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 |  موضوع: كتاب Dynamic Modeling, Predictive Control and Performance Monitoring الجمعة 22 نوفمبر 2024, 1:04 am | |
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أحضرت لكم كتاب
Dynamic Modeling, Predictive Control and Performance Monitoring
A Data-driven Subspace Approach
Biao Huang, Ramesh Kadali
و المحتوى كما يلي :
Contents
Notation XIX
1
Introduction
1
1.1 An Overview of This Book .
1
1.2 Main Features of This Book .
4
1.3 Organization of This Book .
4
Part I Dynamic Modeling through Subspace Identification
2
System Identification: Conventional Approach .
9
2.1 Introduction .
9
2.2 Discrete-time Systems .
9
2.2.1 Finite Difference Models
10
2.2.2 Exact Discretization for Linear Systems .
10
2.2.3 Backshift Operator and Discrete-time Transfer Functions
11
2.3 An Example of System Identification: ARX Modeling .
12
2.4 Persistent Excitation in Input Signal
13
2.5 Model Structures .
15
2.5.1 Prediction Error Model (PEM) .
15
2.5.2 AutoRegressive with Exogenous Input Model (ARX)
15
2.5.3 AutoRegressive Moving Average with Exogenous
Input Model (ARMAX) .
16
2.5.4 Box-Jenkins Model (BJ)
17
2.5.5 Output Error Model (OE) .
17
2.5.6 MISO (Multi-input and Single-output) Prediction
Error Model .
18
2.5.7 State Space Model
18
2.6 Prediction Error Method
19
2.6.1 Motivation
19
2.6.2 Optimal Prediction .
21
2.6.3 Prediction Error Method
24
XIV
Contents
2.7 Closed-loop Identification
25
2.7.1 Identifiability without External Excitations
26
2.7.2 Direct Closed-loop Identification .
27
2.7.3 Indirect Closed-loop Identification
28
2.7.4 Joint Input-output Closed-loop Identification
29
2.8 Summary
29
3
Open-loop Subspace Identification
31
3.1 Introduction .
31
3.2 Subspace Matrices Description .
31
3.2.1 State Space Models .
31
3.2.2 Notations and Subspace Equations .
33
3.3 Open-loop Subspace Identification Methods
40
3.4 Regression Analysis Approach
40
3.5 Projection Approach and N4SID .
43
3.5.1 Projections
43
3.5.2 Non-steady-state Kalman Filters .
44
3.5.3 Projection Approach for Subspace Identification
46
3.6 QR Factorization and MOESP .
48
3.7 Statistical Approach and CVA .
49
3.7.1 CVA Approach .
49
3.7.2 Determination of System Order
51
3.8 Instrument-variable Methods and EIV Subspace Identification
51
3.9 Summary
53
4
Closed-loop Subspace Identification .
55
4.1 Introduction .
55
4.2 Review of Closed-loop Subspace Identification Methods .
57
4.2.1 N4SID Approach .
57
4.2.2 Joint Input-Output Approach
59
4.2.3 ARX Prediction Approach .
60
4.2.4 An Innovation Estimation Approach
61
4.3 An Orthogonal Projection Approach
63
4.3.1 A Solution through Orthogonal Projection .
63
4.3.2 The Problem of Biased Estimation and the Solution
67
4.3.3 Model Extraction through Kalman Filter State Sequence
69
4.3.4 Extension to Error-in-variable (EIV) Systems
71
4.3.5 Simulation .
72
4.4 Summary
78
5
Identification of Dynamic Matrix and Noise Model Using
Closed-loop Data
79
5.1 Introduction .
79
5.2 Estimation of Process Dynamic Matrix and Noise Model
81
5.2.1 Estimation of Dynamic Matrix of the Process
84
5.2.2 Estimation of the Noise Model .
85
Contents
XV
5.3 Some Guidelines for the Practical Implementation of the
Algorithm .
