Improved robust model predictive control with structured uncertainty

Feng, Liuyang; Wang, Jianliang; Poh, Eng Kee

doi:10.1016/j.jprocont.2007.01.008

Cited by 36 publications

(21 citation statements)

References 11 publications

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“…The review paper written by Embiruçu et al17 referred to successful classic controllers studies implemented in complex polymeric systems. Richalet et al,18 Cutter and Ramaker,19 Garcia and Morshedi,20 Houk et al,21 Qin and Badgwell,22 Rodrigues and Odloak,23 Feng et al,24 and Karra et al25 employed linear models and built suitable linear model predictive control (LMPC) schemes for representing industrial processes. As the main interest of the paper is implementing polymer quality control experimentally and show how simple and robust is the monitoring technique for industrial purposes, classic, LMPC and nonlinear model predictive control (NLMPC) strategies were investigated.…”

Section: Controlmentioning

confidence: 99%

Single‐Point Intrinsic Viscosity and Density Measurements for In‐Line MIMO Control Purposes of a Lumped‐Distributed Polymeric System

Vega¹,

Bellumat²,

Lima³

et al. 2012

Macro Reaction Engineering

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Simulation and experimental studies are carried out for peroxide‐initiated styrene solution polymerization in a lumped‐distributed reactor. Weight‐average molecular weight (intrinsic viscosity measurements) and conversion (density data) are the output signals, while the manipulated variables are jacket reactor temperature and feed modifier (tert‐dodecylmercaptan) concentration. A simple in‐line viscometrical method makes the in‐line control of weight‐average molecular weight in solution polymerization feasible, using independent conversion measurements from an in‐line densitometer. Successful closed‐loop servo and regulatory control results are obtained.

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Section: Controlmentioning

confidence: 99%

Single‐Point Intrinsic Viscosity and Density Measurements for In‐Line MIMO Control Purposes of a Lumped‐Distributed Polymeric System

Vega¹,

Bellumat²,

Lima³

et al. 2012

Macro Reaction Engineering

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“…Moreover, the offered algorithm is much more efficient than NP algorithm in terms of much less time for computation. / m kmol is suddenly poured into the reactor in 0.2 second, when the system is already in steady state situation at the setpoint of 17 3 / m kmol . A successful disturbance rejection is observed.…”

Section: System Dynamicsmentioning

confidence: 99%

“…These systems are multi-input and multi-output and may be highly nonlinear. Self-tuning PID [1,2], robust controllers [3] adaptive-like control systems [4] and different kinds of nonlinear predictive controllers [5,6] have been successfully tested on this class of chemical systems. CSTR is known as an outstanding example for the application of neuro-predictive controllers [7] as a subset of nonlinear predictive controllers.…”

Section: Introductionmentioning

confidence: 99%

Intelligent control of a nonlinear tank reactor based on Lyapunov direct method

Mohammadzaheri

Chen

2009

2009 IEEE International Conference on Industrial Technology

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In this paper, intelligent control of the outlet flow concentration of a non-thermic Catalytic Continuous Stirred Tank Reactor (CSTR) is addressed. Control command is the sum of two control commands: steady state and transient commands. A fuzzy controller generates transient control command pushing the system towards the reference (desired situation). Steady state control command is generated to maintain steady state situation (based on the concept of 'control equilibrium point'). This research is performed in simulation environment; however, the mathematical model of the system is not used during stability analysis to keep the methodology useful in case of considerable uncertainties; instead, some obvious facts about the system in the form of fuzzy rules are used for stability analysis, namely 'fuzzy rough model'. Using this technique, Lyapunov asymptotic stability of control system is proved. For comparison, the case study is also controlled by neuro-predictive algorithm. The studied CSTR is known as a very good example of neuro-predictive control application; however, the newly offered hybrid intelligent method leads much better reference tracking performance as well as less change in control command (which is very important in implementation).

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“…In the last decade, for the first time, an LMI‐based systematic approach is proposed to MPC design for discrete‐time system . Then this method is widely used by the other researchers to design an algorithmic MPC in discrete‐time control systems . In these studies, the final control law, which is applied to the plant, has a linear form like u ( k ) = F ( k ) x ( k ) , k = 1 , 2 , 3 , .…”

Section: Introductionmentioning

confidence: 99%

A robust control system scheme based on model predictive controller (MPC) for continuous‐time systems

Ghaffari

2017

Optim Control Appl Methods

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Summary In this paper, an LMI framework based on model predictive strategy is addressed to design a robust dynamical control law in a typical control system. In the proposed method, instead of traditional static controller, a dynamic control law is used. With a suitable matrix transformation, the controller parameters selection are translated into an optimization problem with some LMI constraints. The plant input and output constraints are also handled with another LMIs. The controller is represented in state space form, and its parameters are computed in real‐time operation. For achieving this goal, by solving an optimization problem, a dynamic controller is designed, which meets the required plant performances. These results are used in 2 numerical examples to demonstrate the effectiveness of the proposed approach.

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Improved robust model predictive control with structured uncertainty

Cited by 36 publications

References 11 publications

Single‐Point Intrinsic Viscosity and Density Measurements for In‐Line MIMO Control Purposes of a Lumped‐Distributed Polymeric System

Single‐Point Intrinsic Viscosity and Density Measurements for In‐Line MIMO Control Purposes of a Lumped‐Distributed Polymeric System

Intelligent control of a nonlinear tank reactor based on Lyapunov direct method

A robust control system scheme based on model predictive controller (MPC) for continuous‐time systems

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