An energy-time optimal autonomous motion control framework for overhead cranes in the presence of obstacles

2020 ◽  
pp. 095440622095450
Author(s):  
Xinwei Wang ◽  
Jie Liu ◽  
Xianzhou Dong ◽  
Haijun Peng ◽  
Chongwei Li
Keyword(s):  
Motion Control ◽  
Trajectory Tracking ◽  
Overhead Cranes ◽  
Control Framework ◽  
Weighted Matrix ◽  
Autonomous Motion ◽  
Avoidance Condition ◽  
Time Optimal ◽  
Predictive Controller ◽  
Time And Energy

This paper focuses on the autonomous motion control of 3-D underactuated overhead cranes in the presence of obstacles, and an “offline motion planning + online trajectory tracking” framework is developed. In the motion planner, to meet the balance between transfer time and energy consumption, the transfer mission is formulated as an energy-time hybrid optimal control problem. And a simple and conservative collision-avoidance condition is derived. To achieve fast and robust calculations, an iterative procedure that determines optimal terminal time based on the secant method is developed. Finally, to realize the high-precision trajectory tracking and fast residual sway suppression, a model predictive controller with a piecewise weighted matrix is designed. Numerical simulation demonstrates that the discussed framework is effective.

A Novel EPT Autonomous Motion Control Framework for an Off-axle Hitching Tractor-trailer System with Drawbar

2020 ◽  
pp. 1-1
Author(s):  
Jie Liu ◽  
Xianzhou Dong ◽  
Junyu Wang ◽  
Oskar Ljungqvist ◽  
Chen Lu ◽  
...  
Keyword(s):  
Motion Control ◽  
Control Framework ◽  
Autonomous Motion

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

2020 ◽  
Author(s):  
Benjamin Armentor ◽  
Joseph Stevens ◽  
Nathan Madsen ◽  
Andrew Durand ◽  
Joshua Vaughan
Keyword(s):  
Trajectory Tracking ◽  
Wind Waves ◽  
Tracking Error ◽  
Tracking Performance ◽  
Model Predictive Controller ◽  
Prediction Horizon ◽  
Surface Vessels ◽  
Waves And Currents ◽  
Predictive Controller ◽  
Disturbance Model

Abstract For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

Managing Self-Replicating Innovative Goods

2021 ◽  
Author(s):  
Bin Hu ◽  
Zhankun Sun
Keyword(s):  
Innovation Diffusion ◽  
Operations Management ◽  
Continuous Time ◽  
Three Dimensional ◽  
Production Capacity ◽  
Marketing Strategies ◽  
Control Framework ◽  
Time Optimal ◽  
Inventory Decision ◽  
Self Replication

Inspired by self-replicating three-dimensional printers and innovative agricultural and husbandry goods, we study optimal production and sales policies for a manufacturer of self-replicating innovative goods with a focus on the unique “keep-or-sell” trade-off—namely, whether a newly produced unit should be sold to satisfy demand and stimulate future demand or added to inventory to increase production capacity. We adopt the continuous-time optimal control framework and marry a self-replication model on the production side to the canonical innovation-diffusion model on the demand side. By analyzing the model, we identify a condition that differentiates Strong and Weak Replicability regimes, wherein production and sales, respectively, take priority over the other and fully characterize their distinct optimal policies. These insights prove robust and helpful in several extensions, including backlogged demand, liquidity constraints, stochastic innovation diffusion, launch inventory decision, and exogenous demand. We also find that social marketing strategies are particularly well suited for self-replicating innovative goods under Strong Replication. This paper was accepted by Victor Martínez de Albéniz, operations management.

A Receding-Horizon Framework for Co-Optimizing the Velocity and Power-Split of Automated Plug-In Hybrid Electric Vehicles

2021 ◽  
Vol 1 (4) ◽  
Author(s):  
Di Chen ◽  
Mike Huang ◽  
Anna Stefanopoulou ◽  
Youngki Kim
Keyword(s):  
Computation Time ◽  
Global Optimality ◽  
Receding Horizon ◽  
Prediction Horizon ◽  
Control Framework ◽  
Power Split ◽  
Online Implementation ◽  
Driving Scenario ◽  
Predictive Controller ◽  
Fuel Economy Improvement

Abstract This paper presents a control framework to co-optimize the velocity and power-split operation of a plug-in hybrid vehicle (PHEV) online in the presence of traffic constraints. The principal challenge in its online implementation lies in the conflict between the long control horizon required for global optimality and limits in available computational power. To resolve the conflict between the length of horizon and its computation complexity, we propose a receding-horizon strategy where co-states are used to approximate the future cost, helping to shorten the prediction horizon. In particular, we update the co-state using a nominal trajectory and the temporal-difference (TD) error based on co-state dynamics. Our simulation results demonstrate a 12% fuel economy improvement over the sequential/layered control strategy for a given driving scenario. Moreover, its real-time practicality is evidenced by a computation time per model predictive controller (MPC) step on average of around 80 ms within a 10 s prediction horizon.

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