scholarly journals Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Santiago Rómoli ◽  
Mario Serrano ◽  
Francisco Rossomando ◽  
Jorge Vega ◽  
Oscar Ortiz ◽  
...  
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Rbf Neural Network ◽  
Control Strategies ◽  
Tracking Error ◽  
Closed Loop Control ◽  
Online Information ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Bioreactor

The lack of online information on some bioprocess variables and the presence of model and parametric uncertainties pose significant challenges to the design of efficient closed-loop control strategies. To address this issue, this work proposes an online state estimator based on a Radial Basis Function (RBF) neural network that operates in closed loop together with a control law derived on a linear algebra-based design strategy. The proposed methodology is applied to a class of nonlinear systems with three types of uncertainties: (i) time-varying parameters, (ii) uncertain nonlinearities, and (iii) unmodeled dynamics. To reduce the effect of uncertainties on the bioreactor, some integrators of the tracking error are introduced, which in turn allow the derivation of the proper control actions. This new control scheme guarantees that all signals are uniformly and ultimately bounded, and the tracking error converges to small values. The effectiveness of the proposed approach is illustrated on the basis of simulated experiments on a fed-batch bioreactor, and its performance is compared with two controllers available in the literature.

scholarly journals Design and Control of a Piezoelectric-Driven Microgripper Perceiving Displacement and Gripping Force

Micromachines ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 121 ◽  
Author(s):  
Yanru Zhao ◽  
Xiaojie Huang ◽  
Yong Liu ◽  
Geng Wang ◽  
Kunpeng Hong
Keyword(s):  
Pid Controller ◽  
Closed Loop ◽  
Tracking Error ◽  
Closed Loop Control ◽  
Structure Parameters ◽  
Loop Control ◽  
Amplification Ratio ◽  
Gripping Force ◽  
And Control ◽  
Tip Displacement

A piezoelectric-driven microgripper with three-stage amplification was designed, which is able to perceive the tip displacement and gripping force. The key structure parameters of the microgripper were determined by finite element optimization and its theoretical amplification ratio was derived. The tracking experiments of the tip displacement and gripping force were conducted with a PID controller. It is shown that the standard deviation of tracking error of the tip displacement is less than 0.2 μm and the gripping force is 0.35 mN under a closed-loop control. It would provide some references for realizing high-precision microassembly tasks with the designed microgripper which can control the displacement and gripping force accurately.

EVALUATION OF CLOSED-LOOP CONTROL SYSTEM FOR RESTORING STANDING AND SITTING FUNCTIONS BY FUNCTIONAL ELECTRICAL STIMULATION

2005 ◽  
Vol 17 (01) ◽  
pp. 19-26 ◽  
Author(s):  
CHENG-LIANG LIU ◽  
CHUNG-HUANG YU ◽  
SHIH-CHING CHEN ◽  
CHANG-HUNG CHEN
Keyword(s):  
Electrical Stimulation ◽  
Functional Electrical Stimulation ◽  
Closed Loop ◽  
Spinal Cord Injuries ◽  
Control Method ◽  
Control Strategies ◽  
Evaluation Process ◽  
Closed Loop Control ◽  
Loop Control ◽  
Loop Control System

Functional electrical stimulation (FES) is a method for restoring the functional movements of paraplegic or patients with spinal cord injuries. However, the selection of parameters that control the restoration of standing up and sitting functions has not been extensively investigated. This work provides a method for choosing the four main items involved in evaluating the strategies for sit-stand-sit movements with the aid of a modified walker. The control method uses the arm-supported force and the angles of the legs as feedback signals to change the intensity of the electrical stimulation of the leg muscles. The control parameters, Ki and Kp, are vary for different control strategies. Four items are collected through questionnaires and used for evaluation. They are the maximum reactions of the two hands, the average reaction of the two hands, largest absolute angular velocity of the knee joints, and the sit-stand-sit duration time. The experimental data are normalized to facilitate comparison. Weighting factors are obtained and analyzed from questionnaires answered by experts and are added to evaluation process for manipulation. The results show that the best strategy is the closed-loop control with parameters Ki=0.5 and Kp=0.

scholarly journals Control of Flexible Manipulator Systems

1996 ◽  
Vol 210 (2) ◽  
pp. 113-130 ◽  
Author(s):  
M O Tokhi ◽  
A K M Azad
Keyword(s):  
Closed Loop ◽  
Control Strategies ◽  
Open Loop ◽  
Flexible Manipulator ◽  
Closed Loop Control ◽  
Loop Control ◽  
Acceleration Feedback ◽  
Low Pass ◽  
Input Torque ◽  
Collocated Control

This paper presents an investigation into the development of open-loop and closed-loop control strategies for flexible manipulator systems. Shaped torque inputs, including Gaussian-shaped and low-pass (Butter-worth and elliptic) filtered input torque functions, are developed and used in an open-loop configuration and their performance studied in comparison to a bang-bang input torque through experimentation on a single-link flexible manipulator system. Closed-loop control strategies that use both collocated (hub angle and hub velocity) and non-collocated (end-point acceleration) feedback are then proposed. A collocated proportional and derivative (PD) control is first developed and its performance studied through experimentation. The collocated control is then extended to incorporate, additionally, non-collocated feedback through a proportional integral derivative (PID) configuration. The performance of the hybrid collocated and non-collocated control strategy thus developed is studied through experimentation. Experimental results verifying the performance of the developed control strategies are presented and discussed.

