A bio-system inspired nonline ar intelligent controller with application to bio-reactor system

Bao, Liu; Ding, Yongsheng; Gao, Na; Zhang, Xin

doi:10.1016/j.neucom.2015.05.017

Cited by 9 publications

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are multiple ways to control bioreactors. Nagy [52] a model of a predictive controller based on an artificial neural network for temperature control of a bioreactor. Liu et al [53] presented a novel non-linear guided intelligent controller inspired by the command properties of glucose in the human body and applied it to bioreactor systems, with temperature as the main process parameter, as it influences thermal movement in bioreactors.…”

Section: Fractional-order Pid Controllers In Bioreactorsmentioning

confidence: 99%

Fractional-Order PID Controllers for Temperature Control: A Review

Jamil

Ali

et al. 2022

Energies

View full text Add to dashboard Cite

Fractional-order proportional integral derivative (FOPID) controllers are becoming increasingly popular for various industrial applications due to the advantages they can offer. Among these applications, heating and temperature control systems are receiving significant attention, applying FOPID controllers to achieve better performance and robustness, more stability and flexibility, and faster response. Moreover, with several advantages of using FOPID controllers, the improvement in heating systems and temperature control systems is exceptional. Heating systems are characterized by external disturbance, model uncertainty, non-linearity, and control inaccuracy, which directly affect performance. Temperature control systems are used in industry, households, and many types of equipment. In this paper, fractional-order proportional integral derivative controllers are discussed in the context of controlling the temperature in ambulances, induction heating systems, control of bioreactors, and the improvement achieved by temperature control systems. Moreover, a comparison of conventional and FOPID controllers is also highlighted to show the improvement in production, quality, and accuracy that can be achieved by using such controllers. A composite analysis of the use of such controllers, especially for temperature control systems, is presented. In addition, some hidden and unhighlighted points concerning FOPID controllers are investigated thoroughly, including the most relevant publications.

show abstract

Section: Fractional-order Pid Controllers In Bioreactorsmentioning

confidence: 99%

Fractional-Order PID Controllers for Temperature Control: A Review

Jamil

Ali

et al. 2022

Energies

View full text Add to dashboard Cite

show abstract

“…The second one consists of the adaptive neuro-fuzzy inference system-PID (ANFIS-PID), self-tuning of the gains of the PID controller. However, the fuzzy logic controller [11] or the smart method of controlling [12] (and many others) are very often confused for the intelligent controller.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Controller Design by the Artificial Intelligence Methods

Nowaková

Pokorný

2020

Sensors

View full text Add to dashboard Cite

With the rapid growth of sensor networks and the enormous, fast-growing volumes of data collected from these sensors, there is a question relating to the way it will be used, and not only collected and analyzed. The data from these sensors are traditionally used for controlling and influencing the states and processes. Standard controllers are available and successfully implemented. However, with the data-driven era we are facing nowadays, there is an opportunity to use controllers, which can include much information, elusive for common controllers. Our goal is to propose a design of an intelligent controller–a conventional controller, but with a non-conventional method of designing its parameters using approaches of artificial intelligence combining fuzzy and genetics methods. Intelligent adaptation of parameters of the control system is performed using data from the sensors measured in the controlled process. All parts designed are based on non-conventional methods and are verified by simulations. The identification of the system’s parameters is based on parameter optimization by means of its difference equation using genetic algorithms. The continuous monitoring of the quality control process and the design of the controller parameters are conducted using a fuzzy expert system of the Mamdani type, or the Takagi–Sugeno type. The concept of the intelligent control system is open and easily expandable.

show abstract

Reinforcement learning based temperature control of a fermentation bioreactor for ethanol production

Rajasekhar,

Radhakrishnan,

Mohamed

2024

Biotech & Bioengineering

View full text Add to dashboard Cite

Ethanol production is a significant industrial bioprocess for energy. The primary objective of this study is to control the process reactor temperature to get the desired product, that is, ethanol. Advanced model‐based control systems face challenges due to model‐process mismatch, but Reinforcement Learning (RL) is a class of machine learning which can help by allowing agents to learn policies directly from the environment. Hence a RL algorithm called twin delayed deep deterministic policy gradient (TD3) is employed. The control of reactor temperature is categorized into two categories namely unconstrained and constrained control approaches. The TD3 with various reward functions are tested on a nonlinear bioreactor model. The results are compared with existing popular RL algorithm, namely, deep deterministic policy gradient (DDPG) algorithm with a performance measure such as mean squared error (MSE). In the unconstrained control of the bioreactor, the TD3 based controller designed with the integral absolute error (IAE) reward yields a lower MSE of 0.22, whereas the DDPG produces an MSE of 0.29. Similarly, in the case of constrained controller, TD3 based controller designed with the IAE reward yields a lower MSE of 0.38, whereas DDPG produces an MSE of 0.48. In addition, the TD3 trained agent successfully rejects the disturbances, namely, input flow rate and inlet temperature in addition to a setpoint change with better performance metrics.

show abstract

A bio-system inspired nonline ar intelligent controller with application to bio-reactor system

Cited by 9 publications

References 30 publications

Fractional-Order PID Controllers for Temperature Control: A Review

Fractional-Order PID Controllers for Temperature Control: A Review

Intelligent Controller Design by the Artificial Intelligence Methods

Reinforcement learning based temperature control of a fermentation bioreactor for ethanol production

Contact Info

Product

Resources

About