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Precise modeling of differential drive robots is crucial for effective control and trajectory planning in autonomous systems. A comparative analysis of two modeling approaches for a four-wheel differential drive robot is presented in this paper. The first approach, named Motor-Based Model (MBM), identifies four transfer functions, one for each motor, while the second approach, named Simplified Model (SM), uses only two transfer functions, one for linear velocity and another for angular velocity. Both models were validated by comparing their predicted trajectories against real odometry data obtained from a SLAM system implemented on a differential-drive robot. This provided a practical assessment of each model’s accuracy and underscored the importance of model selection in control design and navigation tasks. The results showed that the Motor-Based Model (MBM) consistently outperformed the Simplified Model (SM) in terms of odometry accuracy, both in position and orientation. Across all trajectories, the average RMSE for position using MBM was 0.309 m, while the SM recorded a higher average RMSE of 0.414 m. Similarly, the maximum position error averaged 0.522 m for MBM and 0.710 m for SM, confirming that MBM is more accurate and consistent in position tracking. Regarding the results of orientation estimation, when averaged across all experiments, the MBM maintained a lower angular RMSE of 0.170 rad in contrast to SM, which achieves an RMSE of 0.239 rad. The maximum angular error was also higher for the MBM at 0.316 rad, compared to 0.447 rad for the SM. Moreover, the computational performance evaluation indicated that the SM consistently outperformed MBM, achieving a 30% reduction in simulation time and substantially lower memory usage. These results demonstrate the relationship between model complexity and accuracy and suggest that the motor-specific model is more appropriate for applications requiring precise mapping or localization, such as SLAM, while the simplified model may be suitable for simpler use cases with lower computational requirements, such as embedded systems with limited resources. This paper provides a practical evaluation of the accuracy and computational performance of two modeling approaches, highlighting the implications of model selection for the design of navigation tasks.

This thesis presents a control system for a high-speed solid-rotor synchronous reluctance flywheel motor/generator. The objective of this research is to derive a model for a solid-rotor synchronous reluctance machine and provide a control scheme based on the model which has stable performance at high speed. The control system should be robust with respect to parameter deviation caused by phenomena such as nonlinear magnetics, rotor temperature variation, and inaccurate measurement. This project also includes the development of an LC filter design to improve the thermal performance of the system. A dynamic model for a synchronous reluctance machine with a conducting rotor has been developed and an open-loop current regulator for high-speed operation has been designed based upon this model. The machine dynamic model is similar to an induction machine model, yet includes a magnetic saliency of the rotor. The model is then used to calculate command voltages for a desired current in an open-loop current regulator. Techniques for parameter extraction and discrete-time models for digital implementation are presented. Experimental results consisting of a 120kW discharge of a flywheel energy storage system validates the performance of the controller. The feedforward controller includes machine parameters, and the performance inherently relies on the correctness of these values. However, inductance and resistance parameters will vary due to flux saturation and temperature, respectively. Although a feedforward control scheme is simple, fast and effective, a direct influence which deteriorates control performance can be seen on the controller’s output if the parameters are varied. Hence, a feedback compensation method has been investigated to handle the possible deviation of the parameters, and to improve the feedforward controller’s robustness. A systematic approach to designing a feedback compensator for the feedforward controller is presented. Also, a stability analysis for the feedback-compensated system has been performed. Improved current tracking performance can be seen in the experimental results. The flux-linkage/current relationships of the machine are one of the major nonlinearities to be handled in a machine controller. A more precise modeling of nonlinear magnetics becomes essential for control purposes and for understanding the limitations imposed by them. The feedforward controller can handle more diverse operating conditions by incorporating a better model of the machine dynamics. Therefore, a more accurate model to represent the nonlinear magnetics for the feedforward controller has been developed. The performance improvement by the modified model has been shown through the experimental results. Although synchronous reluctance machines with solid rotor construction have advantages in certain high-speed applications such as flywheel energy storage systems, the solid rotor allows the flow of eddy currents, which results in heat generation. A three-phase LC filter can reduce rotor losses due to the switching harmonics. The design and control of a high-speed synchronous reluctance drive with a three-phase LC filter has been investigated. A two-phase dynamic model of the drive which incorporates the LC filter dynamics is presented. The model is used to predict rotor losses due to switching harmonics generated by the three-phase inverter using phasor analysis. A feedforward current regulator is utilized, which is modified to include the effects of the LC filter. Experimental results validate the proposed approach.