MSC Projects 2014-15
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Control-Based Projects
[HKL01] Fuzzy-Model-Based Control of Virtual Ball-and-Plate System
The aim of this project is to control the ball-and-plate system using fuzzy-model-based control method. A fuzzy model will be developed to facilitate the design of the fuzzy controller. Stability analysis will then be carried out. By solving numerically the solution to the stability conditions, a fuzzy controller can be obtained. To verify the analysis result, the control system will be implemented using a Lego Mindstorm NXT kit and a smart phone/tablet. The Lego Mindstorm NXT kit will be used to implement the control platform. The smart phone/tablet will be used to implement the virtual ball-and-pate system, which is mounted on the Lego Mindstorm platform. The smart phone/tablet will show a ball on the screen. The information of the ball, e.g., position, velocity, etc, will be fed back to the Lego Mindstorm platform. By adjusting the angle of the platform (which controls the angle of the smart phone/tablet), the ball position is driven to the origin. Student taking this project is required to be hardworking and self-motivated. Good Matlab and smart phone programming skills are essential to this project.
Another option: Instead of virtual ball-and-pate system, a physical system can be implemented using Lego Mindstorm NXT kit or Arduino microcontroller. An image processing technique will be developed to locate the ball position in the beam or an inferred sensor is used instead. Various controllers will be employed to stabilize the ball position or drive the ball to keep track of a pre-defined trajectory. Good programming and hardware-building skills are essential to the project.
[HKL02] Fuzzy Control of Virtual Inverted Pendulum System
Please refer to the description of [HKL01]. Instead of ball-and-plate system, an inverted pendulum system or a system of your choice is considered.
[HKL03] Fuzzy Control of Vehicle Active Suspension Systems
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.
[HKL04] Control of Electro-hydraulic Systems using Intelligent Control
Electro-hydraulic systems have demonstrated a wide range of applications including robot manipulators and anti-lock bracking systems. As the presences of uncertainties and nonlinearities, it makes the analysis and design difficult. The aim of this project is to design an intelligent fuzzy controller for the control of electro-hydraulic systems.by perfoming the following tasks: 1) The dynamic model of the electro-hydraulic systems 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) Stability analysis will be conducted to come up with stability conditions for control synthesis. 3) A Matlab software with Graphic User Interface (GUI) will be implemented to facilitate computer simulations and control synthesis with animated output. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are essential to this project.
[HKL05] Controlling Chaos in a Memristor Based Circuit using Intelligent Control
Chaos is a random-like but deterministic signal, which demonstrates an application on signal encription and decryption for communication systems. Memristor is the missing fourth element relating electric charge and magnetic flux linkage. This project aims to study the chaotic beheaviour of a memristor based circuit. Intelligent control techniques are emplyed to control the chaotic of the circuit. A fuzzy model will first be constructed to describe the dynsmics of the circuit. Stability analysis will be conducted to obtain stability conditions for control synthesis. A Graphic User Interface (GUI) will be implemented for demonstration purposes. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill is essential to this project.
Computational Intelligence-Based Projects
[HKL06] Solving Shop Scheduleing Problems using Evolutionary Algorithms
The aim of this project is to solve shop scheulding problem which is a class of multi-stage schedulding problems. More details can be found in wikipedia. Shop scheulding demonstrates a wide range of applications such as manufacturing and production process, for example, which requires to determine the best flow of different components (raw materials, unfinished products, tools) to complete a task. Evolutionary algorithms are investigated, improved and applied to solve various shop scheduling problems. Comparisons will be made between various evolutionary algorithms. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill and mathematical background are preferred.
[HKL07] Classification of Epilepsy using Computational Intelligence Techniques
This project aims at classifying the epilepsy status of patients. Real clinical data will be used for this project. Given EEG epochs obtained from patients, classifiers are designed to recognise three status, namely seizure-free, pre-seizure and seizure. Features will be extracted from the raw data which will be used as the input of classifiers. Different computational intelligence techniques including neural networks, support vector machines, fuzzy logic systems, self-organisation map and traditional classifiers including k-NN and naive Bayes will be employed for classifying the epilepsy status and compare their performance. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill is essential to this project.
[HKL08] Classification of EMG Signals using Neural Networks
This project aims at classification of EMG signals using neural networks. EMG data of different movement actions will be collected by the student taking this project. Various feature points will be extracted for the purposes of classification. Various neural-network-based classifiers will be proposed and implemented using Matlab. Recognition performance will be investigated and compared with existing methods. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill is essential to this project.
[HKL09] Voice Command Recognition
This project will implement a voice command recogniser for a robot. Speech recognition will be done by the following steps: 1) Voice data will be collected as input commands. 2) Feature extraction will be done for all collected speech data. 3) Neural networks or support vector machines will be employed to implement the recogniser. The voice commanded recognizer will be implemented using Matlab. Recognition performance will be investigated and compared with existing methods. It will then implement the voice commanded recogniser into a robot for demonstration purposes. Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill is essential to this project.
[HKL10] Recognition of Handwritten Characters using Neural Networks
The aim of this project is to recognise handwritten characters using neural-network-based techniques. A number of neural networks will be investigated and employed to implement the recognisers. An algorithm for pattern recognition will be developed to capture the feature points from the images of the characters. Various recognisers constructed by neural networks will be proposed to perform recognition using the captured feature points. Learning and optimisation algorithms will be employed to train the neural networks (or support vector machines). The proposed approaches will be compared with the traditional ones in terms of recognition accuracy. Matlab will be used to implement the proposed algorithms and a graphic user interface (GUI). Student taking this project is required to be hardworking and self-motivated. Good Matlab programming skill is essential to this project.