Reprint
Optimization for Control, Observation and Safety
Edited by
April 2020
500 pages
- ISBN978-3-03928-440-5 (Paperback)
- ISBN978-3-03928-441-2 (PDF)
This book is a reprint of the Special Issue Optimization for Control, Observation and Safety that was published in
Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Summary
Mathematical optimization is the selection of the best element in a set with respect to a given criterion. Optimization has become one of the most used tools in control theory to compute control laws, adjust parameters (tuning), estimate states, fit model parameters, find conditions in order to fulfill a given closed-loop property, among others. Optimization also plays an important role in the design of fault detection and isolation systems to prevent safety hazards and production losses that require the detection and identification of faults, as early as possible to minimize their impacts by implementing real-time fault detection and fault-tolerant systems. Recently, it has been proven that many optimization problems with convex objective functions and linear matrix inequality (LMI) constraints can be solved easily and efficiently using existing software, which increases the flexibility and applicability of the control algorithms. Therefore, real-world control systems need to comply with several conditions and constraints that have to be taken into account in the problem formulation, which represents a challenge in the application of the optimization algorithms. This book offers an overview of the state-of-the-art of the most advanced optimization techniques and their applications in control engineering.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
model predictive control; control horizon; steam power plant; steam/water loop; multi-input and multi-output system; loop design; FFANN; control; optimization; ABC; modeling; buck converter; settling time; distributed observers; sensor position; perturbation estimation; PDE; fault detection and classification; uncertainty analysis; lithium-ion battery; optimization; thermal management; polynomial chaos expansion; profile monitoring; polynomial regression model; sum of sine function; Hotelling’s T2 control chart; EWMA control chart; availability assessment; integrated modular avionics; model-based safety analysis; AltaRica 3.0; KPCA; T2 statistical model; SPE statistical model; kernel function; robust open-loop optimal control; generalized polynomial chaos; chance constraints; subset simulation; open-loop optimal control; battery charge–discharge; optimization; evolutionary computation; population minimization; hybridization; local search; global search; adaptive differential evolution; external archives; metaheuristics; feature selection; PEM fuel cell; control; neural network; principal component analysis; modeling; system identification; underactuated ship; bilinear model predictive controller; directly observer; uncertain estimator; global optimization; meta-heuristics; swarm intelligence; benchmark functions; exploration; exploitation; global minimum; local minimum; cyber-physical production systems; self-learning factory; holonic manufacturing systems; machine learning; transfer learning; predictive analytics; windmill park; wind speed estimator; multi-objective optimization; sequential optimisation; distributed model predictive control; linear parameter varying (LPV) systems; Takagi-Sugeno systems; convex systems; linear matrix inequalities (LMIs); fault diagnosis; fault tolerant control (FTC); Unscented Kalman Filter; particle filter; weight optimization; hybrid filter; gas turbine; high-viscosity; two-phase flow; detrended fluctuation analysis; heavy oils; PV; 3-φ transformerless inverter; NPC; boost converter; model predictive control; maximum power point tracking; relay protection equipment; whole monitoring data; generalized proportional hazard model (GPHM); adaptive homotopy algorithm; jacobi matrix; leak isolation; nonlinear observer; genetic algorithm; fault diagnosis; DPF; blockage; fault diagnosis; exhaust pressure; spectral analysis; Automated manufacturing system; colored Petri net; deadlock prevention; siphon; active distribution network; converter; digital relay; DG; fault; protection; power system; renewable energy resource; drinking water networks; model predictive control; reliability; linear parameter varying; operation and management; economic cost; Takagi–Sugeno; fault estimation; unknown input; interval observer; permanent magnet motor; modified GA; fuzzy-PID control; autonomous hovercraft; ISE criterion; system safety assessment; fly-by-wire system; fault injection; Monte Carlo simulation; dynamic behavior mode; n/a