Evaluation of Varies Model Order in GA-Optimized Parameter Estimation of Toothbrush Rig System

Parameter estimation is a vital part in constructing the best model of a dynamic system. This paper analyzed the performance of toothbrush rig parameter estimation using different model orders. Parameter estimation process of the system is performed through system identification. The approximate mathematical model that resemble the real system is obtained when the output is measured after loading the input signal. The application of real-coded genetic algorithm (RCGA) is proposed as optimization method in estimating the parameters of dynamic system. The best model is obtained by optimizing the objective function of mean squared errors. The performance is analyzed to get the approximate model of the real system using three different model orders with 10 times analysis for each model. A few criteria have been considered which are the optimization result of objective function, time execution and validation process. Real- coded genetic algorithm indicates that parameter estimation with model order 3 is chosen as the best model or the dynamic system as it has the highest performance compared to others

OUTPUT BASED INPUT SHAPING FOR OPTIMAL CONTROL OF SINGLE LINK FLEXIBLE MANIPULATOR

Endpoint residual vibrations and oscillations due to flexible and rigid body motions are big challenges in control of single link flexible manipulators, it makes positioning of payload difficult especially when using various payloads. This paper present output based input shaping with two different control algorithms for optimal control of single link flexible manipulators. Output based filter (OBF) is designed using the signal output of the system and then incorporated with both linear quadratic regulator (LQR) and PID separately for position and residual vibration control. The Robustness of these control algorithms are tested by changing the payloads from 0g to30g, 50g and 70g in each case. Based on MATLAB simulation results and time response analysis, LQR-OBF outperformed the PID-OBF in both tracking and vibration reduction

Vibrations and Intelligent Tracking Control of Single Link Flexible Manipulator

Residual vibrations and oscillations at the Endpoint due to flexible body motions are big challenges in control of single link flexible manipulators. This makes payloads positioning very difficult and hence less efficient and low productivity. In this paper, a hybrid intelligent control of single link flexible manipulator is proposed. Output-based filter (OBF) was designed using the signal output of the system to suppress the tip deflections and it was incorporated with both linear quadratic regulator (LQR) controller and fuzzy logic controller for set point tracking control of the system. Based on the Simulation results, it was observed that, a good tracking and significant tip deflections reduction was achieved. This was measured using the time response analysis. OBF-LQR performed better and more compatible then OBF-fuzzy

Evolutionary swarm algorithm for modelling and control of horizontal flexible plate structures

Numerous advantages offered by the horizontal flexible structure have attracted increasing industrial applications in many engineering fields particularly in the airport baggage conveyor system, micro hand surgery and semiconductor manufacturing industry. Nevertheless, the horizontal flexible structure is often subjected to disturbance forces as vibration is easily induced in the system. The vibration reduces the performance of the system, thus leading to the structure failure when excessive stress and noise prevail. Following this, it is crucial to minimize unwanted vibration so that the effectiveness and the lifetime of the structure can be preserved. In this thesis, an intelligent proportional-integral-derivative (PID) controller has been developed for vibration suppression of a horizontal flexible plate structure. Initially, a flexible plate experimental rig was designed and fabricated with all clamped edges boundary conditions at horizontal position. Then, the data acquisition and instrumentation systems were integrated into the experimental rig. Several experimental procedures were conducted to acquire the input-output vibration data of the system. Next, the dynamics of the system was modeled using linear auto regressive with exogenous, which is optimized with three types of evolutionary swarm algorithm, namely, the particle swarm optimization (PSO), artificial bee colony (ABC) and bat algorithm (BAT) model structure. Their effectiveness was then validated using mean squared error, correlation tests and pole zero diagram stability. Results showed that the PSO algorithm has superior performance compared to the other algorithms in modeling the system by achieving lowest mean squared error of 6103947.4 , correlation of up to 95 % confidence level and good stability. Next, five types of PID based controllers were chosen to suppress the unwanted vibration, namely, PID-Ziegler Nichols (ZN), PID-PSO, PID-ABC, Fuzzy-PID and PID-Iterative Learning Algorithm (ILA). The robustness of the controllers was validated by exerting different types of disturbances on the system. Amongst all controllers, the simulation results showed that PID tuned by ABC outperformed other controllers with 47.60 dB of attenuation level at the first mode (the dominant mode) of vibration, which is equivalent to 45.99 % of reduction in vibration amplitude. By implementing the controllers experimentally, the superiority of PID-ABC based controller was further verified by achieving an attenuation of 23.83 dB at the first mode of vibration and 21.62 % of reduction in vibration amplitude. This research proved that the PID controller tuned by ABC is superior compared to other tuning algorithms for vibration suppression of the horizontal flexible plate structure