86
5.4 Extension to the Case of Measured Disturbance Variables .
87
5.5 Closed-loop Simulations .
89
5.5.1 Univariate System
89
5.5.2 Multivariate System
90
5.6 Identification of the Dynamic Matrix: Pilot-scale
Experimental Evaluation
94
5.7 Summary
96
Part II Predictive Control
6
Model Predictive Control: Conventional Approach 101
6.1 Introduction . 101
6.2 Understanding MPC 102
6.3 Fundamentals of MPC
103
6.3.1 Process and Disturbance Models . 103
6.3.2 Predictions 105
6.3.3 Free and Forced Response . 106
6.3.4 Objective Function . 107
6.3.5 Constraints 107
6.3.6 Control Law . 108
6.4 Dynamic Matrix Control (DMC) . 108
6.4.1 The Prediction Model . 109
6.4.2 Unconstrained DMC Design . 111
6.4.3 Penalizing the Control Action 111
6.4.4 Handling Disturbances in DMC 112
6.4.5 Multivariate Dynamic Matrix Control . 113
6.4.6 Hard Constrained DMC . 115
6.4.7 Economic Optimization . 116
6.5 Generalized Predictive Control (GPC) 117
6.6 Summary 118
7
Data-driven Subspace Approach to Predictive Control 121
7.1 Introduction . 121
7.2 Predictive Controller Design from Subspace Matrices 122
7.2.1 Inclusion of Integral Action 125
7.2.2 Inclusion of Feedforward Control . 127
7.2.3 Constraint Handling 128
7.3 Tuning the Noise Model . 130
7.4 Simulations 132
7.5 Experiment on a Pilot-scale Process 138
7.6 Summary 141
XVI
Contents
Part III Control Performance Monitoring
8
Control Loop Performance Assessment: Conventional
Approach . 145
8.1 Introduction . 145
8.2 SISO Feedback Control Performance Assessment . 146
8.3 MIMO Feedback Control Performance Assessment 150
8.4 Summary 155
9
State-of-the-art MPC Performance Monitoring . 157
9.1 Introduction . 157
9.2 MPC Performance Monitoring: Model-based Approach 158
9.2.1 Minimum-variance Control Benchmark 158
9.2.2 LQG/MPC Benchmark . 159
9.2.3 Model-based Simulation Approach 160
9.2.4 Designed/Historical vs Achieved 161
9.2.5 Historical Covariance Benchmark . 161
9.2.6 MPC Performance Monitoring through Model Validation 162
9.3 MPC Performance Monitoring: Model-free Approach 165
9.3.1 Impulse-Response Curvature . 165
9.3.2 Prediction-error Approach . 166
9.3.3 Markov Chain Approach 166
9.4 MPC Economic Performance Assessment and Tuning . 167
9.5 Probabilistic Inference for Diagnosis of MPC Performance . 171
9.5.1 Bayesian Network for Diagnosis 171
9.5.2 Decision Making in Performance Diagnosis 173
9.6 Summary 175
10 Subspace Approach to MIMO Feedback Control
Performance Assessment 177
10.1 Introduction . 177
10.2 Subspace Matrices and Their Estimation 179
10.2.1 Revisit of Important Subspace Matrices . 179
10.2.2 Estimation of Subspace Matrices from Open-loop Data 180
10.3 Estimation of MVC-benchmark from Input/Output Data 181
10.3.1 Closed-loop Subspace Expression of Process Response
under Feedback Control . 181
10.3.2 Estimation of MVC-benchmark Directly from
Input/Output Data . 183
10.4 Simulations and Application Example . 190
10.5 Summary 193
Contents
XVII
11 Prediction Error Approach to Feedback Control
Performance Assessment 195
11.1 Introduction . 195
11.2 Prediction Error Approach to Feedback Control Performance
Assessment 196
11.3 Subspace Algorithm for Multi-step Optimal Prediction Errors 201
11.3.1 Preliminary 201
11.3.2 Calculation of Multi-step Optimal Prediction Errors 202
11.3.3 Case Study 206
11.4 Summary 211
12 Performance Assessment with LQG-benchmark from
Closed-loop Data . 213
12.1 Introduction . 213
12.2 Obtaining LQG-benchmark from Feedback Closed-loop Data . 214
12.3 Obtaining LQG-benchmark with Measured Disturbances 217
12.4 Controller Performance Analysis 219
12.4.1 Case 1: Feedback Controller Acting on the Process
