Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results

This paper focuses on current control in a permanentmagnet synchronous motor (PMSM). The paper has two main objectives: The first objective is to develop a neural-network (NN) vector controller to overcome the decoupling inaccuracy problem associated with conventional PI-based vector-control methods. The NN is developed using the full dynamic equation of a PMSM, and trained to implement optimal control based on approximate dynamic programming. The second objective is to evaluate the robust and adaptive performance of the NN controller against that of the conventional standard vector controller under motor parameter variation and dynamic control conditions by (a) simulating the behavior of a PMSM typically used in realistic electric vehicle applications and (b) building an experimental system for hardware validation as well as combined hardware and simulation evaluation. The results demonstrate that the NN controller outperforms conventional vector controllers in both simulation and hardware implementation

Intelligent Learning Control System Design Based on Adaptive Dynamic Programming

Adaptive dynamic programming (ADP) controller is a powerful neural network based control technique that has been investigated, designed, and tested in a wide range of applications for solving optimal control problems in complex systems. The performance of ADP controller is usually obtained by long training periods because the data usage efficiency is low as it discards the samples once used. Experience replay is a powerful technique showing potential to accelerate the training process of learning and control. However, its existing design can not be directly used for model-free ADP design, because it focuses on the forward temporal difference (TD) information (e.g., state-action pair) between the current time step and the future time step, and will need a model network for future information prediction. Uniform random sampling again used for experience replay, is not an efficient technique to learn. Prioritized experience replay (PER) presents important transitions more frequently and has proven to be efficient in the learning process. In order to solve long training periods of ADP controller, the first goal of this thesis is to avoid the usage of model network or identifier of the system. Specifically, the experience tuple is designed with one step backward state-action information and the TD can be achieved by a previous time step and a current time step. The proposed approach is tested for two case studies: cart-pole and triple-link pendulum balancing tasks. The proposed approach improved the required average trial to succeed by 26.5% for cart-pole and 43% for triple-link. The second goal of this thesis is to integrate the efficient learning capability of PER into ADP. The detailed theoretical analysis is presented in order to verify the stability of the proposed control technique. The proposed approach improved the required average trial to succeed compared to traditional ADP controller by 60.56% for cart-pole and 56.89% for triple-link balancing tasks. The final goal of this thesis is to validate ADP controller in smart grid to improve current control performance of virtual synchronous machine (VSM) at sudden load changes and a single line to ground fault and reduce harmonics in shunt active filters (SAF) during different loading conditions. The ADP controller produced the fastest response time, low overshoot and in general, the best performance in comparison to the traditional current controller. In SAF, ADP controller reduced total harmonic distortion (THD) of the source current by an average of 18.41% compared to a traditional current controller alone

Machine learning based modelling and control of wind turbine structures and wind farm wakes

With the fast development of wind energy, new technological challenges emerge, which calls for new research efforts to further reduce the cost of wind power. A lot of efforts have been spent to tackle the modelling and control of wind turbines and wind farms. However, big research gaps still exist due to the complexity and strong nonlinearity of the underlying structural and fluid systems. On the other hand, machine learning (ML), which is very powerful in handling complex and nonlinear systems, is developing very fast in the past years. Therefore, this thesis aims to tackle the modelling and control issues arising from the fast-developing wind industry, based on both traditional methods (including structural mechanics, control engineering, fluid dynamics, and scientific computing) and ML (including reinforcement learning, supervised ML, dimensionality reduction, generative adversarial network, and physics-informed deep learning). First, at the turbine level, mitigation of dynamic response of a floating wind turbine using active tuned mass dampers is investigated, where a reinforcement learning algorithm is employed and a neural network structure is designed to realize the employed algorithm. Second, at the farm level, novel static and dynamic wind farm wake models are developed by proposing novel ML-based surrogate modelling methods for distributed fluid systems and then training the model based on highfidelity CFD database generated by large eddy simulations. Third, the prediction of the spatiotemporal wind field in the whole domain in front of a wind turbine is investigated by combining data (i.e. LIDAR measurements at sparse locations) and physics (i.e. Navier-Stokes equations) in a unified manner via physics-informed deep learning. The results presented in this thesis fully demonstrate the great performance of the proposed structural controllers, the great accuracy, efficiency & robustness of the developed wind farm models, and the great accuracy of the full spatiotemporal wind field predictions. xi