MSC Projects 2017-18 (2)

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Keywords: Computer Programming, Fuzzy Control, Control Theory, Linear/Nonlinear Control, Computational Intelligence, Convolution Neural Network, Fuzzy Logic, Neural Network, Machine Learning, Pattern Recognition, Population-Based Search Algorithms (Genetic Algorithm, Particle Swarm Optimisation, Q-learning, Support Vector Machine, etc.)

Requirements: Hardworking, self-motivated, creative, good programming skills, willing to learn.

Control Type Projects

[HKL01] Position and Tracking Control of Wheeled Mobile Robots using Fuzzy Logic Controller

References: Matlab Fuzzy Logic Toolbox, A Short Fuzzy Logic Tutorial, Type-2 Fuzzy Stes and Systems, General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial, Tutorial on Type-2 Fuzzy Sets and Systems, PID Controller, Fuzzy PID Controller, Linear State-feedback Controller

Open Source toobox: An Open Source Matlab/Simulink Toolbox for Interval Type-2 Fuzzy Logic Systems, toolbox downloaded here

The aim of this project is to design a fuzzy controller for wheeled mobile robot for position and tracking control. For position control, the wheeled mobile robot is driven to reach a target position. For tracking control, the wheeled mobile robot is driven to move along a pre-defined trajectory. This project can be broken down into the following tasks. 1) The dynamic model of the wheeled mobile robot is developed. Based on the model, various types of controllers, such as fuzzy controller, PID controller, linear state-feedback controller, will be employed to perform the control. 2) The system performance will be investigated and optimised by genetic algorithm (GA) or particle swarm optimization (PSO). 3) A path planning algorithm will be developed to find the shortest path to reach the target with the ability of obstacle avoidance. 4) A Matlab software with Graphic User Interface (GUI) will be implemented to facilitate computer simulations and control synthesis with animated output. 5) The system will be implemented physically using a Lego Mindstorm NXT kit. Hardware results will be recorded and compared. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are essential to this project.

[HKL02] Fuzzy Control of Vehicle Active Suspension Systems

References: Matlab LMI toolbox, Active Suspension, Automotive Suspension Systems, Automotive Suspension Systems, Modelling simulation and control of an active suspension systemodelling simulation and control of an active suspension system

The aim of the project is to design a fuzzy controller to control the vehicle active suspension system, subject to the performance constraints and user preferences. This project can be broken down into the following tasks. 1) Fuzzy model will be constructed describing the system dynamics for analysis and design. 2) Stability analysis and control synthesis will be investigated based on the Lyapunov stability theory. A set of linear matrix inequalities (LMIs) will be derived to determine the system stability and feedback gains of the fuzzy controller. 3) Computer simulations will be done to verify the analysis result and control performance will be compared with other control schemes. 4) A Graphic User Interface (GUI) will be implemented based on Matlab which involves an animated inverted pendulum. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are essential to this project.

[HKL03] Regulation of DC-DC Power Converters using Neural and Neural-Fuzzy Networks

References: Matlab Neural Network Document, Matlab Neuro-Fuzzy Network, A Short Fuzzy Logic Tutorial, Type-2 Fuzzy Stes and Systems, General Type-2 Fuzzy Logic Systems Made Simple: A Tutorial, Tutorial on Type-2 Fuzzy Sets and Systems, PID Controller, Fuzzy PID Controller, Linear State-feedback Controller, Neuro-Fuzzy Network, Neuro-Fuzzy Systems, Neuro-Fuzzy Systems: A Survey, DC-DC Converters, DC-DC Converter Tutorial, DC-DC Converter, Basic DC-DC Converter Theory

This project is to design a control strategy for the regulation of DC-DC power converters, which can maintain the output voltage to constant subject to input disturbance and/or load variation. Matlab (or Pspice) are used to implement the converter's models (for example, buck, boost, buck-boost DC-DC converters) and controllers (for example, neural-network-based, neuro-fuzzy, fuzzy logic, interval-type-2 fuzzy logic, PID, fuzzy PID, state-feedback controllers). Simulations are to be done for verification of the design. Graphic User Interface (GUI) is to be built for visualisation of the design process and simulations.

Below are some online tutorials for buck, boost and buck-boost converters.

Computational Intelligence Type Projects

[HKL04] Object Recognition using Convolution Neural Networks

References: What is deep learning?, Object recognition example, Convolution Neural Networks, Deep Learning Basics and Examples, CNN1, CNN2

This project is to build a classifier/recognizer using machine learbing techniques such as convolution neural networks and training algorthms. The classifier/recognizer can be applied to some real-life applications, say, recognition of objects from static images or videos. This project involves the design of classifiers and their implementation. A graphical user interface is to be built for the demonstration of the results. Comparison will be done to demonstrate that the proposed clssifiers are more efficient than some existing ones.

Below are some examples:

[HKL05] Electronic Arts

This project uses computational intelligence and machine learning techniques to teach a machine which can create arts, say, evolve images, mimic drawings, etc.

References: Automatic and Interactive Evolution of Vector Graphics Images with Genetic Algorithms, Evolving Line Drawings.

Some examples from youtube are shown below:

[HKL06] Auto-Driving using Computational Intelligence Techniques

This project is to create artificial creatures living in a simulated environment. Artificial creatures will evolve to adopt the environment for survival using computational intelligence techniques, for example, genetic algorithm/particle swarm optimisation, fuzzy logic and neural networks. Variable computational techniques will be employed, modified and tested under different scenarios for comparison of performance.

Some examples from youtube of artifical life are shown below: