Design and Implementation
Linear Quadratic Regulator
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This project deals with the design and implementation of an adaptive robust controller, i.e. a LQR controller, suitable for the given system which is a DC servo motor. The parameters of the plant are identified using system identification techniques. System identification is an important step in the analysis and design of control systems. It involves mathematical modeling of the controlled process, experimentally, using measured data i.e. input and output. The model of the plant is assumed to be an ARX model and the parameters are estimated using Least Squares Estimation technique. The paramet...
This project deals with the design and implementation of an adaptive robust controller, i.e. a LQR controller, suitable for the given system which is a DC servo motor. The parameters of the plant are identified using system identification techniques. System identification is an important step in the analysis and design of control systems. It involves mathematical modeling of the controlled process, experimentally, using measured data i.e. input and output. The model of the plant is assumed to be an ARX model and the parameters are estimated using Least Squares Estimation technique. The parameters are then passed on to the controller design algorithm which is a discrete time LQR. LQR controller is an optimal robust controller which incorporates state feedback. The entire project has been divided into three main parts, where in the first part a model is found that describes the process. The second part describes how the controllers were developed and the final part how they were implemented in real time.The identification algorithm and the controller algorithm are developed in C code and are executed in TMS320F2812 DSP for its implementation.
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