A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes

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.

Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming

“…The EE method consists of generating a series of R trajectories, where each trajectory is composed of k + 1 points, where k is the number of input variables to be varied. The inputs are then perturbed one factor at a time (OAT) by a step ∆, as shown in (12), and require a total of R(k + 1) model evaluations.…”

“…In [12], Bayesian optimization is used to optimize the crane model and controller with training data collected from experiments carried out on a laboratory-scale overhead crane. A flat output identification algorithm is proposed in [13] to identify a rotary crane based on data measured on a laboratory stand.…”

Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane

“…Different structures of artificial neural network (ANN) models of an overhead crane's inverse dynamics were trained and validated using the Levenberg-Marquardt algorithm, with data computed in simulations carried out using a model derived analytically in [35]. A hybrid PID and MPC scheme with Bayesian optimization for the data-driven identification of linear-model and controller parameter tuning was tested on a laboratory-scale overhead crane in [36]. An interesting alternative to model-based control is data-driven model-free control, which has recently proven its effectiveness for complex dynamical problems [37,38].…”

Design and Control of Advanced Mechatronics Systems

“…The next paper [6] presented a performance-driven model predictive control algorithm for a two-dimensional underactuated bridge crane. In the proposed dual-layer control architecture, an inner-loop controller used a proportional-integral-derivative controller to achieve anti-sway rapidly.…”