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

PSO and Kalman Filter-Based Node Motion Prediction for Data Collection from Ocean Wireless Sensors Network with UAV

Source at https://ctsoc.ieee.org/In this paper, we consider a wireless sensor network of nodes at the sea surface drifting due to wind and sea currents. In our scenario an Unmanned Aerial Vehicle (UAV) will be used to gather data from the sensor nodes. The goal is to find a flyable path which is optimal in terms of sensor node energy consumption, total channel throughput between the UAV and sensor nodes, flight time for the UAV and frequency of the node visits by the UAV. Finally, the path should also be optimal concerning node position estimation uncertainty. A Kalman Filter (KF) is used to estimate the nodes motions and Particle Swarm Optimization (PSO) is the method used to calculate the UAV path taking all of these objectives into account. The proposed node tracking aware path planning solution is compared to two other scenarios: One where the path planning is based on full knowledge of the node positions at all times, and one where path planning is based on the last known positions of the nodes

Path planning for UAV harvesting information from dynamical wireless sensor nodes at sea

A system of several wireless sensor nodes and one unmanned aerial vehicle (UAV) is considered in this research. The nodes are only floating and drifting with the sea stream. The UAV will be operating as a data mule to gather sensing information from wireless sensor nodes. Unlike prior studies, this paper addressed a realistic ocean model for the nodes movements which will be the references to the Kalman Filter (KF) in estimating for the nodes’ positions. Simulation results are evaluated for an optimal flight-able path for the UAV under several constraints by particle swarm optimization (PSO). Specifically, the deviation between the estimated positions and the referenced positions, total energy consumption by the sensors network, data rates between UAV and the nodes, flight time for the UAV, and frequency of visiting the nodes by the UAV will be considered for optimization. The systems performances will be evaluated based on these scenarios: a) an ideal and unrealistic scenario where the UAV follows the nodes continuously; b) a realistic case where the UAV only flies periodically. Discussions and solutions were also addressed for the situations when the deployed nodes are more significantly separated than the cases simulated in the paper.acceptedVersio

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

Adaptive sampling for UAV sensor network in oil spill management

In this paper we propose a method for adaptive sampling using Unmanned Aerial Vehicles (UAVs) in oil spill management. The goal is to measure and estimate oil spill concentrations at the sea surface, while at the same time identify the leak rates of sources at known positions. First we construct a cost which approximates the benefit of sampling locations at specific times. This cost is based on measures of observability and of persistency of excitation for the oil spill model. A receding horizon Mixed-Integer Linear Programming (MILP) problem is solved in order to find UAV trajectories which are optimal with respect to the cost. For UAV trajectory tracking we use a Lyapunov based controller. The oil spill concentration measurements taken by the UAVs by following these tracks are used in an adaptive observer, which provides state (concentration) and parameter (leak rate) estimates. Under the assumption that the sampling strategy described above lead to uniform complete observability and persistency of excitation, we prove Uniform Global Asymptotic Stability (UGAS) of the state estimation, parameter identification and UAV trajectory tracking errors. Finally, we provide a simulation of the proposed strategy, and compare it with two other strategies.acceptedVersio

Nonlinear observer design for a Greitzer compressor model

In this paper two different observers for a nonlinear compressor model have been developed and compared: A nonlinear observer based on a circle criterion design and an Extended Kalman Filter. Both of these observers were implemented together with linear control strategies in order to (surge-)control the nonlinear Greitzer compressor model. The newly developed nonlinear observer is a full state observer providing local asymptotic stability results. Compared to the Extended Kalman Filter, the nonlinear observer showed itself at least equivalent, even superior for open-loop estimation.© IEEE This is the authors’ accepted and refereed manuscript to the articl

Neural Network-based Model Predictive Control with Input-to-State Stability

Learning-based controllers, and especially learning-based model predictive controllers, have been used for a number of different applications with great success. In spite of good performance, a lot of these cases lack stability guarantees. In this paper we consider a scenario where the dynamics of a nonlinear system are unknown, but where input and output data are available. A prediction model is learned from data using a neural network, which in turn is used in a nonlinear model predictive control scheme. The closed-loop system is shown to be input-to-state stable with respect to the prediction error of the learned model. The approach is tested and verified in simulations, by employing the controller to a benchmark system, namely a continuous stirred tank reactor plant. Simulations show that the proposed controller successfully drives the system from random initial conditions, to a reference equilibrium point, even in the presence of noise. The results also verify the theoretical stability result.acceptedVersio

Learning-based Robust Model Predictive Control for Sector-bounded Lur'e Systems

For dynamical systems with uncertainty, robust controllers can be designed by assuming that the uncertainty is bounded. The less we know about the uncertainty in the system, the more conservative the bound must be, which in turn may lead to reduced control performance. If measurements of the uncertain term are available, this data may be used to reduce the uncertainty in order to make bounds as tight as possible. In this paper, we consider a linear system with a sector-bounded uncertainty. We develop a model predictive control algorithm to control the system, and use a weighted Bayesian linear regression model to learn the least conservative sector condition using measurements collected in closed-loop. The resulting robust model predictive control algorithm therefore reduces the conservativeness of the controller, and provides probabilistic guarantees of asymptotic stability and constraint satisfaction. The efficacy of the proposed method is shown in simulation.publishedVersio

A gap analysis for automated cargo handling operations with geared vessels frequenting small sized ports

With the Yara Birkeland, the world’s first autonomous cargo ship developed for commercial use, nearing regular unmanned operation, it is crucial to assess the availability and readiness of unmanned cargo handling solutions. While there are already fully automated container terminals at large international ports, the purpose of this study is to consider solutions to support autonomous ships for small sized ports with little infrastructure, typical of coastal harbors in Norway. The analysis centers on geared cargo vessels that can navigate such ports with minimal or no crew onboard, and the primary method used involved workshops and interviews with personnel from relevant industries. An important finding is the lack of skilled crane operators that are willing to follow the ship. The study concludes that it is important to address the following 3 key technological gaps: (1) the autonomous connection and release of break-bulk, (2) automatic securing and lashing of onboard cargo, and (3) shipboard cranes that can operate without an onsite crane operator.publishedVersio