A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes

A crane system often works in a complex environment. It is difficult to model or learn its true dynamics by traditional system identification approaches. If a dynamics model is created by minimizing its prediction error, its use tends to introduce inaccuracies and thus lead to suboptimal performance...

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Published in:	Machines (Basel) Vol. 9; no. 8; p. 177
Main Authors:	Bao, Hanqiu, Kang, Qi, An, Jing, Ma, Xianghua, Zhou, Mengchu
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.08.2021
Subjects:	Algorithms anti-sway Artificial intelligence Bridges Control algorithms Control systems design Controllers Cranes Cranes & hoists data-driven approach Dynamics Iterative methods Machine learning Mathematical models Methods Optimal control Optimization Ordinary differential equations Parameters Performance prediction performance-driven model predictive control Prediction models Predictive control Proportional integral derivative Robots Robust control System identification Systems stability Two dimensional models underactuated bridge crane
ISSN:	2075-1702, 2075-1702
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Abstract	A crane system often works in a complex environment. It is difficult to model or learn its true dynamics by traditional system identification approaches. If a dynamics model is created by minimizing its prediction error, its use tends to introduce inaccuracies and thus lead to suboptimal performance. Is it possible to learn the dynamics model of a crane that can achieve the best performance, instead of learning its true dynamics? This work answers the question by presenting a performance-driven model predictive control (P-MPC) algorithm for a two-dimensional underactuated bridge crane. In the proposed dual-layer control architecture, an inner-loop controller uses a proportional–integral–derivative controller to achieve anti-sway rapidly. An outer-loop controller uses MPC to ensure accurate trolley positioning under control constraints. Compared with classical MPC, this work proposes a data-driven method for plant modeling and controller parameter updating. By considering the control target at the learning stage, the method can avoid adjusting the controller to deal with uncertainty. We use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing control performance and then used in conjunction with optimal control schemes to efficiently design a controller for a given task. The model is updated directly based on the performance observed in experiments on the physical system in an iterative manner till a desired performance is achieved. The controller parameters and prediction models of the best closed-loop performance can be found through continuous experiments and iterative optimization. Simulation and experiment results show that we can explicitly find the dynamics model that produces the best performance for an actual system, and the method can quickly suppress swing and realize accurate trolley positioning. The results verified its effectiveness, feasibility, and superior performance on comparing it with state-of-the-art methods.
AbstractList	A crane system often works in a complex environment. It is difficult to model or learn its true dynamics by traditional system identification approaches. If a dynamics model is created by minimizing its prediction error, its use tends to introduce inaccuracies and thus lead to suboptimal performance. Is it possible to learn the dynamics model of a crane that can achieve the best performance, instead of learning its true dynamics? This work answers the question by presenting a performance-driven model predictive control (P-MPC) algorithm for a two-dimensional underactuated bridge crane. In the proposed dual-layer control architecture, an inner-loop controller uses a proportional–integral–derivative controller to achieve anti-sway rapidly. An outer-loop controller uses MPC to ensure accurate trolley positioning under control constraints. Compared with classical MPC, this work proposes a data-driven method for plant modeling and controller parameter updating. By considering the control target at the learning stage, the method can avoid adjusting the controller to deal with uncertainty. We use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing control performance and then used in conjunction with optimal control schemes to efficiently design a controller for a given task. The model is updated directly based on the performance observed in experiments on the physical system in an iterative manner till a desired performance is achieved. The controller parameters and prediction models of the best closed-loop performance can be found through continuous experiments and iterative optimization. Simulation and experiment results show that we can explicitly find the dynamics model that produces the best performance for an actual system, and the method can quickly suppress swing and realize accurate trolley positioning. The results verified its effectiveness, feasibility, and superior performance on comparing it with state-of-the-art methods.
Author	Zhou, Mengchu Kang, Qi Bao, Hanqiu An, Jing Ma, Xianghua
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SubjectTerms	Algorithms anti-sway Artificial intelligence Bridges Control algorithms Control systems design Controllers Cranes Cranes & hoists data-driven approach Dynamics Iterative methods Machine learning Mathematical models Methods Optimal control Optimization Ordinary differential equations Parameters Performance prediction performance-driven model predictive control Prediction models Predictive control Proportional integral derivative Robots Robust control System identification Systems stability Two dimensional models underactuated bridge crane
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