Comparison of various optimization criteria for actuator placement for active vibration control of smart composite beam

Position of piezoelectric actuators and sensors on a smart structure directly affects the control performances of a smart structure. In order to improve efficiency of active vibration control of a smart structure, optimization of piezoelectric actuators and sensors placement has been performed. There are various optimization criteria for optimal placement of piezoelectric actuator. The ‘state-of-the-art’ of optimization criteria is presented in [1]. The aim of this paper is to compare control effectiveness of smart composite cantilever beam, where optimal configurations of actuator-sensor pairs were found by using four optimization criteria (LQR based optimization, grammian matrices, performance index and fuzzy optimization strategy). The problem is formulated as multi-input-multi-output (MIMO) model. The beam is discretized by using the finite element method (FEM). The particle swarm optimization (PSO) method is used to find optimal configurations for each configuration

Comparison of various optimization criteria for actuator placement for active vibration control of smart composite beam

Position of piezoelectric actuators and sensors on a smart structure directly affects the control performances of a smart structure. In order to improve efficiency of active vibration control of a smart structure, optimization of piezoelectric actuators and sensors placement has been performed. There are various optimization criteria for optimal placement of piezoelectric actuator. The ‘state-of-the-art’ of optimization criteria is presented in [1]. The aim of this paper is to compare control effectiveness of smart composite cantilever beam, where optimal configurations of actuator-sensor pairs were found by using four optimization criteria (LQR based optimization, grammian matrices, performance index and fuzzy optimization strategy). The problem is formulated as multi-input-multi-output (MIMO) model. The beam is discretized by using the finite element method (FEM). The particle swarm optimization (PSO) method is used to find optimal configurations for each configuration

Intelligent identification and control of a flexible beam

High demands in weight reduction have been observed in many areas. There are many benefits with weight reduction including reducing cost, increase efficiency and pushing the technology beyond the limit. The weight reduction requires lighter materials to be used, therefore less stiff structures are utilized. The less stiff the structure, the more flexible and easier it is to vibrate. The vibration produced by these types of structures may cause a lot of problems, including fatigue failure, resonance failure, defects, and even life. This project studies a type of structure configuration; a flexible cantilever beam. The objectives of this project are to identify the model and to develop the controller for the flexible beam. Previous studies have shown various methods are suitable to identify the system, these include the ones considered in this project; the parametric modelling using Recursive Least Square, as well as the nonparametric modelling using Multilayer Perceptron Neural Network. An experimental rig of flexible cantilever beam is developed for this project to obtain the input data for the system identification. A Proportional-Integral-Derivative controller is developed utilizing both system models identified, using automatic and heuristic tunings techniques within MATLAB environment. The performance developed by the controller is verified through simulations in MATLAB Simulink. The controller is proven to be stable with significant vibration suppression of the flexible beam

Intelligent identification and control of a flexible beam

High demands in weight reduction have been observed in many areas. There are many benefits with weight reduction including reducing cost, increase efficiency and pushing the technology beyond the limit. The weight reduction requires lighter materials to be used, therefore less stiff structures are utilized. The less stiff the structure, the more flexible and easier it is to vibrate. The vibration produced by these types of structures may cause a lot of problems, including fatigue failure, resonance failure, defects, and even life. This project studies a type of structure configuration; a flexible cantilever beam. The objectives of this project are to identify the model and to develop the controller for the flexible beam. Previous studies have shown various methods are suitable to identify the system, these include the ones considered in this project; the parametric modelling using Recursive Least Square, as well as the nonparametric modelling using Multilayer Perceptron Neural Network. An experimental rig of flexible cantilever beam is developed for this project to obtain the input data for the system identification. A Proportional-Integral-Derivative controller is developed utilizing both system models identified, using automatic and heuristic tunings techniques within MATLAB environment. The performance developed by the controller is verified through simulations in MATLAB Simulink. The controller is proven to be stable with significant vibration suppression of the flexible beam

Tip position control of single flexible manipulators based on LQR with the Mamdani model

