Gordana Collier

Teaching control using NI Starter Kit Robot

Teaching engineering concepts using demonstrations and experiments on real hardware is always engaging and well received by students. This paper provides reference materials (both theoretical and test results), to be used in Control teaching and assessment using a laboratory experiment, with a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). This robotic vehicle is programmed using a graphical programming environment. The Adaptive Cruise Control (ACC) algorithm based on Proportional-Integral (PI) and Proportional - Integral - Derivative (PID) Control are deployed on a field programmable gate array (FPGA), included in the robots architecture. The robot model (based on a given second order transfer function) is controlled using the same method. The results obtained are compared for the simulation model and a real robot. The performance comparison demonstrates a good correlation between theory and implementation, whilst demonstrating problems and discrepancies introduced by a real system.

Adaptive Cruise Control for a Robotic Vehicle Using Fuzzy Logic

This paper presents the modelling, simulation, and implementation of an ACC system for a robotic vehicle using Fuzzy Logic control strategy. Implementation has been carried out using the graphical programming environment of LabVIEW and a robotic vehicle based on a real-time single board computer. The paper demonstrates control design process including the implementation of the fuzzy logic design rules, showing a good correlation between simulation behaviour and real-life implementation. The work is a part of a larger research informed teaching project for teaching engineering at Kingston University London.

Robotic Implementation of the Adaptive Cruise Control-Comparison of Three Control Methods

This paper explores the practical implementation of the Adaptive Cruise Control (ACC) system on a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). The ACC algorithm based on three control methodologies, the fuzzy PID control, model predictive control (MPC) and conventional PID control, is deployed on a field programmable gate array (FPGA), included in the robot’s architecture. The results are compared both in the simulation and using the real robot. The comparison of the performance demonstrates a good correlation between theory and real implementation, whilst highlighting problems introduced by a real system.

Teaching Fuzzy Logic Control Based on a Robotic Implementation

Abstract Advanced control concepts present a teaching challenge - even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modeling the plant, applying control strategy and tuning. This paper provides reference materials (both theoretical and test results), to be used in control teaching and assessment using a laboratory experiment, with a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). This paper explores the practical implementation of the ACC system through use of a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). The ACC algorithm based on fuzzy PID control is deployed on a field programmable gate array (FPGA), included in the robots architecture. This robotic vehicle is programmed using a graphical programming language (LabVIEW). A Kalman filter is used to estimate the unmeasured parameters while implementing the control algorithm in the hardware (the real robot). The results obtained are compared for the simulation model and the real robot, respectively. The experiment demonstrates clear correlation between theoretical expectations and real-life system performance and at the same time offers a novel idea how to deliver this advanced control concept in an applied and visual manner.

Teaching Model Predictive Control Algorithm Using Starter Kit Robot

Abstract Advanced control concepts present a teaching challenge, where even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modelling the plant, applying control strategy and tuning. This paper describes one of a series of three experiments demonstrating the implementation of different control strategies using adaptive cruise control (ACC) on robot models and real robots. The experiment described here utilises the model predictive control (MPC) strategy implemented in ACC. The algorithm is realised using the graphical programming language (LabVIEW) as the design environment and National Instruments Robotics Starter Kit robot as the target hardware, with the code being deployed on a field programmable gate array (FPGA), included in the robot’s architecture. Two robotic vehicles, ‘the leader’ and ‘the follower’ are programmed to execute ACC: the velocity of the leader robot and the distance between the robots are augmented into the robot’s state-space equation, to design the controller (MPC), which was then tuned for both velocity and distance tracking modes. The experiment offers a novel idea on how to deliver this advanced control strategy in an applied and visual manner with laboratory experimentation supporting the theoretical aspects of learning. It brings to life some often stated theoretical qualities of an MPC controller, including quick rise time, minor fluctuation and a small distance tracking error, in line with current scientific papers. Thus, it demonstrates to students a clear correlation between theoretical expectations and real-life system performance whilst challenging their ability to work with real hardware.

Modelling of a pan and tilt servo system

Two-axis pan and tilt systems are widely used in surveillance applications for high accuracy positioning of sensor payloads such as cameras and laser pointers. In order to develop advanced control algorithms to improve the performance of these systems, a model of the system must be developed. This model should include the dynamics of the system to include effects such as compliance and account for friction effects in the drive. This paper discusses the development of the overall model of the system using National Instruments LabVIEW, and in particular, the models for friction and the drive train that will be used.

Upper Limb Joint Torque Distribution Resulting from the Flat Tennis Serve Impact Force

The serve is a vital part of a tennis player’s game. Serve has to be fast and powerful to enhance the chance of winning the initial point. The extreme intensity and severity of the ball racket collision involved at the service often lead to various upper limb injuries. Detailed biomechanical models of the human upper arm are needed to study how the impulse force propagates to individual joint or muscle level. This study investigates the torque distribution resulting from the impact force of the flat service stroke based on a detailed 9- Degrees of Freedom (DoF) kinematic model. Five trials were sampled from a professional level tennis player during the flat service scenario. A Qualisis motion capturing system with six cameras was used to capture 3 Dimensional (3D) position data of the subject, at a sampling rate of 240 Hz. The torque distribution was quantitatively analyzed using a Jacobian matrix. The results of the analysis reflected that the highest torque was apportioned to sterno-clavicular joint followed by elbow, sterno-clavicular joint and wrist joint respectively. A statistical based sensitivity analysis has been carried out on upper limb joints. The proximal radioulnar Joint was found to be the most sensitive joint with respect to torque distribution.