MODELLING AND CONTROL OF MULTI-FINGERED ROBOT HAND USING INTELLIGENT TECHNIQUES

Research and development of robust multi-fingered robot hand (MFRH) have been going on for more than three decades. Yet few can be found in an industrial application. The difficulties stem from many factors, one of which is that the lack of general and effective control techniques for the manipulation of robot hand. In this research, a MFRH with five fingers has been proposed with intelligent control algorithms. Initially, mathematical modeling for the proposed MFRH has been derived to find the Forward Kinematic, Inverse Kinematic, Jacobian, Dynamics and the plant model. Thereafter, simulation of the MFRH using PID controller, Fuzzy Logic Controller, Fuzzy-PID controller and PID-PSO controller has been carried out to gauge the system performance based parameters such rise time, settling time and percent overshoot

Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

This paper proposes two applications of Grey Wolf Optimizer (GWO) algorithms to a path planning (PaPl) problem and a Proportional-Integral (PI)-fuzzy controller tuning problem. Both optimization problems solved by GWO algorithms are explained in detail. An off-line GWO-based PaPl approach for Nonholonomic Wheeled Mobile Robots (NWMRs) in static environments is proposed. Once the PaPl problem is solved resulting in the reference trajectory of the robots, the paper also suggests a GWO-based approach to tune cost-effective PI-fuzzy controllers in tracking control problem for NWMRs. The experimental results are demonstrated through simple multiagent settings conducted on the nRobotic platform developed at the Politehnica University of Timisoara, Romania, and they prove both the effectiveness of the two GWO-based approaches and major performance improvement

Optimal Fuzzy Controller Design for Autonomous Robot Path Tracking Using Population-Based Metaheuristics

This researchwas funded by projects TecNM-5654.19-P and DemocratAI PID2020-115570GB-C22.In this work, we propose, through the use of population-based metaheuristics, an optimization method that solves the problem of autonomous path tracking using a rear-wheel fuzzy logic controller. This approach enables the design of controllers using rules that are linguistically familiar to human users. Moreover, a new technique that uses three different paths to validate the performance of each candidate configuration is presented. We extend on our previous work by adding two more membership functions to the previous fuzzy model, intending to have a finer-grained adjustment. We tuned the controller using several well-known metaheuristic methods, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), GreyWolf Optimizer (GWO), Harmony Search (HS), and the recent Aquila Optimizer (AO) and Arithmetic Optimization Algorithms. Experiments validate that, compared to published results, the proposed fuzzy controllers have better RMSE-measured performance. Nevertheless, experiments also highlight problems with the common practice of evaluating the performance of fuzzy controllers with a single problem case and performance metric, resulting in controllers that tend to be overtrained.TecNM-5654.19-PDemocratAI PID2020-115570GB-C2

Goal-seeking Behavior-based Mobile Robot Using Particle Swarm Fuzzy Controller

Behavior-based control architecture has successfully demonstrated their competence in mobile robot development. Fuzzy logic system characteristics are suitable to address the behavior design problems. However, there are difficulties encountered when setting fuzzy parameters manually. Therefore, most of the works in the field generate certain interest for the study of fuzzy systems with added learning capabilities. This paper presents the development of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A goal-seeking behaviors based on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations and experiments with MagellanPro mobile robot have been performed to analyze the performance of the algorithm.  The promising results have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System

In this article, a robot manipulator is controlled by the PID controller in a closed loop system with unit feedback. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). In this case the Ant colony Optimization algorithm (ACO) is used to find the best gain parameters of the PID. The Ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located, this concept is applied to tuning PID parameters by minimizing the objective function such that the robot manipulator has improved performance characteristics. This work uses the Matlab Simulink environment, First, after obtaining the system model, the ant colony algorithm is used to determine the proper coefficients p, i, and Kd in order to minimize the trajectory errors of the two joints of the robot manipulator. Then, the parameters will implement in the robot system. According to the results of the computer simulations, the proposed method (ACO-PID) gives a system that has a good performance compared with the classical PID

Novel metaheuristic hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation

© 2014 Elsevier B.V. All rights reserved. This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. Spiral dynamic algorithm (SDA) has faster convergence speed and good exploitation strategy. However, the incorporation of constant radius and angular displacement in its spiral model causes the exploration strategy to be less effective hence resulting in low accurate solution. Bacteria chemotaxis on the other hand, is the most prominent strategy in bacterial foraging algorithm. However, the incorporation of a constant step-size for the bacteria movement affects the algorithm performance. Defining a large step-size results in faster convergence speed but produces low accuracy while de.ning a small step-size gives high accuracy but produces slower convergence speed. The hybrid algorithms proposed in this paper synergise SDA and bacteria chemotaxis and thus introduce more effective exploration strategy leading to higher accuracy, faster convergence speed and low computation time. The proposed algorithms are tested with several benchmark functions and statistically analysed via nonparametric Friedman and Wilcoxon signed rank tests as well as parametric t-test in comparison to their predecessor algorithms. Moreover, they are used to optimise hybrid Proportional-Derivative-like fuzzy-logic controller for position tracking of a flexible manipulator system. The results show that the proposed algorithms significantly improve both convergence speed as well as fitness accuracy and result in better system response in controlling the flexible manipulator

Dynamic Modeling and Torque Feedforward based Optimal Fuzzy PD control of a High-Speed Parallel Manipulator

Dynamic modeling and control of high-speed parallel manipulators are of importance due to their industrial applications deployed in production lines. However, there are still a number of open problems, such as the development of a precise dynamic model to be used in the model-based control design. This paper presents a four-limb parallel manipulator with Schönflies motion and its simplified dynamic modeling process. Then, in order to fix the issue that computed torque method control (CTC) will spend a lot of time to calculate dynamic parameters in real-time, offline torque feedforward-based PD (TFPD) control law is adopted in the control system. At the same time, fuzzy logic is also used to tune the gains of PD controller to adapt to the variation of external disturbance and compensate the un-modeled uncertainty. Additionally, bottom widths of membership functions of fuzzy controller are optimized by bat algorithm. Finally, three controllers of CTC, TFPD and bat algorithm-based torque feedforwad fuzzy PD controller (BA-TFFPD) are compared in trajectory tracking simulation. Fro the result, compared with TFPD and CTC, BA-TFFPD can lead faster transient response and lower tracking error, which prove the validity of BA-TFFPD