with Unmeasured Disturbances 219
12.4.2 Case 2: Feedforward Plus Feedback Controller Acting
on the Process . 220
12.4.3 Case 3: Feedback Controller Acting on the Process
with Measured Disturbances . 221
12.5 Summary of the Subspace Approach to the Calculation of
LQG-benchmark . 222
12.6 Simulations 223
12.7 Application on a Pilot-scale Process 224
12.8 Summary 227
References 229
Index . 237
Notation
[A, B, C, D]
Dynamic state space system matrices of the process
[Acl, Bcl, Ccl, Dcl]
Dynamic state space system matrices of the closed-loop
system
[Ac, Bc, Cc, Dc]
Dynamic state space system matrices of the controller
[Bm, Dm]
Dynamic state space system matrices corresponding to
the measured variables
−1 −1
C(z−¹) D(z−¹)
Polynomials in the backshift operator, z−
∆u
Incremental control moves vector over the control hori-
zon
yˆ
Predicted output trajectory vector over the prediction
horizon
yˆt
Predicted value of system output(s) at sampling instant
t
r
Setpoint (reference) trajectory vector over the prediction
horizon
y∗
Predicted free response trajectory vector over the pre-
diction horizon
G˜
p(z−¹)
Delay-free transfer function matrix of Gp
at
Integrated white noise
d
Process time delay for the univariate process or order of
the interactor matrix for multivariate process
E[ ]
Ef
Expectation operator
Future data Hankel matrix for et
Ei
Polynomial obtained in Diophantine expansion
Ep
Past data Hankel matrix for et
et
White noise (innovations) sequences
Fi
Markov parameter (or impulse response) matrix at ith
sample
fi
Impulse response coefficient at ith sample
Gm
Multivariate step response coefficient matrix correspond-
ing to the measured disturbance input at ith sample
Gcl(z−¹)
Transfer function representation of the closed-loop sys-
Identity matrix Optimization o
Kalman filt
= K − K∗
XX
Notation
Gc(z−¹)
Transfer function representation of the controller
Gs
State space representation of the controller
Gi
Multivariate step response coefficient matrix correspond-
ing to the deterministic input at ith sample
gi
Univariate step response coefficient at ith sample
Gl(z−¹)
Transfer function representation of the stochastic part of
the system or disturbance model
G
p(z−¹)
Transfer function representation of the deterministic part
of the system or process model
Gs
State space representation of the process
h
Dimension of measured disturbance(s)
cl
Lower triangular Toeplitz matrix of closed-loop system
defined as
deterministic Toeplitz matrix defined
Lower triangular Toeplitz matrix corresponding to the
measured disturbances defined as
L er triangular stochastic Toeplitz matrix defined as
ontaining
parameters
u
Q Non-negative definite weighting matr
R Non-negative definite weighting matr
R₁, R₂ Non-negative definite weighting mat jective function
Rf Future data Hankel matrix for setpoi
Rp Past data Hankel matrix for setpoint
rt System setpoint(s) at sampling insta
Sf Future data Hankel matrix for st
SN Dynamic matrix (with N block rows an of step-response coefficients
st Output(s) measurement noise at sam
U, S, V Matrices from singular value decomp
U₁, U₂ Left matrices obtained in singular va
Notation
XXI
Klqg
LQG state feedback gain
l
Dimension of system input(s)
LCL
Closed-loop subspace matrix from Ef
Uf
L L
Closed-loop subspace matrix from Rf
L
Uf
LCL
LCL
Closed-loop subspace matrix from Ef
→
Yf
LCL
Closed-loop subspace matrix from Rf
Yf
LCL
Closed-loop subspace matrix from W
C
l
Yf
Lₑ
Subspace matrix containing the noise mo Markov pa-
rameters; Lₑ
is shorthand representation of Hi
where i
is typically selected as N
Lm
rbances
Lu
Subspace matrix containing the process Markov param-
eters; Lu
