An observer design for active suspension system

The purpose of this paper is to construct an active suspension for a quarter car model with observer design. The proportional-integral sliding mode is chosen as a control strategy, and the road profile is estimated by using an observer design. The performance of the proposed controller will be compared with the linear quadratic regulator by performing extensive computer simulation

Super-opposition spiral dynamic-based fuzzy control for an inverted pendulum system

This paper presents a hybrid spiral dynamic algorithm with a super-opposition spiral dynamic algorithm (SOSDA) strategy. An improvement on the spiral dynamic algorithm (SDA), this method uses a concept centered on opposition-based learning, which is used to evaluate the fitness of agents at the opposite location to the current solution. The SDA is a simple-structured and deterministic type of algorithm, which also performs competitively in terms of solution accuracy. However, its deterministic characteristic means the SDA suffers premature convergence caused by the unbalanced diversification and intensification during its search procedure. Thus, the algorithm fails to achieve highly accurate solutions. It is proposed that adopting super-opposition into the SDA would enable the deterministic and random techniques to complement one another. The SOSDA was tested on four benchmark functions and compared to the original SDA. To analyze the result statistically, the Friedman and Wilcoxon tests were conducted. Furthermore, the SOSDA was applied to optimize the parameters of an interval type-2 fuzzy logic control (IT2FLC) for an inverted pendulum (IP). The statistical results produced by the SOSDA for both benchmark functions and the IP show that the proposed algorithm significantly outperformed the SDA. The SOSDA-based IT2FLC scheme also produced better IP responses than the SDA-based IT2FLC

Adaptive levy flight distribution algorithm for solving a dynamic model of an electric heater

This paper presents an improved version of Levy Flight Distribution (LFD) algorithm. The original LFD is formulated based on the random walk strategy. However, it suffers a premature convergence due to imbalance exploration and exploitation. Consequently, the algorithm produces unsatisfactory performance in terms of its final accuracy achievement. As a solution to the problem, an adaptive scheme of search agents step size is incorporated into the original LFD algorithm. Moreover, a mating strategy is also adopted to improve its stochastic nature throughout the search process. The algorithm is applied to optimize a nonlinear dynamic model of an electric water heater. A fuzzy-based Hammerstein structure is adopted to represent the heater model. It comprises a combination of both linear and nonlinear equations so that it can capture the dynamic behavior of the heater satisfactorily. The proposed adaptive LFD algorithm is compared with the original LFD algorithm. The result shows that the proposed algorithm has attained a better accuracy. It also has captured the dynamic behavior of the heater more adequately

Initial study of multiple excitation source for electrical resistance tomography in steel pipe application

Tomography is a method of reconstructing the image of an object’s interest within the sensing zone. Electrical resistance tomography (ERT) system operates when using current as an excitation source and output voltage is meas-ured at the detection electrode and the research will result in the changes of elec-tric potential distribution. A lot of researches have been made using ERT to iden-tify a liquid-gas regime in the steel pipe focused on improving image resolution of the regime. However, a common excitation source of ERT used only a single excitation. Thus, this research uses COMSOL Multiphysics as a platform for sim-ulation of multiple excitations of electrical resistance tomography for liquid-gas regime identification in steel pipe. The analysis and performance of new simula-tion which applies multiple excitation sources have been compared with the sin-gle excitation. Besides, the project is limited to 54mm inner diameter of the steel pipe. As a conclusion, 50% of the excitation source can increase the image reso-lution of those regimes especially in the middle of the steel pipe

Quasi oppositional—Manta ray foraging optimization and its application to pid control of a pendulum system

This paper presents an improved version of Manta Ray Foraging Optimization (MRFO). MRFO is relatively a single objective optimization algorithm. It was inspired from the behavior of a cartilaginous fish called Manta Ray. Manta Ray applies three strategies in searching foods which are chain, cyclone and somersault foraging. From the study, MRFO is a relatively new developed algorithm and has low convergence rate. However, MRFO has potential to be improved in that aspect. In the meanwhile, Opposition-based Learning (OBL) is a well-known technique in increasing the convergence rate. Therefore, a type of OBL namely Quasi Oppositional-based Learning will be adopted into MRFO in order to increase the possibility of finding the solution by considering the opposite individual location of fitness. This version of MRFO is called as Oppositional-based MRFO (OMRFO). Further, OMRFO was performed on several benchmark function. A statistical non-parametric Wilcoxon Test was conducted to analyze the accuracy of MRFO and OMRFO. Furthermore, the proposed algorithm was applied to an inverted pendulum system. Result from shows that performance of OMRFO is significantly outperformed MRFO after tested in the benchmark functions

Active steering for vehicle system using sliding mode control

The objectives of this thesis are to present a modeling and control of a single-track car model for active steering vehicle system. The sliding mode control strategy will be utilized to overcome various coefficients of road frictions and external disturbances on the system. In order to compensate the disturbances, side slip angles and yaw rate of the vehicle will be observed. The model presented take into account different friction of road coefficients of the system. From the mathematical derivation it is found that the system has fulfilled a matching condition. Extensive computer simulations are performed for various types of disturbances such as crosswind and braking torque. From the simulation results the effect of disturbance attenuation will be observed. The performance of the proposed controller will be compared to the linear quadratic regulator and pole placement techniques. The results showed that the sliding mode control scheme is effectively in attenuating various disturbances for different road coefficients as compared to the LQR and pole placement control schemes. Furthermore, the simulation results also showed that the system is insensitive to the external disturbances and capable to overcome ‘late action’ by the driver due to sudden disturbance on any road conditions

Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system : a comparative assessment

This paper presents investigations into the development of an interval type-2 fuzzy logic control (IT2FLC) mechanism integrated with particle swarm optimization and spiral dynamic algorithm. The particle swarm optimization and spiral dynamic algorithm are used for enhanced performance of the IT2FLC by finding optimised values for input and output controller gains and parameter values of IT2FLC membership function as comparison purpose in order to identify better solution for the system. A new model of triple-link inverted pendulum on two-wheels system, developed within SimWise 4D software environment and integrated with Matlab/Simulink for control purpose. Several tests comprising system stabilization, disturbance rejection and convergence accuracy of the algorithms are carried out to demonstrate the robustness of the control approach. It is shown that the particle swarm optimization-based control mechanism performs better than the spiral dynamic algorithm-based control in terms of system stability, disturbance rejection and reduce noise. Moreover, the particle swarm optimization-based IT2FLC shows better performance in comparison to previous research. It is envisaged that this system and control algorithm can be very useful for the development of a mobile robot with extended functionality

Novel Adaptive Bacterial Foraging Algorithms for Global Optimisation with Application to Modelling of a TRS

In this paper, adaptive bacterial foraging algorithms and their application to solve real world problems is presented. The constant step size in the original bacterial foraging algorithm causes oscillation in the convergence graph where bacteria are not able to reach the optimum location with large step size, hence reducing the accuracy of the final solution. On the contrary, if a small step size is used, an optimal solution may be achieved, but at a very slow pace, thus affecting the speed of convergence. As an alternative, adaptive schemes of chemotactic step size based on individual bacterium fitness value, index of iteration and index of chemotaxis are introduced to overcome such problems. The proposed strategy enables bac- teria to move with a large step size at the early stage of the search operation or during the exploration phase. At a later stage of the search operation and exploitation stage where the bacteria move towards an optimum point, the bacteria step size is kept reducing until they reach their full life cycle. The performances of the proposed algorithms are tested with various dimensions, fitness landscapes and complexities of several standard benchmark functions and they are statistically evaluated and compared with the original algorithm. Moreover, based on the statistical result, non-parametric Friedman and Wilcoxon signed rank tests and parametric t-test are performed to check the significant difference in the performance of the algorithms. The algorithms are further employed to predict a neural network dynamic model of a laboratory-scale helicopter in the hovering mode. The results show that the proposed algorithms outperform the predecessor algorithm in terms of fitness accuracy and convergence speed

Novel hybrid bacterial foraging and spiral dynamics algorithms

This paper presents three novel hybrid optimization algorithms based on bacterial foraging and spiral dynamics algorithms and their application to modelling of flexible maneuvering systems. Hybrid bacteria-chemotaxis spiral- dynamics algorithm is a combination of chemotaxis strategy in bacterial foraging algorithm and linear adaptive spiral dynamics algorithm. Chemotactic behaviour of bacteria is a good strategy for fast exploration if large value of step size is defined in the motion. However, this results in oscillation in the search process and bacteria cannot reach optimum fitness accuracy in the final solution. On the contrary, spiral dynamics provides good exploitation strategy due to its dynamic step size. However, it suffers from getting trapped at local optima due to poor exploration in the diversification phase. Employing the chemotaxis and spiral dynamics strategies at the initial and final stages respectively will thus balance the exploration and exploitation. Hybrid spiral-bacterial foraging algorithm and hybrid chemotaxis-spiral algorithm, on the other hand are developed based on adaptation of spiral dynamics model into chemotaxis phase of bacterial foraging with the aim to guide bacteria movement globally. The proposed algorithms are used to optimize parameters of a linear parametric model of a flexible robot manipulator system. The performances of the proposed hybrid algorithms are presented in comparison to their predecessor algorithms in terms of fitness accuracy, time-domain and frequency-domain responses of the models. The results show that the proposed algorithms achieve better performance

Novel Adaptive Spiral Dynamics Algorithms for Global Optimization

This paper presents adaptive versions of spiral dynamics algorithm (SDA) referred to as adaptive SDA (ASDA). SDA is known as fast computing algorithm due to its simplicity in the structure and it has stable convergence response when approaching the optimum point in the search space. However, the performance of SDA is still poor due to incorporation of single radius value during the whole search process. In ASDA, the spiral radius is made dynamic by employing novel mathematical equations and incorporating non-mathematical fuzzy logic strategy establishing the relationship between fitness value and spiral radius. This results in better performance in terms of convergence speed, accuracy, and total computing time while retaining the simple structure of SDA. Several uni-modal and multi-modal benchmark functions are employed to test the algorithm in finding the global optimum point. The results show that ASDA outperforms SDA in all test functions considered