Spherical Modeling and Nonlinear Model Predictive Control of Electroporation

2011 ◽  
Author(s):  
Jingang Yi ◽  
Jianbo Li ◽  
Hao Lin
Keyword(s):  
Pore Size ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Transmembrane Potential ◽  
Dynamic Models ◽  
Effective Means ◽  
Spherical Model ◽  
Biological Research ◽  
Control Scheme ◽  
Predictive Controller

Electroporation is an effective means to deliver molecules into the cellular cytoplasm, and has been widely applied in both biological research and clinical applications. The process however suffers from low viability and efficiency. In this work, we present a multi-pulse feedback control scheme for enhancing viability and efficiency of electroporation process. We first present a spherical model for cell transmembrane potential (TMP) dynamics to describe the spatial distribution of electric potential on cell membrane. Then we analyze the models and discuss the bifurcation property of the system. Based on the dynamic models, we design a nonlinear model predictive controller (NMPC) to track pore size profiles in electroporation. We demonstrate that by using NMPC, the pore size can be precisely regulated to targeted values for drug delivery applications. The control design is illustrated through simulation examples.

scholarly journals Nonlinear model predictive control of a boiler unit: A fault tolerant control study

2012 ◽  
Vol 22 (1) ◽  
pp. 225-237 ◽  
Author(s):  
Krzysztof Patan ◽  
Józef Korbicz
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Nonlinear Model Predictive Control ◽  
Boiler Unit ◽  
Predictive Controller ◽  
One Step

Nonlinear model predictive control of a boiler unit: A fault tolerant control studyThis paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as a one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are also investigated. A set of eight faulty scenarios is prepared to verify the quality of the fault tolerant control. Based of different faulty situations, a fault compensation problem is also investigated. As the automatic control system can hide faults from being observed, the control system is equipped with a fault detection block. The fault detection module designed using the one-step ahead predictor and constant thresholds informs the user about any abnormal behaviour of the system even in the cases when faults are quickly and reliably compensated by the predictive controller.

Adaptive Generalized Predictive Controller and Cartesian Force Control for Robot Arm Using Dynamics and Geometric Identification

2018 ◽  
Vol 30 (6) ◽  
pp. 927-942 ◽  
Author(s):  
Shohei Hagane ◽  
Liz Katherine Rincon Ardila ◽  
Takuma Katsumata ◽  
Vincent Bonnet ◽  
Philippe Fraisse ◽  
...  
Keyword(s):  
Predictive Control ◽  
High Performance ◽  
Wall Painting ◽  
Robot Arm ◽  
Inertial Parameters ◽  
Control Scheme ◽  
Predictive Controller ◽  
Adaptive Predictive Control ◽  
Predictive Controllers ◽  
Contact Tasks

In realistic situations such as human-robot interactions or contact tasks, robots must have the capacity to adapt accordingly to their environment, other processes and systems. Adaptive model based controllers, that requires accurate dynamic and geometric robot’s information, can be used. Accurate estimations of the inertial and geometric parameters of the robot and end-effector are essential for the controller to demonstrate a high performance. However, the identification of these parameters can be time-consuming and complex. Thus, in this paper, a framework based on an adaptive predictive control scheme and a fast dynamic and geometric identification process is proposed. This approach was demonstrated using a KUKA lightweight robot (LWR) in the performance of a force-controlled wall-painting task. In this study, the performances of a generalized predictive control (GPC), adaptive proportional derivative gravity compensation, and adaptive GPC (AGPC) were compared. The results revealed that predictive controllers are more suitable than adaptive PD controllers with gravitational compensation, owing to the use of well-identified geometric and inertial parameters.

scholarly journals Learning-based nonlinear model predictive control with accurate uncertainty compensation

2021 ◽  
Author(s):  
Jingjie Xie ◽  
Xiaowei Zhao ◽  
Hongyang Dong
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Nonlinear Model Predictive Control ◽  
Nominal Model ◽  
Performance Requirements ◽  
Control Scheme ◽  
System Uncertainties ◽  
And Performance ◽  
Safety And Stability

AbstractA learning-based nonlinear model predictive control (LBNMPC) method is proposed in this paper for general nonlinear systems under system uncertainties and subject to state and input constraints. The proposed LBNMPC strategy decouples the robustness and performance requirements by employing an additional learned model and introducing it into the MPC framework along with the nominal model. The nominal model helps to ensure the closed-loop system’s safety and stability, and the learned model aims to improve the tracking behaviors. As a core of the learned model construction, an online parameter estimator is designed to deal with system uncertainties. This estimation process effectively evaluates both the current and historical effects of uncertainties, leading to superior estimating performance compared with conventional methods. By constructing an invariant terminal constraint set, we prove that the LBNMPC is recursively feasible and robustly asymptotically stable. Numerical verifications for a two-link manipulator are conducted to validate the effectiveness and robustness of the proposed control scheme.

scholarly journals Hybrid Grey Wolf Optimization Nonlinear Model Predictive Control for Aircraft Engines Based on an Elastic BP Neural Network

2019 ◽  
Vol 9 (6) ◽  
pp. 1254 ◽  
Author(s):  
Lingfei Xiao ◽  
Min Xu ◽  
Yuhan Chen ◽  
Yusheng Chen
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Bp Neural Network ◽  
Aircraft Engine ◽  
Aircraft Engines ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Model Predictive Controller ◽  
Predictive Controller

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

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