Acknowledgements	5
	Contents	7
	List of Figures	10
	List of Tables	12
	Nomenclature	13
	1 Introduction	16
	1.1 Background and motivation	16
	1.1.1 FDI and FTC in complex industrial systems	17
	1.1.2 PnP control concept	20
	1.2 Objective of the work	21
	1.3 Outline of the thesis	22
	2 Basics of Process Monitoring Techniques	24
	2.1 Mathematical description of automatic control processes	24
	2.1.1 Description of nominal system behavior	24
	2.1.2 Coprime factorization technique	25
	2.1.3 Description of systems with disturbances	26
	2.1.4 Description of systems with faults	26
	2.2 Model-based residual generation techniques	27
	2.2.1 Kernel representation and fault detection filter	27
	2.2.2 Diagnostic observer	28
	2.2.3 Parity space approach	29
	2.2.4 Interconnections between DO and PS schemes	31
	2.3 Data-driven residual generation techniques	32
	2.3.1 SIM-aided process monitoring	32
	2.3.2 Data-driven design of residual generator	33
	2.4 Residual evaluation and decision making	35
	2.4.1 Residual evaluation strategies	36
	2.4.2 Threshold setting and decision making	37
	2.5 Multivariate statistical process monitoring techniques	37
	2.6 Concluding remarks	38
	3 Basics of FTC Structure	39
	3.1 Standard feedback control structure	39
	3.2 Well-posedness and internal stability	40
	3.2.1 Well-posedness	40
	3.2.2 Internal stability	41
	3.3 Image representation and state feedback control	43
	3.4 Parameterization of stabilizing controllers	44
	3.5 Model uncertainty and robustness	47
	3.5.1 Small gain theorem	47
	3.5.2 Coprime factor uncertainty	48
	3.6 The fault-tolerant control architecture	51
	3.7 Concluding remarks	53
	4 PnP Process Monitoring and Control Architecture	54
	4.1 Problem formulation	54
	4.2 Scalability of feedback control systems	56
	4.3 The PnP process monitoring and control architecture	59
	4.3.1 The PnP-PMCA	59
	4.3.2 Comparison with the fault-tolerant control architecture	61
	4.3.3 Industrial implementation of the PnP-PMCA	63
	4.4 PnP control strategies for new actuators and sensors	66
	4.4.1 PnP control strategy for new actuators	66
	4.4.2 PnP control strategy for new sensors	67
	4.5 Concluding remarks	68
	5 Real-Time Configuration Techniques for PnP Process Monitoring	69
	5.1 Adaptive observer-based configuration	70
	5.1.1 The canonical forms of LTI state-space systems	70
	5.1.2 Adaptive configuration approach	72
	5.2 Iterative configuration approach	79
	5.2.1 The input/output normal form	81
	5.2.2 Iterative configuration approach	84
	5.3 Process monitoring with deterministic disturbance	91
	5.3.1 Preliminaries related to the model-based solution	91
	5.3.2 A data-driven process monitoring approach	93
	5.4 Concluding remarks	95
	6 Real-Time Configuration Techniques for PnP Performance Optimization	96
	6.1 Control performance assessment system	96
	6.2 Internal stability of the PnP-PMCA	99
	6.2.1 Closed-loop dynamics of the PnP-PMCA	99
	6.2.2 Constraints on closed-loop internal stability	101
	6.3 Control performance optimization in PnP-PMCA	105
	6.3.1 Iterative robustness optimization	105
	6.3.2 Iterative tracking performance optimization	112
	6.4 Convergence analysis	117
	6.5 Concluding remarks	118
	7 Benchmark Study and Real-Time Implementation	120
	7.1 Application to rolling mill benchmark	120
	7.1.1 General description of rolling mill system	120
	7.1.2 PnP process monitoring and disturbance compensation system	124
	7.1.3 Roll eccentricity monitoring and compensation module	125
	7.1.4 Case study and simulation results	131
	7.2 Real-time implementation on BLDC motor test rig	137
	7.2.1 Description of the test rig	137
	7.2.2 HIL simulation result	139
	7.3 Concluding remarks	143
	8 Conclusions and Future Work	145
	A Proof of Theorem 4.2	147
	Bibliography	150