4 research outputs found
Multi-agent model predictive control for transport phenomena processes
Throughout the last decades, control systems theory has thrived, promoting new areas
of development, especially for chemical and biological process engineering. Production
processes are becoming more and more complex and researchers, academics and industry professionals dedicate more time in order to keep up-to-date with the increasing complexity and nonlinearity. Developing control architectures and incorporating novel control techniques as a way to overcome optimization problems is the main focus for all people involved.
Nonlinear Model Predictive Control (NMPC) has been one of the main responses
from academia for the exponential growth of process complexity and fast growing scale.
Prediction algorithms are the response to manage closed-loop stability and optimize
results. Adaptation mechanisms are nowadays seen as a natural extension of prediction methodologies in order to tackle uncertainty in distributed parameter systems (DPS), governed by partial differential equations (PDE). Parameters observers and Lyapunov adaptation laws are also tools for the systems in study.
Stability and stabilization conditions, being implicitly or explicitly incorporated in the
NMPC formulation, by means of pointwise min-norm techniques, are also being used and combined as a way to improve control performance, robustness and reduce computational effort or maintain it low, without degrading control action.
With the above assumptions, centralized (or single agent) or decentralized and distributed Model Predictive Control (MPC) architectures (also called multi-agent) have been applied to a series of nonlinear distributed parameters systems with transport phenomena, such as bioreactors, water delivery canals and heat exchangers to show the importance and success of these control techniques
ΠΡΠ½ΠΎΠ²Ρ ΡΠ΅ΠΎΡΠΈΠΈ ΡΠ°ΡΡΠΈΡΠ½ΠΎΠΉ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ ΠΈ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ : [ΡΡΠ΅Π±Π½ΠΎΠ΅ ΠΏΠΎΡΠΎΠ±ΠΈΠ΅]
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°ΡΡΡΡ Π·Π°Π΄Π°ΡΠΈ ΡΠ°ΡΡΠΈΡΠ½ΠΎΠΉ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ (ΡΡΠ°Π±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ), Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π·Π°Π΄Π°ΡΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ (ΡΡΠ°Π±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ) ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ ΡΠ°ΡΡΠΈ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
, β Π½ΠΎΠ²ΡΠ΅ Π·Π°Π΄Π°ΡΠΈ, Π²ΠΎΠ·Π½ΠΈΠΊΡΠΈΠ΅ Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΎΠ±ΡΠ΅ΠΉ ΡΠ΅ΠΎΡΠΈΠΈ ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΠΈ, Π²ΠΎΡΡ
ΠΎΠ΄ΡΡΠ΅ΠΉ ΠΊ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΡΡΠ΄Π°ΠΌ Π. Π. ΠΡΠΏΡΠ½ΠΎΠ²Π° ΠΈ Π. ΠΡΠ°Π½ΠΊΠ°ΡΠ΅. ΠΠ»Π°Π³ΠΎΠ΄Π°ΡΡ Π±ΠΎΠ»ΡΡΠΎΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΎΠ±ΡΠ½ΠΎΡΡΠΈ, ΡΡΠΈ Π·Π°Π΄Π°ΡΠΈ ΡΠ²Π»ΡΡΡΡΡ ΠΌΠ΅ΠΆΠ΄ΠΈΡΡΠΈΠΏΠ»ΠΈΠ½Π°ΡΠ½ΡΠΌΠΈ ΠΈ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡ ΠΏΡΠΈ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠΈ ΠΌΠ½ΠΎΠ³ΠΈΡ
ΡΠ²Π»Π΅Π½ΠΈΠΉ ΠΈ ΡΠΏΡΠ°Π²Π»ΡΠ΅ΠΌΡΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ² Π² ΡΠ°ΠΌΡΡ
ΡΠ°Π·Π½ΡΡ
ΡΠ°Π·Π΄Π΅Π»Π°Ρ
Π½Π°ΡΠΊΠΈ ΠΈ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ
A generalization of input-to-state stability
Input-to-State Stability (ISS) and its many derivatives have proved to be extremely useful in the analysis and design of robustly stable nonlinear systems. In this paper, we present a generalization of ISS that subsumes several ISS-type properties and discuss cases where this generalization may fail