Flexible manipulators have been actively used in various fields, such as aerospace, industry and medical treatment. It remains that the tip of the flexible manipulator should accurately trail the target trajectory without vibration. This paper proposes a novel method of the tip position control of a single flexible manipulator based on LQR with the Mamdani model. Firstly, using the assumed mode method and the Lagrange equations, the dynamic model of the single flexible manipulator is established. Then, the state equations are derived by the dynamic model. Based on the Mamdani model, the fuzzy algorithm is added to the traditional LQR control, and the self-adaptive adjustment of the LQR control variable R is conducted, which improves the adaptability of the control system. Finally, numerical simulations and experiments are presented. The results demonstrate that the novel control method presented in this paper can rapidly achieve the location in the position control and effectively suppress the elastic vibration of the single flexible manipulator, which has more considerable effect compared with the traditional LQR control method

Simultaneous piezoelectric actuator and sensor placement optimization and control design of manipulators with flexible links using SDRE method

This paper presents a control design for flexible manipulators using piezoelectric actuators bonded on nonprismatic links. The dynamic model of the manipulator is obtained in a closed form through the Lagrange equations. Each link is discretized using finite element modal formulation based on Euler-Bernoulli beam theory. The control uses the motor torques and piezoelectric actuators for controlling vibrations. An optimization problem with genetic algorithm GA is formulated for the location and size of the piezoelectric actuator and sensor on the links. The natural frequencies and mode shapes are computed by the finite element method, and the irregular beam geometry is approximated by piecewise prismatic elements. The State-Dependent Riccati Equation SDRE technique is used to derive a suboptimal controller for a robot control problem. A state-dependent equation is solved at each new point obtained for the variables from the problem, along the trajectory to obtain a nonlinear feedback controller. Numerical tests verify the efficiency of the proposed optimization and control design

A New Active Anti-Vibration System Using a Magnetostrictive Bimetal Actuator

This paper introduces a new vibration reduction system using a magnetostrictive (Fe-Ga alloy) bimetal actuator. The proposed method (i) uses a magnetostrictive bimetal actuator instead of prevalent single material ones that need an auxiliary temperature control system and (ii) utilises a novel disturbance rejection control scheme that eliminates an unknown disturbance, without needing knowledge of its dynamics. In experiments, the vibration source is demonstrated as an unbalanced motor attached to the tip of a cantilever beam, resembling a beam-like element subject to ambiance vibrations. In the first step, the fundamental of this anti-vibration system is introduced and described. Then, analytical and data-driven modelling for the combination of the beam, the motor, and the bimetal is reported. These follow by model validation and impulse response analysis. Then, the proposed control system is introduced in detail. Experimental results indicate that the control system results in 33.6% decrease in beam vibration amplitude. Furthermore, the presented method in this paper can be employed as a design guideline for future applications

Evolutionary algorithms for active vibration control of flexible manipulator

Flexible manipulator systems offer numerous advantages over their rigid counterparts including light weight, faster system response, among others. However, unwanted vibration will occur when flexible manipulator is subjected to disturbances. If the advantages of flexible manipulator are not to be sacrificed, an accurate model and efficient control system must be developed. This thesis presents the development of a Proportional-Integral-Derivative (PID) controller tuning method using evolutionary algorithms (EA) for a single-link flexible manipulator system. Initially, a single link flexible manipulator rig, constrained to move in horizontal direction, was designed and fabricated. The input and output experimental data of the hub angle and endpoint acceleration of the flexible manipulator were acquired. The dynamics of the system was later modeled using a system identification (SI) method utilizing EA with linear auto regressive with exogenous (ARX) model structure. Two novel EAs, Genetic Algorithm with Parameter Exchanger (GAPE) and Particle Swarm Optimization with Explorer (PSOE) have been developed in this study by modifying the original Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. These novel algorithms were introduced for the identification of the flexible manipulator system. Their effectiveness was then evaluated in comparison to the original GA and PSO. Results indicated that the identification of the flexible manipulator system using PSOE is better compared to other methods. Next, PID controllers were tuned using EA for the input tracking and the endpoint vibration suppression of the flexible manipulator structure. For rigid motion control of hub angle, an auto-tuned PID controller was implemented. While for vibration suppression of the endpoint, several PID controllers were tuned using GA, GAPE, PSO and PSOE. The results have shown that the conventional auto-tuned PID was effective enough for the input tracking of the rigid motion. However, for end-point vibration suppression, the result showed the superiority of PID-PSOE in comparison to PID-GA, PID-GAPE and PID-PSO. The performance of the best simulated controller was validated experimentally later. Through experimental validation, it was found that the PID-PSOE was capable to suppress the vibration of the single-link flexible manipulator with highest attenuation of 31.3 dB at the first mode of the vibration. The outcomes of this research revealed the effectiveness of the PID controller tuned using PSOE for the endpoint vibration suppression of the flexible manipulator amongst other evolutionary methods

