Control and modeling of underwater flexible manipulator structure

The use of flexible structures in many engineering applications is expanding rapidly. Position control is delicate, such as angular position control of a flexible structure especially underwater condition. Flexible structure in an underwater condition often having the problem of the hub angle position as the hub angle is affected by the inline force of the flexible structure underwater. To develop an optimum control system for the horizontal motion of such condition, the operating system must first be identified. A system model of an experimental test rig representing the Underwater Flexible Single Link Manipulator System (UFSLMS), needs to be developed before designing a controller to control the hub angle position. The objectives of this project are to identify the model and develop the controller to control the hub angle position of a UFSLMS. Previous studies have shown that parametric modelling involving Auto Regressive with Exogenous Input model using Recursive Least Squares algorithm, and non-parametric modelling involving Evolution Algorithm are suitable to model the UFSLMS system, with acceptably low Mean Square Error. The project is done by reviewing the UFSLMS dynamic modelling and control methodology. The collection of data from the UFSLMS system will be simulated and identified as the dynamic UFSLMS. A Proportional- Integral-Derivative controller is developed based on the system identification model, using heuristic techniques within MATLAB environment and robustness test is carried out at different magnitudes to determine the robustness of the controller. The performance of the controllers thus developed is verified and validated by simulation on MATLAB SIMULINK. The objectives are achieved when the controller is proven to be stable by effectively control the hub angle position in the horizontal motion underwater

Evolutionary optimisation and real-time self-tuning active vibration control of a flexible beam system

Active vibration control has long been recognised as a solution for flexible beam structure to achieve sufficient vibration suppression. The flexible beam dynamic model is derived according to the Euler Bernoulli beam theory. The resonance frequencies of the beam are investigated analytically and the validity was experimentally verified. This thesis focuses on two main parts: proportional-integralderivative (PID) controller tuning methods based on evolutionary algorithms (EA) and real-time self-tuning control using iterative learning algorithm and poleplacement methods. Optimisation methods for determining the optimal values of proportional-integral-derivative (PID) controller parameters for active vibration control of a flexible beam system are presented. The main objective of tuning the PID controller is to obtain a fast and stable system using EA such as genetic algorithm (GA) and differential evolution (DE) algorithms. The PID controller is tuned offline based on the identified model obtained using experimental input-output data. Experimental results have shown that PID parameters tuned by EA outperformed conventional tuning method in term of better transient response. However, in term of vibration attenuation, the performance between DE, GA and Ziegler-Nichols (ZN) method produced about the same value. For real-time selftuning control, successful design and implementation has been accomplished. Two techniques, self-tuning using iterative learning algorithm and self-tuning poleplacement control were implemented to adapt the controller parameters to meet the desired performances. In self-tuning using iterative learning algorithm, its learning mechanism will automatically find new control parameters. Whereas the self tuning pole-placement control uses system identification in real time and then the control parameters are calculated online. It is observed that self-tuning using iterative learning algorithm does not require accurate model of the plant and control the vibration based on the reference error, but it is unable to maintain its transient performance due to the change of physical parameters. Meanwhile, self-tuning poleplacement controller has shown its ability to maintain its transient performance as it was designed based on the desired closed loop poles where the control system can track changes in the plant and disturbance characteristics at every sampling time. Overall results revealed the effectiveness of both control schemes in suppressing the unwanted vibration over conventional fixed gain controllers

Optimization with Discrete Simultaneous Perturbation Stochastic Approximation Using Noisy Loss Function Measurements

Discrete stochastic optimization considers the problem of minimizing (or maximizing) loss functions defined on discrete sets, where only noisy measurements of the loss functions are available. The discrete stochastic optimization problem is widely applicable in practice, and many algorithms have been considered to solve this kind of optimization problem. Motivated by the efficient algorithm of simultaneous perturbation stochastic approximation (SPSA) for continuous stochastic optimization problems, we introduce the middle point discrete simultaneous perturbation stochastic approximation (DSPSA) algorithm for the stochastic optimization of a loss function defined on a p-dimensional grid of points in Euclidean space. We show that the sequence generated by DSPSA converges to the optimal point under some conditions. Consistent with other stochastic approximation methods, DSPSA formally accommodates noisy measurements of the loss function. We also show the rate of convergence analysis of DSPSA by solving an upper bound of the mean squared error of the generated sequence. In order to compare the performance of DSPSA with the other algorithms such as the stochastic ruler algorithm (SR) and the stochastic comparison algorithm (SC), we set up a bridge between DSPSA and the other two algorithms by comparing the probability in a big-O sense of not achieving the optimal solution. We show the theoretical and numerical comparison results of DSPSA, SR, and SC. In addition, we consider an application of DSPSA towards developing optimal public health strategies for containing the spread of influenza given limited societal resources