Real-Time Level Plant Control Using Improved BELBIC

Masoudinejad, Mojtaba; Khorsandi, Rahman; Fatehi, Alireza; Lucas, Caro; Derakhshan, Siavash Fakhimi; Jamali, Mohammad Reza

doi:10.3182/20080706-5-kr-1001.00779

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Mistuning of these parameters may cause instability in the system. 18 The agent can be selected as function of the error integration and control output, 17 or function of the controller output, error and error derivative 27 or even as simple proportional–integral (PI)/PID controllers. 11,18,21 However, in these studies, the corresponding parameters were tuned offline for a particular control problem.…”

Section: Design Of Stbelbic Controller For Ehamentioning

confidence: 99%

“…In addition, mistuning can affect directly on the control performance, 26 especially in EHA applications. Furthermore, the internal instability of the BELBIC was studied by Masoudinejad et al 27 to control a plant-level system. Their study revealed that the BELBIC suffers from overshooting, slow adaptive or even unstable when the system or reference properties are changed.…”

Section: Introductionmentioning

confidence: 99%

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A self-tuning brain emotional learning–based intelligent controller for trajectory tracking of electrohydraulic actuator

Nahian

Dinh

Ahn

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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Realization of biologically motivated algorithms in industrial applications is becoming a new research, especially in the field of electrohydraulic systems. One of the recent innovations named brain emotional learning–based intelligent controller has been catching eyes of the researcher as a model-free adaptive controller, which has effective capabilities to handle nonlinearities and uncertainties of controlled systems. The aim of this article is to develop a so-called self-tuning brain emotional learning–based intelligent controller for tracking control of electrohydraulic actuators. Here, the main control unit brain emotional learning–based intelligent controller is used to drive the system to desired targets. Meanwhile, a fuzzy inference is designed to tune online the reward function (RF) parameter of the brain emotional learning–based intelligent controller, which enables the system robustness and stability. A test rig employing an electrohydraulic actuator is then setup to investigate the system control performance. The experimental results implied that proposed controller has strong ability to drive the system to follow different reference trajectories with minimal errors.

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Section: Design Of Stbelbic Controller For Ehamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A self-tuning brain emotional learning–based intelligent controller for trajectory tracking of electrohydraulic actuator

Nahian

Dinh

Ahn

2014

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…From the BEL model being proposed, it was soon applied into control systems of real engineering fields, termed brain emotional learning based on an intelligent controller – originally proposed by Lucas [6]. In recent years, BEL controllers have proved to have good robustness and uncertainty handling properties when applied in many engineering systems, such as simo overhead travelling cranes [7][8], switched reluctance motors [9], plant level systems [10], alarm systems [11], micro-heat exchangers [12], flight simulation servo systems [13][14] and other uncertain nonlinear systems [15].…”

Section: Introductionmentioning

confidence: 99%

UAV Flight Control System Based on an Intelligent BEL Algorithm

Huangzhong

Zhen

Jiang

et al. 2013

International Journal of Advanced Robotic Systems

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A novel intelligent control strategy based on a brain emotional learning (BEL) algorithm is investigated in the application of the attitude control of a small unmanned aerial vehicle (UAV) in this study. The BEL model imitates the emotional learning process in the amygdalaorbitofrontal (A-O) system of mammalian brains. Here it is used to develop the flight control system of the UAV. The control laws of elevator, aileron and rudder manipulators adopt the forms of traditional flight control laws, and three BEL models are used in above three control loops, to online regulate the control gains of each controller. Obviously, a BEL intelligent control system is self-learning and self-adaptive, which is important for UAVs when flight conditions change, while traditional flight control systems remain unchanged after design. In simulation, the UAV is on a flat flight and suddenly a wind disturbs it making it depart from the equilibrium state. In order to make the UAV recover to the original equilibrium state, the BEL intelligent control system is adopted. The simulation results illustrate that the BEL-based intelligent flight control system has characteristics of better adaptability and stronger robustness, when compared with the traditional flight control system.

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BELBIC and Its Industrial Applications: Towards Embedded Neuroemotional Control Codesign

Lucas

2011

Integrated Systems, Design and Technology 2010

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Real-Time Level Plant Control Using Improved BELBIC

Cited by 5 publications

References 3 publications

A self-tuning brain emotional learning–based intelligent controller for trajectory tracking of electrohydraulic actuator

A self-tuning brain emotional learning–based intelligent controller for trajectory tracking of electrohydraulic actuator

UAV Flight Control System Based on an Intelligent BEL Algorithm

BELBIC and Its Industrial Applications: Towards Embedded Neuroemotional Control Codesign

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