Bond graph modeling of centrifugal compression systems

A novel approach to model unsteady fluid dynamics in a compressor network by using a bond graph is presented. The model is intended in particular for compressor control system development. First, we develop a bond graph model of a single compression system. Bond graph modeling offers a different perspective to previous work by modeling the compression system based on energy flow instead of fluid dynamics. Analyzing the bond graph model explains the energy flow during compressor surge. Two principal solutions for compressor surge problem are identified: upstream energy injection and downstream energy dissipation. Both principal solutions are verified in bond graph modelings of single compression system equipped with a surge avoidance system (SAS) and single compression system equipped with an active control system. Moreover, the bond graph model of single compressor equipped with SAS is able to show the effect of recycling flow to the compressor upstream states which improves the current available model. The bond graph model of a single compression system is then used as the base model and combined to build compressor network models. Two compressor networks are modeled: serial compressors and parallel compressors. Simulation results show the surge conditions in both compressor networks.© SAGE. This is the authors’ accepted and refereed manuscript to the article

On Model Predictive Path Following and Trajectory Tracking for Industrial Robots

In this article we show how the model predictive path following controller allows robotic manipulators to stop at obstructions in a way that model predictive trajectory tracking controllers cannot. We present both controllers as applied to robotic manipulators, simulations for a two-link manipulator using an interior point solver, consider discretization of the optimal control problem using collocation or Runge-Kutta, and discuss the real-time viability of our implementation of the model predictive path following controller.Comment: Draft of article for CASE 201

Stable and robust neural network controllers

Neural networks are expressive function approimators that can be employed for state estimation in control problems. However, control systems with machine learning in the loop often lack stability proofs and performance guarantees, which are crucial for safety-critical applications. In this work, a feedback controller using a feedforward neural network of arbitrary size to estimate unknown dynamics is suggested. The controller is designed for solving a general trajectory tracking problem for a broad class of two-dimensional nonlinear systems. The controller is proven to stabilize the closed-loop system, such that it is input-to-state and finite-gain Lp-stable from the neural network estimation error to the tracking error. Furthermore, the controller is proven to make the tracking error globally and exponentially converge to a ball centered at the origin. When the neural network estimate is updated discretely, or the state measurements are affected by bounded noise, the convergence bound is shown to be dependent on the Lipschitz constant of the neural network estimator. In light of this, we demonstrate how regularization techniques can be beneficial when utilizing deep learning in control. Experiments on simulated data confirm the theoretical results.acceptedVersio

A novel corrective-source term approach to modeling unknown physics in aluminum extraction process

With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However, despite the flexibility and surprising accuracy of such black-box models, it remains difficult to trust them. Recent efforts to combine the two approaches aim to develop flexible models that nonetheless generalize well; a paradigm we call Hybrid Analysis and modeling (HAM). In this work we investigate the Corrective Source Term Approach (CoSTA), which uses a data-driven model to correct a misspecified physics-based model. This enables us to develop models that make accurate predictions even when the underlying physics of the problem is not well understood. We apply CoSTA to model the Hall-H\'eroult process in an aluminum electrolysis cell. We demonstrate that the method improves both accuracy and predictive stability, yielding an overall more trustworthy model

Formation Control of Underactuated Bio-inspired Snake Robots

This paper considers formation control of snake robots. In particular, based on a simplified locomotion model, and using the method of virtual holonomic constraints, we control the body shape of the robot to a desired gait pattern defined by some pre-specified constraint functions. These functions are dynamic in that they depend on the state variables of two compensators which are used to control the orientation and planar position of the robot, making this a dynamic maneuvering control strategy. Furthermore, using a formation control strategy we make the multi-agent system converge to and keep a desired geometric formation, and enforce the formation follow a desired straight line path with a given speed profile. Specifically, we use the proposed maneuvering controller to solve the formation control problem for a group of snake robots by synchronizing the commanded velocities of the robots. Simulation results are presented which illustrate the successful performance of the theoretical approach.© ISAROB 2016. This is the authors' accepted and refereed manuscript to the article. Locked until 2017-07-27

Real-time temporal adaptation of dynamic movement primitives for moving targets

This work is aimed at extending the standard dynamic movement primitives (DMP) framework to adapt to real-time changes in the task execution time while preserving its style characteristics. We propose an alternative polynomial canonical system and an adaptive law allowing a higher degree of control over the execution time. The extended framework has a potential application in robotic manipulation tasks that involve moving objects demanding real-time control over the task execution time. The existing methods require a computationally expensive forward simulation of DMP at every time step which makes it undesirable for integration in realtime control systems. To address this deficiency, the behaviour of the canonical system has been adapted according to the changes in the desired execution time of the task performed. An alternative polynomial canonical system is proposed to provide increased real-time control on the temporal scaling of DMP system compared to the standard exponential canonical system. The developed method was evaluated on scenarios of tracking a moving target where the desired tracking time is varied in real-time. The results presented show that the extended version of DMP provide better control over the temporal scaling during the execution of the task. We have evaluated our approach on a UR5 robotic manipulator for tracking a moving object.acceptedVersio

Deep Model Predictive Variable Impedance Control

The capability to adapt compliance by varying muscle stiffness is crucial for dexterous manipulation skills in humans. Incorporating compliance in robot motor control is crucial to performing real-world force interaction tasks with human-level dexterity. This work presents a Deep Model Predictive Variable Impedance Controller for compliant robotic manipulation which combines Variable Impedance Control with Model Predictive Control (MPC). A generalized Cartesian impedance model of a robot manipulator is learned using an exploration strategy maximizing the information gain. This model is used within an MPC framework to adapt the impedance parameters of a low-level variable impedance controller to achieve the desired compliance behavior for different manipulation tasks without any retraining or finetuning. The deep Model Predictive Variable Impedance Control approach is evaluated using a Franka Emika Panda robotic manipulator operating on different manipulation tasks in simulations and real experiments. The proposed approach was compared with model-free and model-based reinforcement approaches in variable impedance control for transferability between tasks and performance.Comment: Preprint submitted to the journal of robotics and autonomous system

Analysis of underwater snake robot locomotion based on a control-oriented model

This paper presents an analysis of planar underwater snake robot locomotion in the presence of ocean currents. The robot is assumed to be neutrally buoyant and move fully submerged with a planar sinusoidal gait and limited link angles. As a basis for the analysis, an existing, controloriented model is further simplified and extended to general sinusoidal gaits. Averaging theory is then employed to derive the averaged velocity dynamics of the underwater snake robot from that model. It is proven that the averaged velocity converges exponentially to an equilibrium, and an analytical expression for calculating the forward velocity of the robot in steady state is derived. A simulation study that validates both the proposed modelling approach and the theoretical results is presented.Prepint - (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works