Iterative Learning of Feed-Forward Corrections for High-Performance Tracking

We revisit a recently developed iterative learning algorithm that enables systems to learn from a repeated operation with the goal of achieving high tracking performance of a given trajectory. The learning scheme is based on a coarse dynamics model of the system and uses past measurements to iteratively adapt the feed-forward input signal to the system. The novelty of this work is an identification routine that uses a numerical simulation of the system dynamics to extract the required model information. This allows the learning algorithm to be applied to any dynamic system for which a dynamics simulation is available (including systems with underlying feedback loops). The proposed learning algorithm is applied to a quadrocopter system that is guided by a trajectory-following controller. With the identification routine, we are able to extend our previous learning results to three-dimensional quadrocopter motions and achieve significantly higher tracking accuracy due to the underlying feedback control, which accounts for nonrepetitive noise

An Upper Bound on the Error of Alignment-Based Transfer Learning Between Two Linear, Time-Invariant, Scalar Systems

Methods from machine learning have successfully been used to improve the performance of control systems in cases when accurate models of the system or the environment are not available. These methods require the use of data generated from physical trials. Transfer Learning (TL) allows for this data to come from a different, similar system. This paper studies a simplified TL scenario with the goal of understanding in which cases a simple, alignment-based transfer of data is possible and beneficial. Two linear, time-invariant (LTI), single-input, single-output systems are tasked to follow the same reference signal. A scalar, LTI transformation is applied to the output from a source system to align with the output from a target system. An upper bound on the 2-norm of the transformation error is derived for a large set of reference signals and is minimized with respect to the transformation scalar. Analysis shows that the minimized error bound is reduced for systems with poles that lie close to each other (that is, for systems with similar response times). This criterion is relaxed for systems with poles that have a larger negative real part (that is, for stable systems with fast response), meaning that poles can be further apart for the same minimized error bound. Additionally, numerical results show that using the reference signal as input to the transformation reduces the minimized bound further.Natural Sciences and Engineering Research Council of Canada (NSERC

Adaptive Robust Attitude Controller Design for a Quadrotor Platform

This paper includes attitude controller design ideas for a quadrotor platform which can be regarded as an exceptionally agile flying robot with highly non-linear and unstable features in flight dynamics. The quadrotor poses severe problems in characterizing the dynamics especially when performing high-speed manoeuvres. These facts cause the quadrotor not to lose its popularity as a compelling tool among avid researchers who endeavour to realize various controller ideas. The procedure in this paper is initiated with the construction of the system model and the verification of this phase relying on the characteristics of the test bed. With the aid of sensors on the off-the-shelf platform, the controllers are designed to enact tracking of the reference commands that contain the desired trajectories and attitudes. The controller methods highlighted in this research are non-linear dynamic inversion, model reference adaptive control and integral back-stepping technique. The trade-off between performance and robustness is investigated as well. The responses of the system to the impacts of the existence of uncertain parameters, unmatched uncertainties or disturbances are exceptional means to judge how robust the controller is. An overview of the cases with parametric uncertainty and the existence of noise, therefore, find its place as a section within this work. This sketch grades the controller options while putting forward the advantage of adaptation. Besides, by employing correction approaches, the advancement of the adaptive controller in terms of robustness is examined where dead zone implementation, parameter bounding, e-, and σ-modifications are exploited. The motivation behind this research is to produce persistent state controllers to lay the first stone for more complex algorithm structures such as autonomous flight phases, obstacle avoidance and way-point targeting. The future work of this study is the justification of the reliability of the methodologies used and the results attained from the simulations through experiments

Control for Societal-Scale Challenges Roadmap 2030

The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade