Control of A 2 D.O.F direct drive robot arm using integral sliding mode control

High accuracy trajectory tracking is a very challenging topic in direct drive robot control. This is due to the nonlinearities and input couplings present in the dynamics of the robot arm. This thesis is concerned with the problems of modelling and control of a 2 degree of freedom direct drive arm. The research work is undertaken in the following five developmental stages; Firstly, the complete mathematical model of a 2 DOF direct drive robot arm including the dynamics of the brushless DC motors actuators in the state variable form is to be developed. In the second stage, the state variable model is to be decomposed into an uncertain model. Then, the Integral Sliding Mode Controller is applied to the robot arm. In the forth stage, perform the simulation. This is done through the simulation on the digital computer using MATLAB/SIMULINK as the platform. Lastly, the performance of Integral Sliding Mode Controller is to be compared with an Independent Joint Linear Control

Inclined ergometer to enhance FES-assisted indoor rowing exercise performance

Improving the FES-assisted indoor rowing exercise (FES-rowing) performance enables the spinal cord injury (SCI) people to perform hybrid FES-exercise in a higher level of intensity. High level of exercise volume and intensity can play a big role in prevention of cardiovascular disease, type 2 diabetes and obesity which is a significant threat to the health of people with chronic SCI. FES-rowing can be enhanced to achieved the high level exercise through the arrangement of the rowing ergometer. In this paper, the performance of FES-rowing using an adjustable inclined rowing ergometer is investigated. Two different methods to enhance the FES-rowing performance using inclined ergometer are implemented. A model of the adjustable inclined ergometer and humanoid are developed using the Visual Nastran (vN4D) software environment and validated by the experimental work. Fuzzy logic control is implemented to control the knee and elbow trajectories for smooth rowing manoeuvre. The generated level of electrical stimulations for activation of quadriceps and hamstrings muscles are recorded and analysed. The FES-rowing efficiency for both methods have been defined and illustrated. The results show the inclined ergometer with upper body effort is the best performance in enhancing the FES-rowing

A Comparative Review of Hand-Eye Calibration Techniques for Vision Guided Robots

Hand-eye calibration enables proper perception of the environment in which a vision guided robot operates. Additionally, it enables the mapping of the scene in the robots frame. Proper hand-eye calibration is crucial when sub-millimetre perceptual accuracy is needed. For example, in robot assisted surgery, a poorly calibrated robot would cause damage to surrounding vital tissues and organs, endangering the life of a patient. A lot of research has gone into ways of accurately calibrating the hand-eye system of a robot with different levels of success, challenges, resource requirements and complexities. As such, academics and industrial practitioners are faced with the challenge of choosing which algorithm meets the implementation requirements based on the identified constraints. This review aims to give a general overview of the strengths and weaknesses of different hand-eye calibration algorithms available to academics and industrial practitioners to make an informed design decision, as well as incite possible areas of research based on the identified challenges. We also discuss different calibration targets which is an important part of the calibration process that is often overlooked in the design process

Rehabilitation system for paraplegic patients using mind machine interface; a conceptual framework

Mind-Machine Interface (MMI) is a newly surfaced term in the field of control engineering and rehabilitation systems. This technique, coupled with the existing functional electrical stimulation (FES) systems, can be very beneficial for effective rehabilitation of disabled patients. This paper presents a conceptual framework for the development of MMI based FES systems for therapeutic aid and function restoration in spinal cord injured (SCI) paraplegic patients. It is intended to acquire thought modulated signals from human brain and then use these signals to command and control FES as desired by the patient. The proposed setup can significantly assist the rehabilitation and recovery of paraplegic patients due to the ease of control for the user

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

The quadriceps muscle of knee joint modelling using neural network approach: Part 2

— Artificial neural network has been implemented in many filed, and one of the most famous estimators. Neural network has long been known for its ability to handle a complex nonlinear system without a mathematical model and has the ability to learn sophisticated nonlinear relationships provides. Theoretically, the most common algorithm to train the network is the backpropagation (BP) algorithm which is based on the minimization of the mean square error (MSE). Subsequently, this paper displays the change of quadriceps muscle model by using fake savvy strategy named backpropagation neural system nonlinear autoregressive (BPNN-NAR) model in perspective of utilitarian electrical affectation (FES). A movement of tests using FES was driven. The data that is gotten is used to develop the quadriceps muscle model. 934 planning data, 200 testing and 200 endorsement data set are used as a part of the change of muscle model. It was found that BPNNNARMA is suitable and efficient to model this type of data. A neural network model is the best approach for modelling nonlinear models such as active properties of the quadriceps muscle with one input, namely output namely muscle force

