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

Opposition-based manta ray foraging algorithm for global optimization and its application to optimize nonlinear type-2 fuzzy logic control

Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent’s step size relative to the algorithm’s iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively

Stabilizing control of two-wheeled wheelchair with movable payload using optimized interval type-2 fuzzy logic

The control schemes of a wheelchair having two wheels with movable payload utilizing the concept of a double-link inverted pendulum have been investigated in this article. The proposed wheelchair has been simulated using SimWise 4D software considering the most efficient parameters. These parameters are extracted using the spiral dynamic algorithm while being controlled with interval type-2 fuzzy logic controller (IT2FLC). The robustness and stability of the implemented controller are assessed under different situations including standing upright, forward motion and application of varying directions and magnitudes of outer disturbances to movable (up and down) system payload. It is shown that the two-wheeled wheelchair adopted by the newly introduced controller has achieved a 94% drop in torque for both Link1 and Link2 and more than 98% fall in distance travelled in comparison with fuzzy logic control type-1 (FLCT1) controller employed in an earlier design. The present study has further considered the increased nonlinearity and complexity of the additional moving payload. From the outcome of this study, it is obvious that the proposed IT2FLC-spiral dynamic algorithm demonstrates better performance than FLCT1 to manage the uncertainties and nonlinearities in case of a movable payload two-wheel wheelchair system

Control of double link flexible robotic manipulator system

Flexible manipulator is widely used in the implementation of industial robotic due to its advantages such as low weight, low power consumption, higher load capacity, high-speed operation, small actuators and low production costs. However, the position and speed of flexible manipulator system are very difficult to control due to the tip vibration that result in degradation of performance. Modelling and control of a double-link flexible robot manipulator are presented in this study. Controlling the movement of a double-link manipulator, on the other hand, has proven to be a challenging task, especially when a flexible framework is used. Moreover, most double-link flexible manipulator system models are not developed based on real hardware. Hence, this project aims to develop a Solidworks design for double link flexible robotics manipulator (DLFRM) as well as a real hardware prototype. The control position performance of DLFRM was analyzed, and the controllers were tested on a hardware prototype. This project started with a simulation of both controllers, which are PID and FLC. The simulation was designed in Solidworks and exported to Simulink and then converted as Simscape. Then, the hardware for each controller was validated using the control parameter in the simulation. The joints for the robot manipulator were designed in Solidwork and built using 3D printing

Integrated Modelling and Control of Linear Actuator Based Automatic Pedal Pressing Mechanism for Low-Speed Driving in a Road Traffic Delay

Sitting in traffic congestion for hours in a posture that requires recurrent actions of manually pressing the pedal and braking excessively can result in fatigue, especially on the driver's leg and back. This fatigue can have long-term implications and adversely affect the driver's health. Thus, this paper aims to model and develop a control system that utilizes a linear actuator to replace the leg activities involved in pressing and releasing the brake pedal. This approach, combined with the implementation of a PID controller, offers a novel solution to control the vehicle speed by integration with the linear actuator that focus on low-speed driving condition. The design process begins with creating a 3D model using SolidWorks to visualize the movement of the linear actuator and Pedal subsystem. This model is then connected to Matlab-Simulink, where a PID controller is implemented and integrated into the electrical circuit to control the actuator's movement. Integration with the vehicle dynamic model enables a comprehensive analysis of the system's behavior on the vehicle dynamics. This research compares the trial and error method with the Matlab tuner for implementing the PID controller. The performance of the system will be evaluated based on the steady state error, overshoot, rise time, and settling time. The results demonstrate that the Matlab tuner outperforms trial and error method by achieving a faster response and significantly reducing steady state error during robustness testing. With the integration of the linear actuator, the system is capable of tracking the desired speed and has the potential to replace the leg activities involved in pressing and releasing the brake pedal. For future work, validating the proposed mechanism with a physical prototype of the linear actuator and pedal using hardware-in-the-loop techniques poses a challenge, as hardware constraints may vary with different environments

Opposition-sooty tern algorithm for fuzzy control optimization of an inverted pendulum system

This paper presents a novel Opposition-Sooty Tern Algorithm (OSTA) which is an improved version of the original Sooty- Tern Optimization Algorithm (STOA). An opposition scheme is incorporated into the STOA structure. This is to enhance the exploration and exploitation of all searching agents throughout a feasible search area. In solving a real-world problem, the algorithm is applied to optimize parameters of a fuzzy logic model for controlling cart's position and pendulum's angle of an inverted pendulum system. Result of the optimization test shows the OSTA has a better accuracy performance compared to its predecessor algorithm. For controlling the inverted pendulum, both OSTA and STOA acquired sufficiently good control performance for the system. However, the fuzzy control scheme optimized by OSTA has resulted in a better tracking and control performance for both cart's position and pendulum's angle

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

Modelling and Control of a Reconfigurable Multipurpose Wheelchair for Elderly/Disabled Mobility

This research is embarking on development of modelling and designing control strategies for a multi purpose wheelchair as a mobile transporter for elderly and disabled people to move in confined and domestic environments independently. The research is aimed at helping people who have physical weakness/disabilities in their upper and lower extremities to move on their own without human intervention. In this work, a novel reconfiguration which allows multi-task operations using the same wheelchair system with compact and simple mechanism is developed for use in confined domestic environment. It can perform manoeuvrability on flat surfaces, stairs climbing (ascending and descending), standing in the upright position on two wheels and transforms back to standard four wheels with reduced initial torque and reduced tilt angle. The wheelchair model is designed in Visual Nastran 4D (VN4D) software with standard specifications of stairs dimension and size. A humanoid model with approximate weight of 71kg is also developed in solid works and incorporated in VN4D to represent a disabled/elderly person. The wheelchair mechanism is based on the link/cluster rotation by lifting the other pair of wheels at the vertical upright position like an inverted pendulum. The completed model in VN4D is then integrated with Matlab/Simulink for control design and performance evaluation. The challenge resides in an appropriate design and implementation of robust controller for the system to guarantee stability of the overall wheelchair while performing multi-function tasks without falling over. A modular fuzzy logic control mechanism with integrated phases is introduced in this work for the two-wheeled stabilization as the main principle of the overall tasks. It is implemented in the stabilizing/landing for stair climbing and sit-to-stand/stand-to-sit transformation control system. Yaw and linear motions are considered in the stair climbing while seat height extension and suspension mechanism are incorporated during standing/sitting control. Moreover, systematic optimization approach is used for the fuzzy input output scaling parameters using spiral dynamic algorithm for performance comparison purposes with heuristic values. Unique rule bases are implemented in all fuzzy modules and controlled independently. The developed control approaches are evaluated through intensive visual simulation and quantitative assessment to verify the proposed control design