is shorthand representation of Hi
where i is
typically selected as N
Lw
Subspace matrix corresponding to past inputs and out-
puts (or state)
Lb
Subspace matrix corresponding to the past inputs and
outputs (or state) in the presence of measured distur-
bances
K∗
Modified Kalman filter gain matrix
Notation
Uf
F matrix for ut
defined as
u2N −1
e d
u2N
. u2N +j−2
nkel matri
U
∗
Fu ata Ha x for measured inputs, u∗
, for
EIV systems; see also Uf
Up
P el matrix for ut
defined as
da
uN
. uN +j−2
ankel ma
U
∗
P ta H trix for measured inputs, u
, for
EIV systems; see also Up
ut
System input(s) at sampling instant t
u∗
M system input(s) at sampling instant t for EIV
stems
V₁, V₂
ght matrices obtained in singular value decomposition
Vf
r v ; see also Uf
Vp
Past data Hankel matrix for vt; see also Up
vt
Input measurement noise
W, W₁, W₂
N -negative definite weighting matrices
Process noise
c
f
atrix of the controller defined as
xc
xc
1
. xᶜ
−1
c
matrix of the controller defined as
cxj−1
Notation XXIII
Xf
e matrix defined as
xN
. xN +j−1
ate m
b
Futu atrix, when the system has measured dis-
t riables, defined as
xb
. xᵇ
−1
sed-l
cl
Future cl oop state matrix
Xp
e matrix defined as
x₀
. xj−₁
sta
xt
Syst tes(s) at sampling instant t
xs
Stochastic component of system states(s) at sampling
instant t
yc,t
Forced response of the process output
yf,t
Free response of the process output
Yf
Future data Hankel matrix for yt
Y
Future data Hankel matrix for measured outputs, yt∗,
for
EIV systems; see also Uf
Yp
Past data Hankel matrix for yt
Y
∗
Past data Hankel matrix for measured outputs, yt∗,
for
EIV systems; see also Up
yt
System output(s) at sampling instant t
yt∗
Measured system output(s) at sampling instant t
y
Deterministic component of the system output(s) at
sampling instant t
yt
Stochastic component of system output(s) at sampling
instant t
D(z)
F, F
Interactor matrix
Polynomials obtained in Diophantine expansion
(η
Polynomials obtained in Diophantine expansion
&fb
LQG-benchmark based controller performance indices
with respect to process output variance
(E)fb, (E)ff &fb
LQG-benchmark based controller performance indices
with respect to process input variance
(Iη)ᶠᵇ,
(Iη )ff &fb
LQG-benchmark based performance improvement in-
dices with respect to process output variance
(IE
)fb,
(IE
)ff &fb
LQG-benchmark based performance improvement in-
dices with respect to process input variance
Γ¯N
ΓN
with its first m rows removed
∆
Differencing operator (1
XXIV Notation
b
N
Extended observability matrix of the expanded system
with inputs and measured disturbances
cl
N
c
Extended observability matrix of the closed-loop system
N
Extended observability matrix of the controller
ΓN
E tended observability matrix of the process, defined as
=
CT (CA)T . (CAN−¹)T
T
γN
Markov parameters used in noise model tuning
λ
Weighting on the control effort in control objective func-
tions
ωi, λi, γi, ψi
Parameters for calculating input and output variances
for LQG benchmarking
Σₑ
Covariance of innovation (white noise) sequence et
Γ
N
ΓN
with its last m rows removed
(t + j
t)
j step ahead prediction from time instant t. For example,
yˆ(t + 2
t) is a two-step ahead prediction from time in-
stant t; for system identification, the prediction is based
on past inputs and outputs, while for predictive control,
the prediction is based on past outputs, past inputs, and
future inputs.
j step ahead prediction from time instant t−j.
ple, yˆ(t | t − 2) is a two-step ahead prediction
For exam-
time
instant
t) for additional explanations
A/BC
Oblique ojection
Subscript; the first column of subspace matrix/vector
starts from time t₁ and ends by time t₂.
