International Journal of Advanced Mechatronic Systems 

About: International Journal of Advanced Mechatronic Systems is an academic journal published by Inderscience Publishers. The journal publishes majorly in the area(s): Control theory & Adaptive control. It has an ISSN identifier of 1756-8412. Over the lifetime, 220 publications have been published receiving 1148 citations. The journal is also known as: IJAMechS & Advanced mechatronic systems.

A ZigBee-based mobile tracking system through wireless sensor networks

Abstract: Wireless sensor networks have been deployed widely. Sensor networks involve sensor nodes which are very small in size, low in cost and have a short battery-life. One of the critical wireless sensor network applications is localisation and tracking mobile sensor nodes. ZigBee is a new emerging technology for low rate, low power and low range communication networks, which aims to provide long battery life for network devices. In this paper, we discuss various localisation and tracking techniques and categorise these techniques based on the communication between nodes in centralised and decentralised localisation systems. We propose a decentralised ZigBee-based tracking system to detect and track the location of mobile nodes indoors based on the received signal strength (RSS). The proposed tracking system is a range-free system, which does not require additional hardware, depends on a new weight function, and can be deployed wherever the node density is low. The tracking system is implemented by ZigBee sensor devices, and experiments are done to evaluate the proposed tracking system based on accuracy and communication cost.

Selection of optimal parameter set using estimability analysis and MSE-based model-selection criterion

Abstract: Parameter estimation in complex mathematical models is difficult, especially when there are too many unknown parameters to estimate, and the available data for parameter estimation are limited. Estimability analysis ranks parameters from most estimable to least estimable based on the model structure, uncertainties in initial parameter guesses, measurement uncertainties, and experimental settings. Difficulties associated with poor numerical conditioning are avoided by only estimating those parameters that are most estimable. The remaining parameters are left at their initial values or can be removed from the model via simplification. In this paper, a mean squared error (MSE)-based model-selection criterion is used to determine the optimal number of parameters to estimate from the ranked parameter list, so that the most reliable model predictions can be obtained. This methodology is illustrated using a dynamic chemical reactor model.

Operator-based non-linear vibration control system design of a flexible arm with piezoelectric actuator

Abstract: Most real plants must deal with some non-linear characteristics. For the non-linear plants, design of non-linear control system using operator-based robust right coprime factorisation is considered. The considered non-linear control system applications show the desired results. In this paper, operator-based non-linear vibration control of a flexible arm experimental system with uncertainties using robust right coprime factorisation is considered. In general, the vibrations by the characteristic vibration mode include from one to infinite order vibration modes and appear on the arm vibration. It is difficult to consider all vibration modes. Consequently, based on the concept of Lipschitz operators and robust right coprime factorisation condition, robust stability of the closed-loop system with unconsidered vibration modes as uncertainties is guaranteed, and desired vibration control performance actuated by piezoelectric element is ensured. Finally, simulation and experimental results are presented to support the effectiveness of the proposed design.

Review of modelling and remote control for excavators

Abstract: An excavator is a typical hydraulic heavy-duty human-operated machine used in general versatile construction operations, such as digging, ground levelling, carrying loads, dumping loads and straight traction. However, there are many tasks, such as hazard environment (nuclear decomposition, earthquake, etc.) which is not suitable for human to work on site. The remotely controllable excavators are required to work in such environment. In this paper, we report the current progress of the ongoing project. We investigate modelling and remote control issues of industry excavators. After reviewing the literature on the related work, architecture for remotely controllable excavators is proposed. The architecture covers actuators, modelling, sensors, image signal processing, communication networks, controllers, task and path planning, human computer interaction, optimal design, co-simulation and virtual training environment. The details of modelling, communication and control of a remotely controllable excavator are provided.

Revision on fuzzy artificial potential field for humanoid robot path planning in unknown environment

Abstract: Path planning in a completely known environment has been experienced various ways. However, in real world, most humanoid robots work in unknown environments. Robots' path planning by artificial potential field and fuzzy artificial potential field methods are very popular in the field of robotics navigation. However, by default humanoid robots lack range sensors; thus, traditional artificial potential field approaches needs to adopt themselves to these limitations. This paper investigates two different approaches for path planning of a humanoid robot in an unknown environment using fuzzy artificial potential (FAP) method. In the first approach, the direction of the moving robot is derived from fuzzified artificial potential field whereas in the second one, the direction of the robot is extracted from some linguistic rules that are inspired from artificial potential field. These two introduced trajectory design approaches are validated though some software and hardware in the loop simulations and the experimental results demonstrate the superiority of the proposed approaches in humanoid robot real-time trajectory planning problems.