Aghil Yousefi-Koma

A novel technique for optimal placement of piezoelectric actuators on smart structures

The problem of positioning of actuators and sensors on smart materials has been a point of interest in recent years. This is due to the fact that in many practical applications there are limitations in space, weight, etc. of the smart structures, which make the problem of positioning more complex. In addition, it is required that the actuators/sensors have the best possible performance. The development of smart structures technology in recent years has provided numerous opportunities for vibration control applications. The use of piezoelectric ceramics or polymers has shown great promise in the development of this technology. The employment of piezoelectric material as actuators in vibration control is beneficial because these actuators only excite the elastic modes of the structures without exciting the rigid-body modes. This is important since very often only elastic motions of the structures are needed to be controlled. The purpose of this paper is to introduce a novel approach developed for optimizing the location of piezoelectric actuators for vibration suppression of flexible structures. A flexible fin with bonded piezoelectric actuators is considered in this study. The frequency response function (FRF) of the system is then recorded and maximization of the FRF peaks is considered as the objective function of the optimization algorithm to find the optimal placement of the piezoelectric actuators on the smart fin. Three multi-layer perceptron neural networks are employed to perform surface fitting to the discrete data generated by the finite element method (FEM). Invasive weed optimization (IWO), a novel numerical stochastic optimization algorithm, is then employed to maximize the weighted summation of FRF peaks. Results indicate an accurate surface fitting for the FRF peak data and an optimal placement of the piezoelectric actuators for vibration suppression is achieved.

Optimal tuning of PID controllers using Artificial Bee Colony algorithm

In this paper, an efficient method based on Artificial Bee Colony (ABC) metaheuristic is implemented for tuning PID controllers. Considering performance indices presented in the literature, benchmark plants of different orders and time delays are controlled by PID controllers with optimum gains. Results clearly demonstrate that the employed method has outperformed other techniques such as fuzzy modeling and genetic algorithm resulting in designs with minimum error, overshoot and settling time.

Optimization of Piezoelectric Actuator Configuration on a Flexible Fin for Vibration Control using Genetic Algorithms

This study presents a novel approach to optimizing the configuration of piezoelectric actuators for vibration control of a flexible aircraft fin. The fitness (cost) function for optimization using a genetic algorithm is derived directly from the frequency response function (FRF) obtained from a finite element model of the fin. In comparison to existing approaches, this method allows optimization on much more complex geometries where the derivation of an analytical fitness function is prohibitive or impossible. This technique is applied to two optimization problems for vibration control of the fin. First, the position of a single actuator is optimized anywhere within a judiciously pre-determined area of the fin using a genetic algorithm for polynomial surface fitting of the FRF in order to obtain a continuous fitness function. Next, the configuration of a pre-determined number of up to 48 separate actuators is optimized within the same area. The optimization approach is verified against experimental results obtained from a set of 12 actuators fixed to an experimental model of the fin.

Dynamic Analysis of Electrostatically Actuated Nanobeam Based on Strain Gradient Theory

In this study, dynamic response of a micro- and nanobeams under electrostatic actuation is investigated using strain gradient theory. To solve the governing sixth-order partial differential equation, mode shapes and natural frequencies of beam using Euler–Bernoulli and strain gradient theories are derived and then compared with classical theory. Galerkin projection is utilized to convert the partial differential equation to ordinary differential equations representing the system mode shapes. Accuracy of proposed one degree of freedom model is verified by comparing the dynamic response of the electrostatically actuated micro-beam with analogue equation and differential quadrature methods. Moreover, the static pull-in voltages of micro-beams found by one DOF model are compared with the reported data in literature. The main advantage of proposed method based on the Galerkin method is its simplicity and also its low computational cost in analyzing the dynamic and static responses of micro- and nanobeams. Additionally, effect of axial force, beam thickness and applied voltage are analyzed. The results obtained based on strain gradient theory, are compared with classical and modified couple stress theories which are the special cases of the strain gradient theory. It is shown that strain gradient theory leads to higher frequency and lower amplitude in comparison with two other theories.

The use of ‘particle swarm’ to optimize the control system in a PZT laminated plate

In many active noise and vibration control systems it is desired to reduce the vibration and also the noise emitted by flexible structures. With this objective the task of the controller is to control the maximum number of modes allowed by the control systems limitations. Some of the key parameters in the control system design for this purpose are the location, number and size of actuators and sensors on a flexible structure. In this paper a simply-supported thin plate with laminated piezoelectric sensors and actuators is studied for noise and vibration attenuation. For this purpose, a performance index based on Hankel singular values of the system is selected. The resulting nonlinear optimization problem is solved using a new particle swarm optimization (PSO) algorithm. The results are compared with a genetic algorithm and verified with a simple state-feedback controller.

