There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

応用水文 = Applied hydrology

Artificial neural network (ANN) based model predictive control (MPC) and optimization of HVAC systems: A state of the art review and case study of a residential HVAC system

Matlab (the language of technical computing): using matlab graphics ver.5

Analytical Methods in Vibrations

This paper presents the implementation of PID controller tuning using two sets of evolutionary techniques which are differential evolution (DE) and genetic algorithm (GA). The optimal PID control parameters are applied for a high order system, system with time delay and non-minimum phase system. The performance of the two techniques is evaluated by setting its objective function with mean square error (MSE) and integral absolute error (IAE). Both techniques will compete to achieve the globally minimum value of its objective functions. Meanwhile, reliability between DE and GA in consistently maintaining minimum MSE is also been studied. This paper also compares the performance of the tuned PID controller using GA and DE methods with Ziegler-Nichols method.

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PID controller tuning using evolutionary algorithms

Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system

This paper investigates the utilisation of neural networks (NNs) and parametric linear approaches for modelling a twin rotor multi-input multi-output system (TRMS) in hovering position. A multi-layer perceptron (MLP) neuro-model is designed to characterise the TRMS. A parametric model of the system is then developed with the conventional recursive least square (RLS) technique. A comparative assessment of the two model types, in characterising the system, is carried out in the time and frequency domains. Experimental results demonstrate the superiority of the NN approach over the conventional linear modelling approach. The developed neuro-modelling approach will be used for control design and development in future work.

Dynamic modelling of a twin rotor system in hovering position

Application of adaptive neural predictive control for an automotive air conditioning system

System identification using parametric linear approaches for modelling a twin rotor multi-input multi-output system (TRMS) in hovering position is presented in this work. The utilisation of a genetic algorithm (GA) optimisation technique for dynamic modelling of a highly non-linear system is studied in comparison to the conventional recursive least squares (RLS) technique. The global search technique of GA is used to identify the parameters of the TRMS based on one-step-ahead prediction. A comparative assessment of the two models in characterising the system is carried out in the time and frequency domains. Experimental results indicate the advantages of GA over RLS in linear parametric modelling. The developed genetic-modelling approach will be used for control design and development in future work.

Intan Zaurah Mat Darus

Papers

PID controller tuning using evolutionary algorithms

Genetic algorithm-based identification of transfer function parameters for a rectangular flexible plate system

Dynamic modelling of a twin rotor system in hovering position

Application of adaptive neural predictive control for an automotive air conditioning system

Parametric modelling of a twin rotor system using genetic algorithms