Lithium iron phosphate intelligent SOC prediction for efficient electric vehicle

This paper presents modelling techniques for Lithium Iron Phosphate (LiFePO4) battery in an electric vehicle. Artificial intelligence techniques namely multi-layered perceptron neural network (MLPNN) and Elman recurrent neural network are devised to estimate the energy remained in the battery bank which referred to state of charge (SOC). The New European Driving Cycle (NEDC) test data is used to excite the cells in driving cycle-based conditions under varied temperature range [0-55]°C. Accurate SOC prediction is a key function for satisfactory implementation of Battery Supervisory System (BSS). It is demonstrated that artificial intelligence methods can be effectively used with highly accurate results. The accuracy of the modeling results is demonstrated through validation and correlation tests

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Functional Electrical Stimulation (FES) cycling, or FES-Cycling, holds great therapeutic potential for individuals with paralysis, such as those with Spinal Cord Injury (SCI), traumatic brain injury, or stroke, aiming to restore mobility. However, the nonlinear nature of the musculoskeletal system poses a significant challenge in controlling FES-Cycling. To address this, an integrated closed-loop phase angle fuzzy-based system was developed. This system offers real-time control by adjusting stimulation intensity (pulse width) within the range of 50 to 200μs while maintaining a constant frequency of 35Hz, thereby ensuring precise pedaling trajectory and cadence patterns. An experimental study involved three healthy individuals (Cases A, B, and C) and one individual with hemiplegia stroke (Case D). Results showed that the proposed system consistently reduced average angle trajectory errors for Cases A, B, and C, with values of 2.6945, 3.2958, and 2.9922 degrees, respectively. Case D, affected by hemiplegia stroke, faced greater challenges and exhibited a higher error of 3.4562 degrees. Fatigue resistance, evaluated through fatigue indices, showed promising results for Cases A, B, and C with values of 0.10778, 0.06866, and 0.04603, respectively. However, Case D experienced higher fatigue (0.2304) due to the unique challenges of hemiplegia stroke. These findings highlight the effectiveness of the proposed control system in optimizing FES-Cycling, particularly for healthy individuals. For individuals with paralysis, like Case D, further research is needed to adapt the system to their specific conditions and cycling patterns. This system holds the potential for enhancing FES-Cycling as a therapeutic strategy and warrants additional investigation and customization for different patient populations

Force Control for One Degree of Freedom Haptic Device using PID Controller

Haptics has been used as an additional feedback to increase human experience to the environment over years and its application has been widening into education, manufacturing and medical. The most developed haptic devices are for rehabilitation purpose. The rehabilitation process usually depends on the physiotherapist. But, it requires repetitive movements for long-term rehabilitation, thus haptic devices are needed. Most of the rehabilitation devices are included with haptic feedback to enhance therapy exercise during the rehabilitation process. However, the devices come with multiple degrees of freedom (DOF), complex design and costly. Rehabilitation for hand movement such as grasping, squeezing, holding and pinching usually does not need an expensive and complex device. Therefore, the goal of this study is to make an enhancement to One DOF Haptic Device for grasping rehabilitation exercise. It is improved to perform a force control mechanism with few types of conventional controller which are Proportional (P) controller, Proportional-Integral (PI) controller, Proportional-Derivative (PD) controller and Proportional-Integral-Derivative (PID) controller. The performance of the haptic device is tested with different conventional controller to obtain the best proposed controller based on the lowest value of Mean Square Error (MSE). The results show that PID Controller (MSE = 0.0028) is the most suitable for the haptic device with Proportional gain (Kp), Integral gain (Ki) and Derivative gain (Kd) are 1.3, 0.01 and 0.2 respectively. The force control mechanism can imitate the training motion of grasping movement for the patient

Control strategy of segregation on HVAC energy efficiency as non propulsion electrical hotel load in EV

A maximum cruising range is adversely affected by electric power consumption of auxiliary electric components for heating and cooling. Under the assumption that range anxiety is one of the main barriers to the electrification of an Electric Vehicle (EV). Considering a heating ventilation air-conditioner (HVAC) system as a non propulsion electrical hotel load consumed more energy that respect to time and thermodynamic circumstances. This paper presents a control strategy on segregation of non propulsion electrical hotel load energy efficiency for an EV. A good HVAC control system, enhance the thermal comfort that will lead to better energy efficiency and driving performance. A concept of enthalpy in analyzing the heat exchange involved to decomposed into sensible heat and latent heat for dynamics of temperature and humidity ratios of the car compartment derivation after taking into account of the differences of ASHF (Apparatus Sensible Heat Factor) and RSHF (Room Sensible Heat Factor). The goal of a PD-type Fuzzy Logic Control (PD-FLC) based controller is to satisfy the convergence and equilibrium properties. The proposed strategy is composed of three part: temperature error, temperature change of error, and the relative humidity error to control the heater damper voltage, outside air damper voltage and blower fan voltage. The control strategy is evaluated in a Matlab/Simulink simulation environment, which performs a better energy efficiency