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

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components. Shunt active filters (SAF) with current controlled voltage source inverters (CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents. However, CCVSI with traditional controllers have a limited transient and steady state performance. In this paper, we propose an adaptive dynamic programming (ADP) controller with online learning capability to improve transient response and harmonics. The proposed controller works alongside existing proportional integral (PI) controllers to efficiently track the reference currents in the d - q domain. It can generate adaptive control actions to compensate the PI controller. The proposed system was simulated under different nonlinear (three-phase full wave rectifier) load conditions. The performance of the proposed approach was compared with the traditional approach. We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison. The online learning based ADP controller not only reduced average total harmonic distortion by 18.41 %, but also outperformed traditional PI controllers during transients

Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications

The rapid transition towards an inverter-dominated power system has reduced the inertial response capability of modern power systems. As a solution, inverters are equipped with control strategies, which can emulate inertia by exchanging power with the grid based on frequency changes. This paper discusses the various current control techniques for application in these systems, known as virtual inertia systems. Some classic control techniques like the proportional-integral, the proportional-resonant, and the hysteresis control are presented first, followed by the design and discussion of two more advanced control techniques based on model prediction and machine learning, respectively. MATLAB/Simulink-based simulations are performed, and results are presented to compare these control techniques in terms of harmonic performance, switching frequency, and transient response

Fitted polynomial growth curve based on 3%, 10%, 21% growth rates regressed on minimum population size (M_t+1) of camera trap tiger population surveyed in between 2009 and 2014 in core area of Suklaphanta National Park.

<p>Fitted polynomial growth curve based on 3%, 10%, 21% growth rates regressed on minimum population size (M_t+1) of camera trap tiger population surveyed in between 2009 and 2014 in core area of Suklaphanta National Park.</p

Tigers in the Terai: Strong evidence for meta-population dynamics contributing to tiger recovery and conservation in the Terai Arc Landscape - Fig 2

<p>Minimum Convex Polygon (MCP) from multiyear camera trap surveys carried out in a) core area of Suklaphanta National Park; b) Babai valley within Bardia National Park.</p

Trend in minimum population size (M_t+1) of the tiger population based on adhoc camera trap tiger population surveyed in between 2006 and 2016 in the core area of Babai Valley within Bardia National Park.

<p>Trend in minimum population size (M_t+1) of the tiger population based on adhoc camera trap tiger population surveyed in between 2006 and 2016 in the core area of Babai Valley within Bardia National Park.</p

A male tiger captured along the Bardia-Katerniaghat forest matrix connected through Khata corridor forest in the western part of Terai Arc Landscape.

<p>A male tiger captured along the Bardia-Katerniaghat forest matrix connected through Khata corridor forest in the western part of Terai Arc Landscape.</p