scholarly journals Verification of Closed-loop Systems with Neural Network Controllers

10.29007/btv1 ◽  
2019 ◽  
Author(s):  
Diego Manzanas Lopez ◽  
Patrick Musau ◽  
Hoang-Dung Tran ◽  
Taylor T. Johnson
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Systems ◽  
Closed Loop ◽  
Closed Loop Control ◽  
Feed Forward ◽  
Loop Control ◽  
Closed Loop Systems ◽  
Feed Forward Neural Networks ◽  
Neural Network Controllers

This benchmark suite presents a detailed description of a series of closed-loop control systems with artificial neural network controllers. In many applications, feed-forward neural networks are heavily involved in the implementation of controllers by learning and representing control laws through several methods such as model predictive control (MPC) and reinforcement learning (RL). The type of networks that we consider in this manuscript are feed-forward neural networks consisting of multiple hidden layers with ReLU activation functions and a linear activation function in the output layer. While neural network con- trollers have been able to achieve desirable performance in many contexts, they also present a unique challenge in that it is difficult to provide any guarantees about the correctness of their behavior or reason about the stability a system that employs their use. Thus, from a controls perspective, it is necessary to verify them in conjunction with their corresponding plants in closed-loop. While there have been a handful of works proposed towards the verification of closed-loop systems with feed-forward neural network controllers, this area still lacks attention and a unified set of benchmark examples on which verification techniques can be evaluated and compared. Thus, to this end, we present a range of closed-loop control systems ranging from two to six state variables, and a range of controllers with sizes in the range of eleven neurons to a few hundred neurons in more complex systems.

Trajektorienbasierte Strategie für die Regelung des Tiefziehprozesses während des Hubes*/Trajectory-based closed loop control for controlling deep-drawing process

2016 ◽  
Vol 106 (10) ◽  
pp. 684-689
Author(s):  
M. Prof. Liewald ◽  
M. Barthau ◽  
S. Braun
Keyword(s):  
Deep Drawing ◽  
Closed Loop ◽  
Control Strategies ◽  
Closed Loop Control ◽  
Drawing Process ◽  
Control Loop ◽  
Deep Drawing Process ◽  
Loop Control ◽  
Control Intervention ◽  
Measurement Devices

Am IFU der Universität Stuttgart wurde ein Regelkreis für das Tiefziehen entwickelt, welcher einen regelnden Eingriff in den Tiefziehvorgang während des Hubes erlaubt. Die Umsetzung dieses Regelungskonzeptes erfolgte mittels eines Ziehwerkzeugs, das an eine vereinfachte Geometrie eines PKW-Vorderkotflügels angelehnt ist. Beschrieben werden die messtechnische Ausstattung des Versuchswerkzeugs, der Aufbau des Regelkreises und die Entwicklung der Regelstrategie. Des Weiteren werden die Ergebnisse der Simulation sowie der ersten Versuche dargestellt.   At IFU, University of Stuttgart a control loop for deep-drawing process, with control intervention during deep-drawing stroke was developed. The closed-loop control was demonstrated on a fender shaped geometry. Described are the measurement devices, design of the closed-loop and the featured control strategies. Results of simulation and sensitivity analysis are also shown.

Research on Intelligent Control Strategy of Inverted Plasma Cutting Power

2011 ◽  
Vol 179-180 ◽  
pp. 1223-1228
Author(s):  
De Li Jia ◽  
Jin Song He ◽  
Ji Chao Ning ◽  
Chun Sheng Wang
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Pid Controller ◽  
Closed Loop ◽  
Rbf Neural Network ◽  
Response Speed ◽  
Discrete Problem ◽  
Plasma Cutting ◽  
Cutting Power ◽  
Loop Control

The inverted plasma cutting power supply has multi-variable, nonlinear, strong coupling and time-varying characteristics and technological requirements. This paper proposes a decoupling control strategy based on fuzzy neural network expert system for the multi-parameter dynamic coupling and the uncertainty of the optimal output of the cutting process. It achieves the reasoning and decision-making through the fuzzy production rules. It adjusts the parameters of the neural network by adaptive algorithm, thus gets the optimal reference current of closed-loop controller, and then achieves the given current closed-loop control by means of RBF neural network adaptive PID controller. The simulation results show that the controller has good self-adaption and approximation. Compared with traditional PID controller, the system has great improvement on static accuracy, robustness and response speed. It is verified that the controller has the advantage of dealing with discrete problem and coupling multivariable problem.

Closed-loop control of fed-batch cultures of recombinantEscherichia coli using on-line HPLC

1994 ◽  
Vol 44 (7) ◽  
pp. 819-829 ◽  
Author(s):  
Claire Turner ◽  
Malcolm E. Gregory ◽  
Nina F. Thornhill
Keyword(s):  
Closed Loop ◽  
Closed Loop Control ◽  
Fed Batch ◽  
Loop Control ◽  
Batch Cultures ◽  
On Line
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