Modeling and Control of Piezoelectric Actuators

Piezoelectric actuators (PEAs) utilize the inverse piezoelectric effect to generate fine displacement with a resolution down to sub-nanometers and as such, they have been widely used in various micro- and nanopositioning applications. However, the modeling and control of PEAs have proven to be challenging tasks. The main difficulties lie in the existence of various nonlinear or difficult-to-model effects in PEAs, such as hysteresis, creep, and distributive vibration dynamics. Such effects can seriously degrade the PEA tracking control performances or even lead to instability. This raises a great need to model and control PEAs for improved performance. This research is aimed at developing novel models for PEAs and on this basis, developing model-based control schemes for the PEA tracking control taking into account the aforementioned nonlinear effects. In the first part of this research, a model of a PEA for the effects of hysteresis, creep, and vibration dynamics was developed. Notably, the widely-used Preisach hysteresis model cannot represent the one-sided hysteresis of PEAs. To overcome this shortcoming, a rate-independent hysteresis model based on a novel hysteresis operator modified from the Preisach hysteresis operator was developed, which was then integrated with the models of creep and vibration dynamics to form a comprehensive model for PEAs. For its validation, experiments were carried out on a commercially-available PEA and the results obtained agreed with those from model simulations. By taking into account the linear dynamics and hysteretic behavior of the PEA as well as the presliding friction between the moveable platform and the end-effector, a model of the piezoelectric-driven stick-slip (PDSS) actuator was also developed in the first part of the research. The effectiveness of the developed model was illustrated by the experiments on the PDSS actuator prototyped in the author's lab. In the second part of the research, control schemes were developed based on the aforementioned PEA models for tracking control of PEAs. Firstly, a novel PID-based sliding mode (PIDSM) controller was developed. The rational behind the use of a sliding mode (SM) control is that the SM control can effectively suppress the effects of matched uncertainties, while the PEA hysteresis, creep, and external load can be represented by a lumped matched uncertainty based on the developed model. To solve the chattering and steady-state problems, associated with the ideal SM control and the SM control with boundary layer (SMCBL), the novel PIDSM control developed in the present study replaces the switching control term in the ideal SM control schemes with a PID regulator. Experiments were carried out on a commercially-available PEA and the results obtained illustrate the effectiveness of the PIDSM controller, and its superiorities over other schemes of PID control, ideal SM control, and the SMCBL in terms of steady state error elimination, chattering suppression, and tracking error suppression. Secondly, a PIDSM observer was also developed based on the model of PEAs to provide the PIDSM controller with state estimates of the PEA. And the PIDSM controller and the PIDSM observer were combined to form an integrated control scheme (PIDSM observer-controller or PIDSMOC) for PEAs. The effectiveness of the PIDSM observer and the PIDSMOC were also validated experimentally. The superiority of the PIDSMOC over the PIDSM controller with σ-β filter control scheme was also analyzed and demonstrated experimentally. The significance of this research lies in the development of novel models for PEAs and PDSS actuators, which can be of great help in the design and control of such actuators. Also, the development of the PIDSM controller, the PIDSM observer, and their integrated form, i.e., PIDSMOC, enables the improved performance of tracking control of PEAs with the presence of various nonlinear or difficult-to-model effects

Precision Control of Piezo Electric Actuator

芝浦工業大学201