Modelling and Control of SCARA Manipulator

AbstractNowadays manipulators are getting more complex due to the development of the motor drives and the nonlinearities of the manipulator dynamics. Due to complexity of this system, the modeling process will become more complicated especially if modeled by using mathematical representation or white box approach. Therefore Computed Aided Design (CAD) modeling approach is more suitable to be applied in this system. In this paper presents the development of the CAD model of robot arm by using SolidWorks software. Then a controller based on Proportional-integral-derivative (PID) has been designed in simulation environment by using Matlab/Simulink platform. This paper shows the advantages of the combination of MATLAB and SolidWorks. SolidWorks is able to ease the modeling process. The performance of PID controller for 4 Degree of Freedom (DOF) of SCARA (Selective Compliance Articulated Robot Arm) manipulator has been assessed for first 2 DOF and shown good results

Optimized Fuzzy Control For Natural Trajectory Based Fes- Swinging Motion

The use of electrical signals to restore the function of paralyzed muscles is called functional electrical stimulation (FES). FES is a promising method to restore mobility to individuals paralyzed due to spinal cord injury (SCI). A crucial issue of FES is the control of motor function by the artificial activation of paralyzed muscles due to the various characteristics of the underlying physiological/biomechanical system. Muscle response characteristics are nonlinear and time-varying. After developing a nonlinear model describing the dynamic behavior of the knee joint and muscles, a closed-loop approach of control strategy to track the reference trajectory is assessed in computer simulations. Then, the controller was validated through experimental work. In this approach only the quadriceps muscle is stimulated to perform the swinging motion by controlling the amount of stimulation pulsewidth. An approach of fuzzy trajectory tracking control of swinging motion optimized with genetic algorithm is presented. The results show the effectiveness of the approach in controlling FES-induced swinging motion in the simulation as well as in the practical environment

Application of Fuzzy Logic in Multi-Mode Driving for a Battery Electric Vehicle Energy Management

Energy management system is an area of emerging interest in a full electric vehicle research. With the increasing moves to a more sustainable vehicle, there is a need to extend the battery range that simultaneously satisfying the conflicting demand between battery capacity and vehicle weight or volume. This paper presents a research conducted in the Universiti Putra Malaysia, focusing on the energy management strategy of a battery-powered electric vehicle. Three vehicle driving modes; sport, comfort, and eco have been individually modelled. Each mode is capable to dominate different driving environments; highway, suburban, and urban. In European driving cycle simulation test, comfort and eco modes have shown large extension in driving range with the maximum of 7.33% and 19.70% respectively. However the speeds have been confined by certain specific limits. The proposed of integrated multi-mode driving using fuzzy logic has enabled an adaptive driving by automatically select the driving parameters based on the speed conditions. The results have proven its ability in reducing the energy consumption as much as 32.25%, and increasing the driving range of 4.21% without downgrading the speed performance

Framework of Lower-Limb Musculoskeletal Modeling for FES Control System Development

In recent years, the demand of interest in functional electrical stimulation (FES) is increasing due to the applications especially on spinal cord injury (SCI) patients. Numerous studies have been done to regain mobility function and for health benefits especially due to FES control development for the paralyzed person. In this paper, the existing general framework modeling methods have been reviewed and the new modeling framework approach has been discussed. In general modeling and simulation can greatly facilitate to test and tune various FES control strategies. In fact, the modeling of musculoskeletal properties in people with SCI is significantly challenging for researchers due to the complexity of the system. The complexities are due to the complex structural anatomy, complicated movement and dynamics, as well as indeterminate muscle function. Although there are some models have been developed, the complexities of the system resulting mathematical representation that have a large number of parameters which make the model identification process even more difficult. Therefore, a new approach of modeling has been presented which is comparatively less burdened compared with mathematical representations. Hence this musculoskeletal model can be used for FES control system development

Fuzzy-based temperature and humidity control for HVAC of electric vehicle

Vehicles are the people’s main means of transportation and thermal comfort in the vehicle cabin plays an important role. The heating, ventilating, and air-conditioning (HVAC) operation system in the vehicle cabin decides the degree of comfort, traffic safety as well as health of the occupants. The challenge of designing the HVAC control is to automatically achieve the thermal comfort, regardless of time varying weather conditions. Since most HVAC systems are complex with nonlinearity, distributed parameters, and multivariable therefore many classical controls do not necessarily yield a satisfactory control performance. This paper presents the development of temperature and humidity control strategy for HVAC based on Fuzzy Logic Control (FLC). The goal of the FLC-based temperature control is to satisfy the convergence and equilibrium property. The simulation test results have been shown a satisfactory to control the temperature and humidity

Hybridized classification algorithms for data classification applications: A review

Machine-based classification usually involves some computer programs, known as algorithms, developed using several mathematical formulations to accelerate the automated classification process. Along with the exponential increase in the size and computational complexity of the data today, such optimized, robust, agile and reliable computational algorithms are required which can efficiently carry out these conforming classification tasks. In this review paper, deterministic optimization techniques have been analysed that are efficiently employed for machine learning applications. In this review, systematic literature review approach has been adopted in which 200 research articles were downloaded from which 100 latest articles has been selected based on the most commonly employed neural networks’ techniques. Moreover, the reported neural networks techniques based on Back Propagation Neural Network (BPNN), Recurrent Neural Networks (RNNs) Algorithm and Levenberg-Marquardt (LM) with several hybridized classification algorithms based on optimization techniques have been indicated that are commonly used to optimize and benefit the classification process