Nicholas Ang's repositories
ESPThesis
A Latex template for writing ESP Honours Thesis
hello-world-bash
An example project for doing hello world
deepchem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
ANN-based-Controllers-for-CSTH-Process
Two linear Artificial Neural Network (ANN)-based controllers for the control of a nonlinear system are presented. The optimization and pole placement control design strategies are used. For both strategies, linear ANNs are used to model a Continuous Stirred Tank Heater (CSTH) process. Third-order discrete-time models or Adaline ANNs are used whose parameters are updated at every sampling time to provide adaptability to the controllers. Simulation tests show that the linear ANN-based controllers can overcome strong nonlinear coupling effects, reject step disturbances, and provide adequate damping to the subsystem interactions. Both ANN-based control design strategies are shown to be interchangeable with each other for the control of the CSTH process.
Two-Input-Two-Output-Control-Scheme
An effective and computationally less intensive method for the stabilization of a Two-Input Two-Output, TITO, system is presented. The Input-Output (IO) models of the TITO under study are each described by a discrete-time model with its parameters estimated once every sample time. The discrete-time models are used in conjunction with two proposed performance indices to yield control signals to improve the overall system performance. The ability of the proposed self-tuning control scheme to reject small and large disturbances, and to overcome strong couplings in Multi-Input Multi-Output, MIMO, systems are investigated and presented in this study. Simulation tests show that the proposed enhanced self-tuning control scheme is suitable for Single-Input Single-Output (SISO) and MIMO systems.