If x is a vector,
for exa le, then
Index
2-norm measure of impulse response
coefficients, 197
AIC criterion, 51
Algorithm for estimating minimum
variance benchmark directly from
input-output data, 188
ARIMAX model, 117
ARMAX mode, 16
ARX model, 15
ARX-prediction based closed-loop
subspace identification, 60
Backshift operator, 11
Bayes rule, 172
Bayesian network for diagnosis, 171
Benchmark problem for closed-loop
subspace identification, 72
Box-Jenkins model, 17
Block-Hankel matrices, 37
Block-Toeplitz matrices, 39
Canonical variables, 49
Closed-loop estimation of dynamic
matrices, 79, 81, 84
Closed-loop estimation of noise model, 85
Closed-loop identification of EIV systems,
71
Closed-loop Markov parameter matrices,
202
Closed-loop potential, 166, 198
Closed-loop SIMPCA, CSIMPCA, 69
Closed-loop SOPIM, CSOPIM, 69
Closed-loop subspace identification
algorithm, 85
Closed-loop subspace matrices in relation
to open-loop subspace matrices, 182
Consistent estimation, 25
Constrained DMC, 115
Constraints, 107, 128
Control horizon, 107, 108
Control-loop performance assessment:
conventional approach, 145
Controller performance index, 188
Controller subspace, 69
Conventional MIMO control performance
assessment algorithm, 151
Conventional SISO feedback control
performance assessment algorithm,
149
Covariance of the prediction error, 197
CVA, 48, 49
Data-driven subspace algorithms to calculate multi-step optimal prediction
errors, 202
Decision making in performance
diagnosis, 173
Delay-free transfer function, 146
Designed vs. achieved benchmark, 161
Determination of model order, 51
Diophantine equations, 118, 146
Direct closed-loop identification method,
27
Discrete transfer function, 12
Disturbance model, 105
DMC prediction equation, 109
Dynamic Bayesian network, 175
Dynamic matrix, 110, 115238 Index
Dynamic matrix control, 108
Economic objective function, 168
Economic optimization, 116
Economic performance assessment and
tuning, 167
Economic performance index, 170
EIV, 63
EIV state space model, 52
EIV subspace identification, 51
Estimation of multi-step optimal
prediction errors, 202
Estimation of MVC Benchmark from
Input/Output Data, 183
Estimation of subspace matrices, 180
Exact discretization, 10
Feedback control invariance property,
148, 151
Feedback control invariant, 145
Feedforward control, 127
Finite step response model, 109
Forced response, 106
Free response, 106, 118
Frobenius norm, 42
Fundamentals of MPC, 103
Generalized likelihood ratio test, 164
Generalized predictive control, GPC, 117
Generalized singular value, 50
GPC control law, 118
Graphic model, 171
Guidelines for closed-loop estimation of
dynamic matrix, 86
Handling Disturbances in DMC, 112
Historical benchmark, 161
Historical covariance benchmark, 161
Impulse response curvature for performance monitoring, 165
Impulse response model, 104
Indirect closed-loop identification, 28
Innovation estimation approach, 61
Innovation form of state space model,
179
Instrument variable, 52, 67, 68
Instrument-variable methods, 51
Integral action, 125
Integrated white noise, 125
Interactor-matrix free methods for
control performance assessment,
196
Joint input-output closed-loop identification, 29
Kalman filter states, 69
Least squares, 70, 181
Linear matrix inequality (LMI) for MPC
economic performance analysis
(LMIPA), 167
Local approach for model validation, 162
LQG benchmark, 159
LQG benchmark from closed-loop data:
subspace approach, 214
LQG benchmark tradeoff curve, 217, 219
LQG benchmark variances of inputs, 216
LQG benchmark variances of outputs,
216
LQG benchmark with measured
disturbances, 217
LQG benchmark: data-driven subspace
approach, 213
LQG performance indices, 219, 220
LQG-benchmark based controller
performance analysis, 219
Markov chain approach for performance
monitoring, 166
Matrix inversion lemma, 84
Maximum likelihood, 50
Maximum likelihood ratio test, 164
Measured disturbances, 87
MIMO DMC problem formulation, 115
MIMO dynamic matrix, 115
MIMO feedback control performance
assessment: conventional approach,
150
Minimum variance benchmark in
subspace, 186
Minimum variance control, 145
Minimum variance control benchmark,
158
Minimum variance control law, 147
Minimum variance term, 151
MISO model for DMC, 114
MISO PEM model, 18
Model free approach for performance
monitoring, 165Index 239
Model predictive control, 101
Model structure selection, 15
Model-based simulation for control
performance monitoring, 160
MOESP, 46, 48
Monte-Carlo simulations, 72
MPC performance assessment: prediction
error approach, 195
MPC performance monitoring, 157
MPC performance monitoring through
model validation, 162
MPC performance monitoring: modelbased approach, 158
MPC relevant model validation, 165
MPC solutions, 108
MPC tradeoff curve, 159
Multi-step optimal prediction errors:
subspace algorithm, 201
Multivariate dynamic matrix control, 113
Multivariate performance assessment,
146
MVC benchmark from subspace matrices,
181
N4SID, 46, 48, 63
Noise model estimation from closed-loop
data, 81
Noise model tuning, 130
Normalized multivariate impulse response
(NMIR) curve, 166
Normalized residual, 164
Objective function, 107
Optimal ith step prediction, 197
Optimal prediction, 21
Optimal prediction for general linear
models, 23
Order of interactor matrix, 151
Orthogonal complement, 47
Orthogonal-projection based identification, 63
Out of control index (OCI), 167
Output error model, 17
Output variance under minimum variance
control expressed in subspace, 183
Penalizing control action, 111
Persistent excitation, 13
Petrochemical distillation column
simulation example, 206
Prediction error approach to control
performance assessment, 196
Prediction error method, 24
Prediction error method: algorithm, 25
Prediction error model, 15
Prediction horizon, 103
Prediction model for DMC, 109
Prediction-error approach for performance monitoring, 166
Predictions for MIMO DMC, 115
Primary residual, 163
Probabilistic inferencing for diagnosis of
MPC performance, 171
Process model subspace, 69
QR decomposition, 48, 184
QR decomposition for projections, 66
Quadratic objective function, 117, 122
Rank determination, 65
Receding horizon, 103
Recurrence relation, 10
Reference closed-loop potential, 198
Reference trajectory, 107
Relative closed-loop potential index, 198
Riccati equation, 23
SISO feedback control performance
assessment: conventional approach,
146
Solution of open-loop subspace identification by projection approach,
46
State space model of closed-loop system,
182
State space models, 105
Static Bayesian network, 174
Statistical approach, 49
Step response model, 104
Subspace approach for MIMO feedback
control performance assessment,
177
Subspace expression of feedback control
invariance property, 186
Subspace identification method via PCA,
SIMPCA, 65
Subspace orthogonal projection identification method via the state
estimation for model extraction,
SOPIM-S, 70240 Index
Subspace orthogonal projection identification method, SOPIM, 65
Subspace predictive controller, SPC, 124
SVD, 48, 65
Theoretical economic index, 170
Time-delays, 12
Total least squares, 63
Tradeoff curve, 159
Transfer function model, 104
Transition tendency index (TTI), 167
Unconstrained DMC, 111
Unified approach to subspace algorithms,
48
Unitary interactor matrix, 151
Univariate performance assessment, 146
White noise, 32Lecture Notes in Control and Information Sciences
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Modelling and Estimation Strategies for Fault
Diagnosis of Non-Linear Systems
215 p. 2007 [978-3-540-71114-8]
Vol. 353: Bonivento C.; Isidori A.; Marconi L.;
Rossi C. (Eds.)
Advances in Control Theory and Applications
305 p. 2007 [978-3-540-70700-4]
Vol. 352: Chiasson, J.; Loiseau, J.J. (Eds.)
Applications of Time Delay Systems
358 p. 2007 [978-3-540-49555-0]
Vol. 351: Lin, C.; Wang, Q.-G.; Lee, T.H., He, Y.
LMI Approach to Analysis and Control of
Takagi-Sugeno Fuzzy Systems with Time Delay
204 p. 2007 [978-3-540-49552-9]Vol. 350: Bandyopadhyay, B.; Manjunath, T.C.;
Umapathy, M.
Modeling, Control and Implementation of Smart
Structures 250 p. 2007 [978-3-540-48393-9]
Vol. 349: Rogers, E.T.A.; Galkowski, K.;
Owens, D.H.
Control Systems Theory
and Applications for Linear
Repetitive Processes
482 p. 2007 [978-3-540-42663-9]
Vol. 347: Assawinchaichote, W.; Nguang, K.S.;
Shi P.
Fuzzy Control and Filter Design
for Uncertain Fuzzy Systems
188 p. 2006 [978-3-540-37011-6]
Vol. 346: Tarbouriech, S.; Garcia, G.; Glattfelder,
A.H. (Eds.)
Advanced Strategies in Control Systems
with Input and Output Constraints
480 p. 2006 [978-3-540-37009-3]
Vol. 345: Huang, D.-S.; Li, K.; Irwin, G.W. (Eds.)
