94 | (4) |
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98 | (1) |
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99 | (6) |
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QoC-aware Dynamic Network QoS adaptation |
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105 | (44) |
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105 | (2) |
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Dynamic CAN message priority allocation according to the control application needs |
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107 | (25) |
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107 | (1) |
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The considered process control application |
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107 | (1) |
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Control performance evaluation |
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108 | (1) |
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The implementation through a network |
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108 | (2) |
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Evaluation of the influence of the network on the behavior of the process control application |
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110 | (1) |
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Idea of hybrid priority schemes: general considerations |
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111 | (3) |
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Three hybrid priority schemes |
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114 | (1) |
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114 | (1) |
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115 | (1) |
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116 | (3) |
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Study of the three schemes based on hybrid priorities |
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119 | (1) |
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119 | (1) |
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120 | (5) |
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125 | (3) |
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128 | (1) |
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128 | (1) |
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129 | (3) |
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Bandwidth allocation control for switched Ethernet networks |
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132 | (12) |
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134 | (1) |
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134 | (1) |
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134 | (1) |
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135 | (3) |
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138 | (1) |
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139 | (2) |
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Network adaptation mechanism |
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141 | (1) |
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141 | (1) |
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Maximum delay computation |
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141 | (1) |
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142 | (2) |
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144 | (1) |
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145 | (4) |
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Plant-state-based Feedback Scheduling |
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149 | (36) |
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149 | (2) |
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Adaptive scheduling and varying sampling robust control |
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151 | (5) |
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Extended elastic tasks controller |
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152 | (1) |
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153 | (3) |
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MPC-based integrated control and scheduling |
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156 | (6) |
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Resource constrained systems |
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157 | (3) |
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Optimal integrated control and scheduling of resource constrained systems |
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160 | (2) |
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A convex optimization approach to feedback scheduling |
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162 | (8) |
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162 | (2) |
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Cost function definition and approximation |
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164 | (1) |
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164 | (1) |
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Introductory example: quadrotor attitude control |
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165 | (1) |
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Optimal sampling period selection |
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166 | (1) |
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166 | (1) |
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167 | (1) |
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Feedback-scheduling algorithm deployment |
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167 | (1) |
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Application to the attitude control of a quadrotor |
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168 | (2) |
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Control and real-time scheduling co-design via a LPV approach |
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170 | (7) |
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A LPV feedback scheduler sensible to the plant's closed-loop performances |
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171 | (3) |
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Application to a robot-arm control |
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174 | (1) |
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Performance evaluation of the control tasks in view of optimal resource distribution |
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174 | (1) |
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175 | (2) |
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Feasibility and possible extensions |
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177 | (1) |
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177 | (4) |
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181 | (4) |
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Overload Management Through Selective Data Dropping |
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185 | (38) |
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185 | (3) |
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186 | (2) |
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188 | (1) |
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Scheduling under (m, k)-firm constraint |
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188 | (5) |
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Dynamic scheduling policy under (m, k)-firm constraints |
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189 | (1) |
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Static scheduling policy under (m, k)-firm constraints and schedulability issue |
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189 | (1) |
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Static scheduling under (m, k)-constraints and mechanical words |
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190 | (1) |
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Sufficient condition for schedulability assessment under (m, k)-pattern defined by a mechanical word |
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191 | (1) |
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Systematic dropping policy in control applications |
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192 | (1) |
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Stability analysis of a multidimensional system |
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193 | (4) |
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193 | (1) |
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Example of multidimensional system |
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194 | (1) |
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Sampling period definition |
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195 | (1) |
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195 | (1) |
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195 | (2) |
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Optimized control and scheduling co-design |
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197 | (12) |
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Optimal control and individual cost function |
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198 | (2) |
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200 | (1) |
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201 | (2) |
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203 | (1) |
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203 | (1) |
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203 | (1) |
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204 | (1) |
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Simulation results for hard real-time constraints |
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204 | (1) |
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Simulation results for (m, k)-firm constraints |
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205 | (4) |
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Plant-state-triggered control and scheduling adaptation and optimization |
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209 | (9) |
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Closed-loop stability of switching systems |
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210 | (1) |
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On-line plant state detection |
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210 | (1) |
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Global optimization of control tasks taking into account the plant state |
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211 | (2) |
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213 | (1) |
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214 | (3) |
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217 | (1) |
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218 | (1) |
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218 | (2) |
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220 | (3) |
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Fault Detection and Isolation, Fault Tolerant Control |
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223 | (44) |
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223 | (1) |
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224 | (14) |
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Introduction to diagnosis |
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224 | (2) |
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Quantitative model-based residuals |
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226 | (2) |
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228 | (1) |
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229 | (2) |
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231 | (1) |
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The system-residual generation |
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231 | (2) |
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233 | (2) |
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235 | (1) |
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236 | (2) |
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Networked-induced effects |
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238 | (5) |
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Example of network-induced drawbacks |
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239 | (1) |
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240 | (2) |
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242 | (1) |
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243 | (5) |
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244 | (1) |
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244 | (1) |
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245 | (1) |
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246 | (1) |
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Data loss and diagnostic blocking |
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247 | (1) |
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248 | (14) |
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Residual generation with transmission delay |
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248 | (1) |
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249 | (1) |
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Optimization-based approach for threshold selection |
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250 | (1) |
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Network calculus-based thresholding |
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251 | (5) |
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Fault isolation filter design in the presence of packet dropouts |
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256 | (3) |
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Estimation and diagnosis with data loss |
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259 | (1) |
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259 | (1) |
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Kalman filter with partial data loss |
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260 | (2) |
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Conclusion and perspectives |
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262 | (1) |
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262 | (5) |
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Implementation: Control and Diagnosis for an Unmanned Aerial Vehicle |
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267 | (38) |
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267 | (2) |
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The quadrotor model, control and diagnosis |
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269 | (13) |
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269 | (1) |
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The physical system model |
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270 | (1) |
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Introduction to quaternions |
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270 | (1) |
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271 | (2) |
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The inertial measurement unit (IMU) model |
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273 | (1) |
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274 | (1) |
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274 | (1) |
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274 | (2) |
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276 | (1) |
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276 | (1) |
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277 | (2) |
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279 | (1) |
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279 | (1) |
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279 | (3) |
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282 | (1) |
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Simulation of the network |
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282 | (3) |
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Architecture of the networked control system |
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282 | (2) |
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284 | (1) |
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Tool implemented in the network simulation |
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285 | (1) |
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Hardware in the loop architecture |
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285 | (5) |
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286 | (2) |
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Quadrotor simulation setup |
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288 | (2) |
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290 | (12) |
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290 | (1) |
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291 | (1) |
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291 | (1) |
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292 | (3) |
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295 | (2) |
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297 | (1) |
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Hardware-in-the loop experiment |
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298 | (1) |
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298 | (1) |
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299 | (1) |
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299 | (3) |
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302 | (1) |
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303 | (2) |
Glossary and Acronyms |
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305 | (4) |
List of Authors |
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309 | (4) |
Index |
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313 | |