Active vibration control of flexible beam incorporating recursive least square and neural network algorithms

In recent years, active vibration control (AVC) has emerged as an important area of scient ific study especially for vibrat ion suppression of flexible structures. Flexible structures offer great advantages in contrast to the conventional structures, but necessary action must be taken for cancelling the unwanted vibration. In this research, a simulation algorithm represent ing flexible beam with specific condit ions was derived from Euler Bernoulli beam theory. The proposed finite difference (FD) algorithm was developed in such way that it allows the disturbance excitat ion at various points. The predicted resonance frequencies were recorded and validated with theoretical and experimental values. Subsequent ly, flexible beam test rig was developed for collecting data to be used in system ident ificat ion (SI) and controller development. The experimental rig was also utilised for implementation and validat ion of controllers. In this research, parametric and nonparametric SI approaches were used for characterising the dynamic behaviour of a lightweight flexible beam using input - output data collected experimentally. Tradit ional recursive least square (RLS) method and several artificial neural network (ANN) architectures were utilised in emulat ing this highly nonlinear dynamic system here. Once the model of the system was obtained, it was validated through a number of validation tests and compared in terms of their performance in represent ing a real beam. Next, the development of several convent ional and intelligent control schemes with collocated and non-collocated actuator sensor configurat ion for flexible beam vibrat ion attenuation was carried out. The invest igat ion involves design of convent ional proportional-integral-derivat ive (PID) based, Inverse recursive least square active vibrat ion control (RLS-AVC), Inverse neuro active vibration control (Neuro-AVC), Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain controllers. All the developed controllers were tested, verified and validated experimentally. A comprehensive comparat ive performance to highlight the advantages and drawbacks of each technique was invest igated analyt ically and experimentally. Experimental results obtained revealed the superiorit y of Inverse RLS-AVC with gain controller over convent ional method in reducing the crucial modes of vibration of flexible beam structure. Vibration attenuation achieved using proportional (P), proportional-integral (PI), Inverse RLS-AVC, Inverse Neuro- AVC, Inverse RLS-AVC with gain and Inverse Neuro-AVC with gain control strategies are 9.840 dB, 6.840 dB, 9.380 dB, 8.590 dB, 17.240 dB and 5.770 dB, respectively

Fuzzy PD control of an optically guided long reach robot

This thesis describes the investigation and development of a fuzzy controller for a manipulator with a single flexible link. The novelty of this research is due to the fact that the controller devised is suitable for flexible link manipulators with a round cross section. Previous research has concentrated on control of flexible slender structures that are relatively easier to model as the vibration effects of torsion can be ignored. Further novelty arises due to the fact that this is the first instance of the application of fuzzy control in the optical Tip Feedback Sensor (TFS) based configuration. A design methodology has been investigated to develop a fuzzy controller suitable for application in a safety critical environment such as the nuclear industry. This methodology provides justification for all the parameters of the fuzzy controller including membership fUllctions, inference and defuzzification techniques and the operators used in the algorithm. Using the novel modified phase plane method investigated in this thesis, it is shown that the derivation of complete, consistent and non-interactive rules can be achieved. This methodology was successfully applied to the derivation of fuzzy rules even when the arm was subjected to different payloads. The design approach, that targeted real-time embedded control applicat.ions from the outset, results in a controller implementation that is suitable for cheaper CPU constrained and memory challenged embedded processors. The controller comprises of a fuzzy supervisor that is used to alter the derivative term of a linear classical Proportional + Derivative (PD) controller. The derivative term is updated in relation to the measured tip error and its derivative obtained through the TFS based configuration. It is shown that by adding 'intelligence' to the control loop in this way, the performance envelope of the classical controller can be enhanced. A 128% increase in payload, 73.5% faster settling time and a reduction of steady state of over 50% is achieved using fuzzy control over its classical counterpart

Development Of Position Feedback Sensor Based On Vision Using Neural Network

Pengukuran untuk kedudukan lurus adalah asas dalam pelbagai proses industri terutamanya sebagai penderia suapbalik kedudukan. Enkoder optik adalah antara penderia kedudukan yang banyak digunakan. Bagaimanapun, beberapa kekurangan didapati dalam penggunaannya. Kerja yang dicadangkan ini mengambil pendekatan dengan membina penderia kedudukan menggunakan pengelasan imej. Imej telah dikelaskan menggunakan rangkaian neural. Measurement of linear position is fundamental in many industrial processes especially as positioning feedback. Optical encoder is one of the frequently used position sensor. Unfortunately, there are some disadvantages in the usage of this type of encoder. This proposed work presents an approach to build a position sensor using image classification. The image was classified by using supervised backpropagation neural network. Input image that was fed to the classifier is a grayscale image of the surrounding environment (up view) which was captured to represent the positio

Bayesian estimation of human impedance and motion intention for human-robot collaboration

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance

Modeling and control of flexible link robots

Master'sMASTER OF ENGINEERIN

Review of machine learning methods in soft robotics

Soft robots have been extensively researched due to their flexible, deformable, and adaptive characteristics. However, compared to rigid robots, soft robots have issues in modeling, calibration, and control in that the innate characteristics of the soft materials can cause complex behaviors due to non-linearity and hysteresis. To overcome these limitations, recent studies have applied various approaches based on machine learning. This paper presents existing machine learning techniques in the soft robotic fields and categorizes the implementation of machine learning approaches in different soft robotic applications, which include soft sensors, soft actuators, and applications such as soft wearable robots. An analysis of the trends of different machine learning approaches with respect to different types of soft robot applications is presented; in addition to the current limitations in the research field, followed by a summary of the existing machine learning methods for soft robots