Neuromorphic Smart Controller for Seismically Excited Structures

2013 ◽  
pp. 216-233
Author(s):  
Yeesock Kim ◽  
Changwon Kim ◽  
Reza Langari
Keyword(s):  
Control System ◽  
Orbitofrontal Cortex ◽  
Mr Damper ◽  
Dynamic Responses ◽  
Building Structure ◽  
Linear Quadratic ◽  
Seismic Excitations ◽  
Linear Quadratic Gaussian ◽  
The One ◽  
Brain Emotional Learning

In this chapter, an application of a neuromorphic controller is proposed for hazard mitigation of smart structures under seismic excitations. The new control system is developed through the integration of a brain emotional learning-based intelligent control (BELBIC) algorithm with a proportional-integral-derivative (PID) compensator and a clipped algorithm. The BELBIC control is based on the neurologically inspired computational model of the amygdala and the orbitofrontal cortex. A building structure employing a magnetorheological (MR) damper under seismic excitations is investigated to demonstrate the effectiveness of the proposed hybrid clipped BELBIC-PID control algorithm. The performance of the proposed hybrid neuromorphic controller is compared with the one of a variety of conventional controllers such as a passive, PID, linear quadratic Gaussian (LQG), and emotional control systems. It is shown that the proposed hybrid neuromorphic controller is effective in improving the dynamic responses of structure-MR damper systems under seismic excitations, compared to the benchmark controllers.

Improvement Shifting Control of Continuously Variable Transmission (CVT) by Using PID, Pole Placement and LQG

2013 ◽  
Vol 446-447 ◽  
pp. 1165-1170
Author(s):  
Shu Yuan Ma ◽  
Bdran Sameh ◽  
Saifullah Samo ◽  
Aymn Bary
Keyword(s):  
Control System ◽  
Control Method ◽  
Operating Conditions ◽  
Pole Placement ◽  
Speed Ratio ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Variable Transmission ◽  
Simulation Results ◽  
Pole Placement Controller

In this paper, the CVT shifting control system based on vehicle operating conditions is modeled and simulated using MATLAB/SIMULINK. The modeling stage begins with the derivation of required mathematical model to illustrate the CVT shifting control system. Then, Linear Quadratic Gaussian (LQG), Proportional- Integrated-Derivative (PID) and Pole Placement are applied for controlling the shifting speed ratio of the modeled CVT shifting system. Simulation results of shifting controllers are presented in time domain and the results obtained with LQG are compared with the results of PID and Pole placement technique. Finally, the performances of shifting speed ratio controller systems are analyzed in order to choose which control method offers the better performance with respect to the desired speed ratio. According to simulation results, the LQG controller delivers better performance than PID and Pole Placement controller.

Linear quadratic Gaussian controller design for plasma current, position and shape control system in ITER

1999 ◽  
Vol 45 (1) ◽  
pp. 55-64 ◽  
Author(s):  
V Belyakov ◽  
A Kavin ◽  
V Kharitonov ◽  
B Misenov ◽  
Y Mitrishkin ◽  
...  
Keyword(s):  
Control System ◽  
Shape Control ◽  
Controller Design ◽  
Plasma Current ◽  
Current Position ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian

Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm

2018 ◽  
Vol 25 (2) ◽  
pp. 273-285 ◽  
Author(s):  
Masoud Bozorgvar ◽  
Seyed Mehdi Zahrai
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Fuzzy Controller ◽  
Mr Damper ◽  
Base Shear ◽  
Linear Quadratic ◽  
Inference System ◽  
Story Drift ◽  
Story Building ◽  
Neural Fuzzy

This research presents designing a control system to reduce seismic responses of structures. Semi-active control of a magnetorheological (MR) damper is used to improve seismic behavior of a 3-story building implementing neural-fuzzy controller made of adaptive neuro-fuzzy inference system (ANFIS) to determine damper input voltage. Both premise and consequent parameters of fuzzy membership and output functions of ANFIS have the ability for training and improvement but most researchers have focused on just consequent parameters. In order to optimize the controller performance, an approach is proposed in this paper where both premise and consequent parameters of fuzzy functions in an ANFIS network are adjusted simultaneously by genetic algorithm (GA). In order to assess the effectiveness of the designed control system, its function is numerically studied on a benchmark 3-story building and is compared to those of a neural network predictive control (NNPC) algorithm, linear quadratic Gaussian (LQG) and clipped optimal control (COC) systems in terms of seismic performance. The results showed desirable performance of the (ANFIS +GA + membership functions + result function) ANFIS–GA–MFR controller in considerably reducing the structure responses under different earthquakes. The proposed controller showed 30 and 39% reductions in peak story drift (J1) and normed story drift (J4) respectively compared to the NNPC controller, 32 and 44% reductions in J1 and J4 respectively compared to the LQG controller, and 27 and 38% reductions in J1 and J4 respectively compared to the COC controller. The proposed controller effectively reduced acceleration and base shear level compared to the uncontrolled state and had a performance relatively similar to those of three other controllers – for instance, it reduced the maximum level acceleration (J2) 10% higher than COC. Also, the results showed that the ANFIS–GA–MFR controller has more efficiency than the basic ANFIS controller, on average about 20%.

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