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Genetic algorithms based adaptive active vibration control of a flexible structure
Abstract
This paper investigates the development of an active vibration control (AVC) mechanism for a flexible plate structure using a genetic modelling strategy where the utilisation of genetic algorithms (GAs) for dynamic modelling of the system is considered. The global search technique of GAs is used to obtain a dynamic model of a flexible plate structure based on one-step-ahead (OSA) prediction and verified within the AVC system. The GA based AVC algorithm thus developed is implemented within a flexible plate simulation environment and its performance in the reduction of deflection at the centre of the plate is assessed. The validation of the algorithm is presented in both the time and frequency domains. An assessment of the results thus obtained is given in comparison to the AVC system using conventional recursive least squares (RLS) method. Investigations reveal that the developed GA based AVC system performs better in the suppression of vibration of a flexible plate structure compared to an RLS based AVC system.
... Force is applied at distance, x, from the fixed end at time, t, and the resulting deflection from its stationary position is denoted by u(x,t) and y(x,t) respectively. The motion of the beam in transverse vibration is formulated by fourth-order partial differential equation (PDE) that yields the following equation [9] and [19]: ...
... Finite difference (FD) method is chosen to obtain the numerical solution of the PDE in Eq. (1). Simulations of flexible plate and beam via FD method are easy to implement and the method has been proven effective in investigating dynamics behavior of structures [4], [5], [9], [19] and [20]. The beam is discretized into a finite number of equal-length sections (segments), each of length, Δx, and the deflection of beam at the end of each segment is sampled at a constant time, Δt. ...
This paper presents the development of dynamic model of a flexible beam structure using finite difference method. A Simple Proportional (P) control scheme is applied to suppress vibration at the tip of the flexible beam. The performance of P controller is studied by gradually increasing manually the proportional gain until significant attenuation of the vibration is observed. Then the controller is further extended to self-tune the proportional gain by using an intelligent mechanism known as Proportional Iterative Learning Algorithms (P-type ILA). The robustness of both controllers in suppressing the vibration is investigated by changing the beam's physical parameter, applying disturbance at different segments and amplitudes respectively. The simulation results clearly revealed the effectiveness and robustness of a self-tuning proportional control over conventional P control scheme as active vibration control of a flexible beam.
... Force is applied at distance, x, from the fixed end at time, t, and the resulting deflection from its stationary position is denoted by u(x,t) and y(x,t) respectively. The motion of the beam in transverse vibration is formulated by fourth-order partial differential equation (PDE) that yields the following equation [9] and [19]: ...
... Finite difference (FD) method is chosen to obtain the numerical solution of the PDE in Eq. (1). Simulations of flexible plate and beam via FD method are easy to implement and the method has been proven effective in investigating dynamics behavior of structures [4], [5], [9], [19] and [20]. The beam is discretized into a finite number of equal-length sections (segments), each of length, Δx, and the deflection of beam at the end of each segment is sampled at a constant time, Δt. ...
This paper presents the development of dynamic model of a flexible beam structure using finite difference method. A Simple Proportional (P) control scheme is applied to suppress vibration at the tip of the flexible beam. The performance of P controller is studied by gradually increasing manually the proportional gain until significant attenuation of the vibration is observed. Then the controller is further extended to self-tune the proportional gain by using an intelligent mechanism known as Proportional Iterative Learning Algorithms (P-type ILA). The robustness of both controllers in suppressing the vibration is investigated by changing the beam's physical parameter, applying disturbance at different segments and amplitudes respectively. The simulation results clearly revealed the effectiveness and robustness of a self-tuning proportional control over conventional P control scheme as active vibration control of a flexible beam.
... GAs are computationally simple and are not limited by assumptions about the search space (Goldberg, 1989). GAs have been successfully applied to engineering search and optimization problems such as system identification (Kristinsson and Dumont, 1992; Li, 1999; Puangdownreong, 2006) and control (Darus, 2004; Hossain et al., 1995; Ghaffari et al., 2007). ...
... GAs are computationally simple and are not limited by assumptions about the search space (Goldberg, 1989). GAs have been successfully applied to engineering search and optimization problems such as system identification (Kristinsson and Dumont, 1992; Li, 1999; Puangdownreong, 2006) and control (Darus, 2004; Hossain et al., 1995; Ghaffari et al., 2007). ...
This paper focuses on an identification technique based on genetic algorithms (GAs) with application to rectangular flexible plate systems for active vibration control. A real coded GA with a new truncation-based selection strategy of individuals is developed, to allow fast convergence to the global optimum. A simulation environment characterizing the dynamic behavior of a flexible rectangular plate system is developed using the central finite difference (FD) techniques. The plate thus developed is excited by a uniformly distributed random disturbance and the input–output data of the system acquired is used for black-box modeling the system with the GA optimization using an autoregressive model structure. Model validity tests based on statistical measures and output prediction are carried out. The prediction capability of the model is further examined with unseen data. It is demonstrated that the GA gives faster convergence to an optimum solution and the model obtained characterizes the dynamic system behavior of the system well.
... The motion of the beam in transverse vibration is, thus, governed by the well-known fourth-order partial differential equation (PDE) [14], [18] ...
Yes This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.
... The motion of the beam in transverse vibration is, thus, governed by the well-known fourth-order partial differential equation (PDE) [14], [18] ...
