Opposition-based spiral dynamic algorithm with an application to optimize type-2 fuzzy control for an inverted pendulum system

This paper presents two variants of the Opposition-based Spiral Dynamic Algorithm (ObSDA) for an application to optimize a type-2 fuzzy logic controller for an inverted pendulum system. Spiral Dynamic Algorithm (SDA) is a group-based optimization algorithm formulated based on the concept of a natural spiral phenomenon on earth. It has the theory of diversification and intensification in its strategy, which allows the algorithm to present itself as a good deterministic type of optimization tool to solve various engineering problems. Despite the good concept and strategy, the algorithm still suffers from getting trapped in a local optima solution. This is due to the limitation of the deterministic strategy that prevents the search agents from sufficiently exploring the whole feasible search space. The search operation only occurs within the area covered by the search agents, and thus there is a low opportunity to thoroughly diverse outside the covered area. Quasi-reflected and Quasi opposition-based strategies were incorporated into the SDA to overcome the exploration problem of the search agents. It helped the search agents to explore the opposite location of the current location of the agents. The opposition strategy also offered varying step sizes to the agents during the movement. The proposed QR-ObSDA and Q-ObSDA were tested on various benchmark functions comprising multimodal and unimodal fitness landscapes. They are also applied to optimize a type-2 fuzzy logic controller for an inverted pendulum system in comparison to SDA, Spotted Hyena Optimizer, Tunicate Swarm Algorithm, and Sooty Tern Optimization Algorithm. A statistical analysis on the accuracy achievement was conducted using Friedman and Wilcoxon Sign Rank methods. The result had shown that the proposed ObSDA variants had outperformed the original SDA in locating the theoretical optima solution of the benchmark functions. Application of the control problem had shown the accuracy performance of ObSDA variants had significantly improved compared to the existing SDA variants and outperformed the other three optimization algorithms

Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems

In this paper, a new variant of Manta Ray Foraging Optimization (MRFO) algorithm is introduced to deal with real parameter constrained optimization problem. Gradient-based Mutation MRFO (GbM-MRFO) is derived from basic strategy of MRFO and synergized with the Gradient-based Mutation strategy. MRFO is a recently new introduced algorithm that consists of strategy of foraging adopted by Manta Ray while Gradient-based Mutation (GbM) is a feasibility-and solution repair strategy adopted from ϵ-Matrix-Adaptation Evolution Strategy (ϵ-MAES). MRFO is proven to solve artificial benchmark-function test by relatively good performance compared to several state-of-the-art algorithm while GbM is a productive approach to repair solution which led to improve the feasibility of the solution throughout the search by using Jacobian approximation in finite differences. GbM-MRFO turn out to be a competitive optimization algorithm on solving constrained optimization problem of Three-bar Truss problem. The performance of GbM-MRFO is proven to be efficient in solving the problems by providing lighter weight of truss with better accuracy of solution

Performance Comparisan Between Sliding Mode Control (Smc) And Pd-Pid Controllers For A Nonlinear Inverted Pendulum System

The objective of this paper is to compare the time specification performance between conventional controller PID and modern controller SMC for an inverted pendulum system. The goal is to determine which control strategy delivers better performance with respect to pendulum's angle and cart's position. The inverted pendulum represents a challenging control problem, which continually moves toward an uncontrolled state. Two controllers are presented such as Sliding Mode Control (SMC) and Proportional- Integral-Derivatives (PID) controllers for controlling the highly nonlinear system of inverted pendulum model. Simulation study has been done in Matlab Mfile and simulink environment shows that both controllers are capable to control multi output inverted pendulum system successfully. The result shows that Sliding Mode Control (SMC) produced better response compared to PID control strategies and the responses are presented in time domain with the details analysis

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

Hybrid spiral-bacterial foraging algorithm for a fuzzy control design of a flexible manipulator

A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and a bacterial foraging algorithm (BFA) is presented in this article. A spiral model is incorporated into the chemotaxis of the BFA algorithm to enhance the capability of exploration and exploitation phases of both SDA and BFA with the aim to improve the fitness accuracy for the SDA and the convergence speed as well as the fitness accuracy for BFA. The proposed algorithm is tested with the Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, and its performance in terms of accuracy is compared with its predecessor algorithms. Consequently, for solving a complex engineering problem, the proposed algorithm is employed to obtain and optimise the fuzzy logic control parameters for the hub angle tracking of a flexible manipulator system. Analysis of the performance test with the benchmark functions shows that the proposed algorithm outperforms its predecessor algorithms with significant improvements and has a competitive performance compared to other well-known algorithms. In the context of solving a real-world problem, it is shown that the proposed algorithm achieves a faster convergence speed and a more accurate solution. Moreover, the time-domain response of the hub angle shows that the controller optimised by the proposed algorithm tracks the desired system response very well

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

Elitism-crossover barnacle mating optimization and its application to PID controller design for a buck converter

This paper presents an elitism-crossover barnacle mating optimization (ECBMO). It is an improvement of barnacle mating optimization (BMO). The original BMO suffers from local optima problem leading to a low accurate solution. A new method of offspring generation is adopted into the original BMO structure. Some features of the best-so-far solution are incorporated into the generated offspring. The accuracy performance of the proposed algorithm is tested on several IEEE functions. A statistical analysis is conducted to compare its performance over the original BMO. It is also applied to optimize proportional integral derivative (PID) parameters for controlling output voltage of a buck converter. Result of benchmark functions test shows the proposed algorithm has attained higher accuracy for all functions compared to BMO algorithm. Application on the real problem shows both algorithms control the converter voltage satisfactorily. However, the ECBMO has achieved more optimal PID parameters and leading to a better output voltage response

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

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

A Novel Adaptive Spiral Dynamic Algorithm for Global Optimization

This paper presents a novel adaptive spiral dynamic algorithm for global optimization. Through a spiral model, spiral dynamic algorithm has a balanced exploration and exploitation strategy. Defining suitable value for the radius and displacement in its spiral model may lead the algorithm to converge with high speed. The dynamic step size produced by the model also allows the algorithm to avoid oscillation around the optimum point. However, for high dimension problems, the algorithm may easily get trapped into local optima. This is due to the incorporation of a constant radius and displacement in the model. In order to solve the problem, a novel adaptive formulation is proposed in this paper by varying the radius and displacement of the spiral model. The proposed algorithm is validated with various dimensions of unimodal and multimodal benchmark functions. Furthermore, it is applied to parameter optimization of an autoregressive with exogenous terms dynamic model of a flexible manipulator system. Comparison with the original spiral dynamic algorithm shows that the proposed algorithm has better accuracy. Moreover, the time domain and frequency domain responses of the flexible manipulator model shows that the proposed algorithm outperforms its predecessor algorithm