Intelligent Computing in Signal Processing
and Pattern Recognition
1179 p. 2006 [978-3-540-37257-8]
Vol. 344: Huang, D.-S.; Li, K.; Irwin, G.W. (Eds.)
Intelligent Control and Automation
1121 p. 2006 [978-3-540-37255-4]
Vol. 341: Commault, C.; Marchand, N. (Eds.)
Positive Systems
448 p. 2006 [978-3-540-34771-2]
Vol. 340: Diehl, M.; Mombaur, K. (Eds.)
Fast Motions in Biomechanics and Robotics
500 p. 2006 [978-3-540-36118-3]
Vol. 339: Alamir, M.
Stabilization of Nonlinear Systems Using
Receding-horizon Control Schemes
325 p. 2006 [978-1-84628-470-0]
Vol. 338: Tokarzewski, J.
Finite Zeros in Discrete Time Control Systems
325 p. 2006 [978-3-540-33464-4]
Vol. 337: Blom, H.; Lygeros, J. (Eds.)
Stochastic Hybrid Systems
395 p. 2006 [978-3-540-33466-8]
Vol. 336: Pettersen, K.Y.; Gravdahl, J.T.;
Nijmeijer, H. (Eds.)
Group Coordination and Cooperative Control
310 p. 2006 [978-3-540-33468-2]
Vol. 335: Kozłowski, K. (Ed.)
Robot Motion and Control
424 p. 2006 [978-1-84628-404-5]
Vol. 334: Edwards, C.; Fossas Colet, E.;
Fridman, L. (Eds.)
Advances in Variable Structure and Sliding Mode
Control
504 p. 2006 [978-3-540-32800-1]
Vol. 333: Banavar, R.N.; Sankaranarayanan, V.
Switched Finite Time Control of a Class of
Underactuated Systems
99 p. 2006 [978-3-540-32799-8]
Vol. 332: Xu, S.; Lam, J.
Robust Control and Filtering of Singular Systems
234 p. 2006 [978-3-540-32797-4]
Vol. 331: Antsaklis, P.J.; Tabuada, P. (Eds.)
Networked Embedded Sensing and Control
367 p. 2006 [978-3-540-32794-3]
Vol. 330: Koumoutsakos, P.; Mezic, I. (Eds.)
Control of Fluid Flow
200 p. 2006 [978-3-540-25140-8]
Vol. 329: Francis, B.A.; Smith, M.C.; Willems,
J.C. (Eds.)
Control of Uncertain Systems: Modelling,
Approximation, and Design
429 p. 2006 [978-3-540-31754-8]
Vol. 328: Loría, A.; Lamnabhi-Lagarrigue, F.;
Panteley, E. (Eds.)
Advanced Topics in Control Systems Theory
305 p. 2006 [978-1-84628-313-0]
Vol. 327: Fournier, J.-D.; Grimm, J.; Leblond, J.;
Partington, J.R. (Eds.)
Harmonic Analysis and Rational Approximation
301 p. 2006 [978-3-540-30922-2]
Vol. 326: Wang, H.-S.; Yung, C.-F.; Chang, F.-R.
H∞ Control for Nonlinear Descriptor Systems
164 p. 2006 [978-1-84628-289-8]
Vol. 325: Amato, F.
Robust Control of Linear Systems Subject to
Uncertain
Time-Varying Parameters
180 p. 2006 [978-3-540-23950-5]
Vol. 324: Christofides, P.; El-Farra, N.
Control of Nonlinear and Hybrid Process Systems
446 p. 2005 [978-3-540-28456-7]
Vol. 323: Bandyopadhyay, B.; Janardhanan, S.
Discrete-time Sliding Mode Control
147 p. 2005 [978-3-540-28140-5]
Vol. 322: Meurer, T.; Graichen, K.; Gilles, E.D.
(Eds.)
Control and Observer Design for Nonlinear Finite
and Infinite Dimensional Systems
422 p. 2005 [978-3-540-27938-9]
Vol. 321: Dayawansa, W.P.; Lindquist, A.;
Zhou, Y. (Eds.)
New Directions and Applications in Control
Theory
400 p. 2005 [978-3-540-23953-6]
Vol. 320: Steffen, T.
Control Reconfiguration of Dynamical Systems
290 p. 2005 [978-3-540-25730-1]
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