Robots in human life

Proceedings of 23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines

Hybrid iterative learning control of a flexible manipulator

This paper presents an investigation into the development of a hybrid control scheme with iterative learning for input tracking and end-point vibration suppression of a flexible manipulator system. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller using hub angle and hub velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate a non-collocated proportional-integral-derivative (PID) controller with iterative learning for control of vibration of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid iterative learning control scheme is assessed in terms of input tracking and level of vibration reduction in comparison to a conventionally designed PD-PID control scheme. The effectiveness of the control scheme in handling various payloads is also studied

Real-time embedded system of super twisting-based integral sliding mode control for quadcopter UAV

This paper presents the development of set-point weighting-based integral super-twisting sliding mode control (SISTASMC) with full-order state observers to overcome the control challenges encountered with nonlinear and underactuated systems. Quadcopter UAV form is a good example of underactuated systems, and this is selected in this research for validating the developed control. A comparative assessment through experimental validation is conducted between SISTASMC and Set-point weighting-based Integral Sliding Mode Control to demonstrate the performance of both controllers. Based on predetermined performance criteria, the results obtained demonstrate good performance of SISTASMC in dealing with uncertainty

PD-Fuzzy Control of Single Lower Limb Exoskeleton for Hemiplegia Mobility

This paper presents studies in the design and control of single leg exoskeleton for hemiplegia mobility in simulation environment. The exoskeleton is designed to support the affected side of the hemiplegia patient while the other leg functions normally. Hip, knee and ankle joints for both humanoid leg and exoskeleton of the affected side are controlled using PD-Fuzzy control to obtain the required natural torque to allow the exoskeleton to compensate for the deficiency in affected leg to achieve normal symmetric gait. The controller is implemented in MATLAB, and the system behaviour observed in Visual Nastran 4D (VN4D) during simulation. Simulation results show that the exoskeleton can support the humanoid with the required augmentation using the proposed design and control

A modified bats echolocation-based algorithm for solving constrained optimisation problems

A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems

Multi-Objective Genetic Algorithm Optimisation Approach for the Geometrical Design of an Active Noise Control Systems

This paper focuses on the geometrical design of active noise control (ANC) in free- field propagation medium. The development and performance assessment uses genetic optimisation techniques to arrange system components so as to satisfy several performance requirements, such as physical extent of cancellation, controller design restriction and system stability. The ANC system design can be effectively addressed if it is considered as multi – objective optimisation problems. The multi-objective genetic algorithms (MOGAs) are well suited to the design of an ANC system and the approach used for it is based on a multi - objective method, with which the physical extent of cancellation and relative stability assessment are dealt with simultaneously

Neural Network-Based Li-Ion Battery Aging Model at Accelerated C-Rate

Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network

Forward and backward motion control of wheelchair on two wheels

The challenge in designing wheelchair on two wheels involves the design and implementation of suitable control strategies for a two wheeled wheelchair to perform comparably similar to a normal four wheeled wheelchair. It is important to note that a wheelchair on two wheels is expected not to take much space during mobility as compared to when it is on four wheels. Moreover, disabled people are encouraged and expected to perform most activities that others can do and hence lead an independent life. Thus, wheelchairs on two wheels are needed for disabled persons to perform some of the essential tasks in their living and work environments. In this research a model of the standard wheelchair is developed as a test and verification platform using Visual Nastran software. Novel fuzzy logic control strategies are designed for lifting up the chair transforming a four-wheeled wheelchair to a two-wheeled wheelchair) and maintaining stability and balance while on two wheels. Furthermore, position control for forward and backward mobility of the wheelchair on two wheels is developed using fuzzy logic control. Simulation results of the proposed control strategy are presented and discussed

Genetic Algorithms Based Approach for Designing Spring Brake Orthosis – Part Ii: Control of FES Induced Movement

Spring brake orthotic swing phase for paraplegic gait is initiated through releasing the brake on the knee mounted with a torsion spring. The stored potential energy in the spring, gained from the previous swing phase, is solely responsible for swing phase knee flexion. Hence the later part of the SBO operation, functional electrical stimulation (FES) assisted extension movement of the knee has to serve an additional purpose of restoring the spring potential energy on the fly. While control of FES induced movement as such is often a challenging task, a torsion spring, being antagonistically paired up with the muscle actuator, as in spring brake orthosis (SBO), only adds to the challenge. Two new schemes are proposed for the control of FES induced knee extension movement in SBO assisted swing phase. Even though the control schemes are closed-loop in nature, special attention is paid to accommodate the natural dynamics of the mechanical combination being controlled (the leg segment) as a major role playing feature. The schemes are thus found to be immune from some drawbacks associated with both closed-loop tracking as well as open-loop control of FES induced movement. A leg model including the FES knee joint model of the knee extensor muscle vasti along with the passive properties is used in the simulation. The optimized parameters for the SBO spring are obtained from the earlier part of this work. Genetic algorithm (GA) and multi-objective GA (MOGA) are used to optimize the parameters associated with the control schemes with minimum fatigue as one of the control objectives. The control schemes are evaluated in terms of three criteria based on their ability to cope with muscle fatigue