Machine Learning and System Identification for Estimation in Physical Systems

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear modeling power while keeping data requirements practical.The thesis covers a wide range of applications, many inspired by applications within robotics, but also extending outside this already wide field.Usage of the proposed methods and algorithms are in many cases illustrated in the real-world applications that motivated the research.Topics covered include dynamics modeling and estimation, model-based reinforcement learning, spectral estimation, friction modeling and state estimation and calibration in robotic machining.In the work on modeling and identification of dynamics, we develop regularization strategies that allow us to incorporate prior domain knowledge into flexible, overparameterized models. We make use of classical control theory to gain insight into training and regularization while using tools from modern deep learning. A particular focus of the work is to allow use of modern methods in scenarios where gathering data is associated with a high cost.In the robotics-inspired parts of the thesis, we develop methods that are practically motivated and make sure that they are implementable also outside the research setting. We demonstrate this by performing experiments in realistic settings and providing open-source implementations of all proposed methods and algorithms

Optimization of Controller Parameters in Julia using ControlSystems.jl and Automatic Differentiation

We describe how to utilize the possibility of differentiating through arbitrary Julia codeto perform tasks such as controller optimization. The user specifies a cost function, forexample, the integrated squared error between output and reference, and constraints, suchas a maximum acceptable value of the sensitivity function. Julia performs the integrationand calculates the sensitivities of the cost and constraint functions with respect to controllerparameters automatically, using automatic differentiation. We conclude with a full exampleincluding gradient-based optimization of the cost function. All code required is open-sourceunder permissive licenses

Modeling and Identification of Position and Temperature Dependent Friction Phenomena without Temperature Sensing

This paper investigates both positional dependence in systems with friction and the influence an increase in temperature has on the friction behavior. The positional dependence is modeled with a Radial Basis Function network and the temperature dependence is modeled as a first order system with the power loss due to friction as input, eliminating the need for temperature sensing. The proposed methods are evaluated in both simulations and experiments on two industrial robots with strong positional and temperature friction dependence

Identification of LTV Dynamical Models with Smooth or Discontinuous Time Evolution by means of Convex Optimization

We establish a connection between trend filtering and system identification which results in a family of new identification methods for linear, time-varying (LTV) dynamical models based on convex optimization. We demonstrate how the design of the cost function promotes a model with either a continuous change in dynamics over time, or causes discontinuous changes in model coefficients occurring at a finite (sparse) set of time instances. We further discuss the introduction of priors on the model parameters for situations where excitation is insufficient for identification. The identification problems are cast as convex optimization problems and are applicable to, e.g., ARX models and state-space models with time-varying parameters. We illustrate usage of the methods in simulations of jump-linear systems, a nonlinear robot arm with non-smooth friction and stiff contacts as well as in model-based, trajectory centric reinforcement learning on a smooth nonlinear system

Two-Degree-of-Freedom Control for Trajectory Tracking and Perturbation Recovery during Execution of Dynamical Movement Primitives

Modeling of robot motion as dynamical movement primitives (DMPs) has becomean important framework within robot learning and control. The ability of DMPs to adapt online with respect to the surroundings, e.g., to moving targets, has been used and developed by several researchers. In this work, a method for handling perturbations during execution of DMPs on robots was developed. Two-degree-of-freedom control was introduced in the DMP context, for reference trajectory tracking and perturbation recovery. Benefits compared to the state of the art were demonstrated. The functionality of the method was verified in simulations and in real-world experiments

Robotic friction stir welding—Seam-tracking control, force control and process supervision

Purpose – This study aims to enable robotic friction stir welding (FSW) in practice. The use of robots has hitherto been limited, because of the large contact forces necessary for FSW. These forces are detrimental for the position accuracy of the robot. In this context, it is not sufficient to rely on the robot’s internal sensors for positioning. This paper describes and evaluates a new method for overcoming this issue.Design/methodology/approach – A closed-loop robot control system for seam-tracking control and force control, running and recording data in real-time operation, was developed. The complete system was experimentally verified. External position measurements were obtained from a laser seam tracker and deviations from the seam were compensated for, using feedback of the measurements to a position controller.Findings – The proposed system was shown to be working well in overcoming position error. The system is flexible and reconfigurable for batch and short production runs. The welds were free of defects and had beneficial mechanical properties.Research limitations/implications – In the experiments, the laser seam tracker was used both for control feedback and for performance evaluation. For evaluation, it would be better to use yet another external sensor for position measurements, providing ground truth.Practical implications – These results imply that robotic FSW is practically realizable, with the accuracy requirements fulfilled.Originality/value – The method proposed in this research yields very accurate seam tracking as compared to previous research. This accuracy, in turn, is crucial for the quality of the resulting material.Keywords Friction stir welding, Robotics, Force control, Seam-tracking control, Control, Sensors, Robot weldin

DifferentialDynamicProgramming.jl : A package for solving Differential Dynamic Programming and trajectory optimization problems.

Solves the same problem as iLQG, with an added constraint on the KL-divergence between the new trajectory distribution and the distribution induced by a previous controller. This feature can be used in an outer loop with repeated experiments between the iterations if the model used for optimization is uncertain