The Magneto-rheological (MR) fluid damper is prevalent in the field of semi-active suspension whose viscosity changes by the change of magnetic field passing through the damping fluid. In this study, a semi-active suspension quarter car model is employed as a plant. The Bingham model of MRF damper is exploited with PID and Fuzzy + PID controllers. The current is controlled by the controllers according to the quarter car chassis disturbance. The step road profile is used as an input disturbance to the suspension system. The displacement of sprung mass is analyzed in terms of time and frequency domain. The maximum power spectral density of acceleration for step response with Fuzzy + PID is reduced by 87.28 % as compared to passive suspension whereas PID reduced only 79.95 %. This indicates that the MRF damper with right tuned Fuzzy + PID controller provide a safer ride compared to PID controller and passive suspension.
Active Vibration Control (AVC) is well known nowadays as an optimum technique in vibration suppression of flexible structures. Due to the complexity of the dynamics system of flexible structures, vibration control process is quite a challenge. In this paper, the vibration control of flexible plate using classical proportional feedback gain controller method is studied, experimentally. The AVC-P controller design is implemented to a full clamped flexible plate system to evaluate its vibration attenuation performance. The system's dynamic model considering the collocated placement of the sensor and actuator is derived within the LabVIEW environment. The first five frequencies of vibration mode were obtained. The result indicated that the AVC-P controller propose possessed the ability to attenuate vibration of the flexible structure.
Active vibration control (AVC) techniques show promising results to reduce unwanted vibration level of flexible structures at any desired location. In this paper, the application of non-parametric identification method using feedforward neural networks (FNNs) to model a flexible beam structure for AVC system is presented. An experimental study was carried out to collect input-output dataset of a flexible beam system. The flexible beam was excited using a pseudo-random binary sequence (PRBS) force signal before acquiring the dynamic response of the system. A non-parametric modelling approach of the system was proposed based on feed-forward neural networks (FNNs) while its weight and bias parameters were optimised using chaotic-enhanced stochastic fractal search (SFS) algorithm. The performance of modified SFS algorithm to train a nonlinear auto-regressive exogenous model (NARX) structure FNNs-based model of the system was then compared with its predecessor and with several well-known metaheuristic algorithms. Correlation tests were used to validate the obtained model. Based on the proposed method, a small mean squared error value has been achieved in the validation phase. Considering both convergence rate and result accuracy simultaneously, the chaotic modified SFS algorithm performs significantly better than other training algorithms. In conclusion, the development of a non-parametric model of the flexible beam structure was conducted and validated for future investigations on active vibration control techniques.
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