Kok-Zuea Tang

Adaptive online correction and interpolation of quadrature encoder signals using radial basis functions

This paper considers the development of an adaptive online approach for the correction and interpolation of quadrature encoder signals, suitable for application to precision motion control systems. It is based on the use of a two-stage double-layered radial basis function (RBF) neural network. The first RBF stage is used to adaptively correct for the imperfections in the encoder signals such as mean, phase offsets, amplitude deviation and waveform distortion. The second RBF stage serves as the inferencing machine to adaptively map the quadrature encoder signals to higher order sinusoids, thus, enabling intermediate positions to be derived. Experimental and simulation results are provided to verify the effectiveness of the RBF approach.

Vehicle dispatching system based on Taguchi-tuned fuzzy rules

Abstract This paper presents the development of a dispatching system for a fleet of automated guided vehicles in a flexible manufacturing environment which is based on a hybrid Fuzzy–Taguchi approach. A fuzzy decision-making system emulates the human behavior necessary for multi-objective directed decision making in a dynamically evolving environment. A statistical approach based on the Taguchi method tunes the fuzzy rules to achieve near optimal performance. Simulation results demonstrate the effectiveness of this marriage of computational tools in dealing with the well-known NP-complete scheduling problem.

Development of an integrated and open-architecture precision motion control system

Abstract In this paper, the development of an integrated and open-architecture precision motion control system is presented. The control system is generally applicable, but it is developed with a particular focus on direct drive servo systems based on linear motors. The overall control system is comprehensive, comprising of various selected control and instrumentation components, integrated within a configuration of hardware architecture centred around a dSPACE DS1004 DSP processor board. These components include a precision composite controller (comprising of feedforward and feedback control), a disturbance observer, an adaptive notch filter, and a geometrical error compensator. The hardware architecture, software development platform, user interface, and all constituent control components will be elaborated on in the paper.

Evolutionary tuning of a fuzzy dispatching system for automated guided vehicles

This paper develops a novel genetic algorithm (GA) based methodology for optimal tuning of a reported fuzzy dispatching system for a fleet of automated guided vehicles in a flexible manufacturing environment. The reported dispatching rules are transformed into a continuously adaptive procedure to capitalize the on-line information available from a shop floor at all times. Simulation results obtained show that the GA is very powerful and effective to achieve optimal fuzzy dispatching rules for higher shop floor productivity and operational efficiency.

Simulation of an evolutionary tuned fuzzy dispatching system for automated guided vehicles

This paper presents the development and simulation of a novel genetic algorithm (GA) based methodology applied to optimal tuning of a fuzzy dispatching system for a fleet of automated guided vehicles in a flexible manufacturing environment. The dispatching rules are further transformed into a continuously adaptive procedure to capitalize the online information available from a shop floor at all times. The entire problem is simulated using MATLAB/SIMULINK. The simulation results obtained show that GA is an efficient and effective tool to achieve optimal performance for the well-known NP-complete scheduling problem.

Application of vibration sensing in monitoring and control of machine health

In this paper, an application for monitoring and control of machine health using vibration sensing is developed. This vibration analyzer is able to continuously monitor and compare the actual vibration pattern against a vibration signature, based on a fuzzy fusion technique. More importantly, this intelligent knowledge-based real-time analyzer is able to detect excessive vibration conditions much sooner than a resulting fault could be detected by an operator. Subsequently, appropriate actions can be taken, say to provide a warning or automatic corrective action. This approach may be implemented independently of the control system and as such can be applied to existing equipment without modification of the normal mode of operation. Simulation and experimental results are provided to illustrate the advantages of the approach taken in this application.

Ex Utero Harvest of Hematopoietic Stem Cells From Placenta/Umbilical Cord With an Automated Collection System

Hematopoietic stem cells (HSCs) from the human placenta and umbilical cord blood (UCB) provide a rich source of highly proliferative cells for many clinical uses with advantages over traditional sources like the bone marrow and periphery blood. However, the key current constraint with this source of HSCs is the inadequate number of HSCs cells that can be harvested in a single collection using current approaches, which render a large number of collections unusable on their own, even for pediatric patients. This paper will present the development of a device to enable more efficient harvesting of HSCs from placentas, which can be used ex utero, upon the discharge of placentas after deliveries. The device can be used to facilitate a two-fraction collection process. Results, in terms of mononucleated cells (MNCs) count, CD34+ cells count, as well as flow cytometry, will be furnished to verify the effectiveness of the developed system.

Neural network-based correction and interpolation of encoder signals for precision motion control

Precision control is the core of many applications in the industry, particularly robotics and drive control. To achieve it, precise measurement of the signals generated by incremental encoder sensors is essential. High precision and resolution motion control relies critically on the precision and resolution achievable from the encoders. In this paper, a dynamic neural network-based approach for the correction and interpolation of quadrature encoder signals is developed. In this work, the radial basis functions (RBF) neural network is employed to carry out concurrently the correction and interpolation of encoder signals in realtime. The effectiveness of the proposed approach is verified in the simulation results provided.

Development of an automated umbilical cord blood collection system

In this paper, the development of an automated umbilical cord blood (UCB) collection system is presented. The overall control system is comprehensive, comprising of various selected control and instrumentation components, integrated within a configuration of hardware architecture centered around a dSPACE DS1002 Digital Signal Processing (DSP) board. The hardware architecture, software development platform, user interface, and all constituent control components will be elaborated on in the paper.

Flexible Dispatching Rules for Automated Guided Vehicles Based on a Self-Adapting Fuzzy Prioritizing System

ABSTRACTIn this paper, we propose a flexible dispatching system for unit load automated guided vehicles in a flexible manufacturing environment, using a self-adapting fuzzy logic-driven approach. Unlike conventional heuristical approaches, we are able to satisfy a multi-criterion formulation of factors affecting the dispatch decision. The system also has a self-adaptation feature in that respective influence of the elementary rules may be continuously adapted to suit the manufacturing environment. Simulation results are provided to compare the effectiveness of the approach against current methodologies.