Nano-resonator frequency response based on strain gradient theory

This paper aims to explore the dynamic behaviour of a nano-resonator under ac and dc excitation using strain gradient theory. To achieve this goal, the partial differential equation of nano-beam vibration is first converted to an ordinary differential equation by the Galerkin projection method and the lumped model is derived. Lumped parameters of the nano-resonator, such as linear and nonlinear springs and damper coefficients, are compared with those of classical theory and it is demonstrated that beams with smaller thickness display greater deviation from classical parameters. Stable and unstable equilibrium points based on classic and non-classical theories are also compared. The results show that, regarding the applied dc voltage, the dynamic behaviours expected by classical and non-classical theories are significantly different, such that one theory predicts the un-deformed shape as the stable condition, while the other theory predicts that the beam will experience bi-stability.To obtain the frequency response of the nano-resonator, a general equation including cubic and quadratic nonlinearities in addition to parametric electrostatic excitation terms is derived, and the analytical solution is determined using a second-order multiple scales method.Based on frequency response analysis, the softening and hardening effects given by two theories are investigated and compared, and it is observed that neglecting the size effect can lead to two completely different predictions in the dynamic behaviour of the resonators. The findings of this article can be helpful in the design and characterization of the size-dependent dynamic behaviour of resonators on small scales.

Design and Analysis of Robust Minimax LQG Controller for an Experimental Beam Considering Spill-Over Effect

In this brief the design and analysis of an optimal robust minimax linear quadratic Gaussian (LQG) control of vibration of a flexible beam is studied. The analysis is performed by transforming the minimax LQG control design problem to its equivalent mixed sensitivity design problem. The first six modes of the beam in the frequency range of 0-700 Hz are selected for control purpose. Among these modes, three modes in the frequency range of 100-400 Hz are used for control, while the other three modes are left as the uncertainty of modeling. Both the model and the uncertainty are measured based on experimental data. The nominal model is identified from frequency response data and the uncertainty is presented by frequency weighted multiplicative modeling method. For the augmented plant consisting of the nominal model and its accompanied uncertainty, a minimax LQG controller is designed. Analysis and tradeoff between robust stability and robust performance is shown by selecting two different choices of uncertainty modeling. Simulation results used to show how the uncertainty weights can be tuned so that the proposed robust controller increase the damping of the system in its resonance frequencies and maintain the robust stability of the feedback system at the same time.

Optimal gait planning for humanoids with 3D structure walking on slippery surfaces

In this study, a gait optimization routine is developed to generate walking patterns which demand the lowest friction forces for implementation. The aim of this research is to fully address the question “which walking pattern demands the lowest coefficient of friction amongst all feasible patterns?”. To this end, first, the kinematic structure of the considered 31 DOF (Degrees of Freedom) humanoid robot is investigated and a closed-form dynamics model for its lower-body is developed. Then, the medium through which the walking pattern generation is conducted is presented. In this medium, after designing trajectories for the feet and the pelvis, the joint space variables are obtained, using the inverse kinematics. Finally, by employing a genetic algorithm (GA), an optimization process is conducted to generate walking patterns with the minimum Required Coefficient Of Friction (RCOF). Six parameters are adopted to parameterize the pelvis trajectory and are exploited as the design variables in this optimization procedure. Also, a parametrical study is accomplished to address the effects of some other variables on RCOF. For comparison purposes, a tip-over Stability Margin (SM) is defined, and an optimization procedure is conducted to maximize this margin. Finally, the proposed gait planning procedure is implemented on SURENA III, a humanoid robot designed and fabricated in CAST, to validate the developed simulation procedure. The obtained results reveal merits of the proposed optimal gait planning procedure in terms of RCOF.

A shape memory alloy spring-based actuator with stiffness and position controllability

In this paper, a one-degree-of-freedom actuator that is based on shape memory alloy (SMA) springs is developed and tested. The use of SMA springs allows a larger actuation workspace and controllable stiffness than SMA wires. It is shown that the actuator demonstrates a good positioning accuracy in addition to a higher level of stiffness control. A strategy based on the variable structure control method is implemented for simultaneous displacement and stiffness control. It is shown that the actuator’s position is always kept within a boundary layer defined around the desired position while a preferred stiffness is also obtained in the actuator. Such an actuator could be used to develop flexible mechanical systems which need to adapt to environmental changes in the form of external loading variation.

ACTIVE VIBRATION CONTROL OF SEISMICALLY EXCITED STRUCTURES BY ATMDS: STABILITY AND PERFORMANCE ROBUSTNESS PERSPECTIVE

This paper demonstrates the trade-off between nominal performance and robustness in intelligent and conventional structural vibration control schemes; and, proposes a systematic treatment of stability robustness and performance robustness against uncertainty due to structural damage. The adopted control strategies include an intelligent genetic fuzzy logic controller (GFLC) and reduced-order observer-based (ROOB) controllers based on pole-placement and linear quadratic regulator (LQR) conventional schemes. These control strategies are applied to a seismically excited truss bridge structure through an active tuned mass damper (ATMD). Response of the bridge-ATMD control system to earthquake excitation records under nominal and uncertain conditions is analyzed via simulation tests. Based on these results, advantages of exploiting heuristic intelligence in seismic vibration control, as well as some complexities arising in realistic conventional control are highlighted. It has been shown that the coupled effect of spill-over (due to reduction and observation) and mismatch between the mathematical model and the actual plant (due to uncertainty and modeling errors) can destabilize the conventional closed-loop system even if each is alone tolerated. Accordingly, the GFLC proves itself to be the dominant design in terms of the compromise between performance and robustness.