This correspondence presents an investigation into the comparative performance of an active vibration control (AVC) system using a number of intelligent learning algorithms. Recursive least square (RLS), evolutionary genetic algorithms (GAs), general regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) algorithms are proposed to develop the mechanisms of an AVC system. The controller is designed on the basis of optimal vibration suppression using a plant model. A simulation platform of a flexible beam system in transverse vibration using a finite difference method is considered to demonstrate the capabilities of the AVC system using RLS, GAs, GRNN, and ANFIS. The simulation model of the AVC system is implemented, tested, and its performance is assessed for the system identification models using the proposed algorithms. Finally, a comparative performance of the algorithms in implementing the model of the AVC system is presented and discussed through a set of experiments.
In the field of measurement technology, the use of unmanned aerial vehicles is becoming more and more popular. For many measurement tasks, the use of such devices offers many advantages in terms of cost and measurement effort. However, the occurring vibrations and disturbances are a signiϐicant disadvantage for the application of these devices for several measurement tasks. Within the scope of this work, a platform for measurement devices is developed. The platform is designed speciϐically for use on drones. The task of the platform is to isolate measurement equipments mounted on it from the drone disturbances. For this purpose we go through the product development process according to VDI 2221 to design a mechanical model of the platform. Then, control strategies are applied to isolate the platform. Since the disturbances acting on a drone are not always stationary, two control strategies known for their ability to handle uncertain systems are used. One of them comes from the ϐield of acoustic.
This paper presents a new approach of proportional-integral-derivative (PID) controller tuning via an evolutionary algorithm that optimally suppresses the vibration of a flexible beam system using a piezoelectric actuator. The system’s dynamic model is identified based on autoregression with exogenous input (ARX) structure using recursive least square. The input-output data were obtained experimentally. This ARX model represents the physical system and is used for the controller optimization process. Evolutionary algorithms such as differential evolution (DE) and genetic algorithms (GA) were applied to optimize and tune the controller parameters offline based on a defined performance index, i.e. mean square error of the vibration signals. The optimum PID parameters were validated experimentally. The performance of PID tuned by DE and GA are compared with conventional PID tuning (using Ziegler Nichols method). Experimental study showed that PID tuned by DE and GA offer a better transient response than the conventional tuning method.
This paper presents the development of dynamic model of flexible beam structure using finite difference method. The simulated model is validated by comparing the resonance modes with the theoretical values. Simple proportional control scheme is used as active vibration control (AVC) algorithm for the system. Simulation study of the control system is done by gradually increasing the proportional gain until it reaches the optimum value. The effectiveness of this control scheme is observed based on its capability to suppress unwanted vibration of the beam. The attenuation of the beam is validated through time and frequency responses. The effect of proportional gain on the dynamic behavior of the actuator is also reported. Simulation is done using LabVIEW graphical programming environment. Results of the study clearly reveal the effectiveness of proportional control scheme in reducing the vibration of the beam.
This research presents an investigation into a new population inheritance approach using a concept taken from the recessive
trait idea for evolutionary optimization. Evolutionary human inheritance recessive trait idea is used to enhance the effectiveness
of the traditional genetic algorithms. The capability of the modified approach is explored by two examples (i) a mathematical function of two variables, and (ii) an active vibration control of a flexible beam system. Finally, a comparative performance for convergence is presented and
discussed to demonstrate the merits of the modified genetic algorithms approach over the traditional ones.
A retrospective review of the development of active noise control systems is presented, arguing that the design of active noise control (ANC) systems should be considered from a control systems point of view. This approach provides a design methodology that accounts for the design parameters of the system which determine its performance, thereby producing an ANC system that reduces the problems associated with, and the limited practical success of, previous techniques. Based on this argument, the fundamental conditions required for cancellation are derived in terms of the power spectral densities of the primary and secondary waves. These conditions are in turn related to the geometry-related (incorporating the acoustic response of the propagation medium) and source-related parameters of the system. From these conditions, the control structures employed in current ANC systems are examined and compared with the reported applications. A method for the design of controllers for use in ANC systems with broadband compact noise sources suitable for implementation on digital signal processing devices is presented. Using this method, experimental results using differing controllers are illustrated and discussed for both synthetic and practical sources. Finally, current developments in ANC systems are summarised and areas for further work are suggested.
A design method is presented for active noise control (ANC)
systems, in a three-dimensional nondispersive propagation medium with
compact broadband noise sources. The design and implementation of the
controller as a digital filter with a fixed transfer function is
presented and verified through a set of practical experiments. The
concept of self-tuning control as a combination of system identification
and control is developed within the ANC system framework. Moreover, as
required by the application, a supervisory level of control based on a
performance criterion is added that automatically activates self-tuning
control. The algorithm is implemented on a high-speed digital signal
processor and tested on the exhaust noise of a 100 cc motorcycle. The
experimental results are presented and compared with those of a system
employing a fixed digital filter controller
Describes results obtained from applying genetic algorithms to the
problem of detecting targets in image data. The method described is a
two-layered approach, with the first layer providing a
focus-of-attention function for the second layer. The first layer is
called a Screener and selects subimages from the original image data to
be processed by the second layer, called the Classifier. The Screener
reduces the computational load of the system. Each layer consists of a
set of linear operators (filters) applied directly to the image data. A
genetic algorithm is applied to populations of filters based on fitness
criteria. The authors note that the statistical classifier chosen for
the Classifier stage drives the evolution of filters that are useful for
that classifier to make good discriminations