A Data-driven MPC Algorithm for Bridge Cranes

Bao, Hanqiu; An, Jing; Zhou, MengChu; Kang, Qi

doi:10.1109/icamechs49982.2020.9310150

Cited by 3 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Magnified results of switching mechanisms η 1 , η 2 , and η 3 as previously expressed in equation ( 25) for the operationalization of multiple nodes are further illustrated through Figures 15(a)-(c). In particular, regulation of the involved switching mechanisms would solely rely on the control variable error of ÀT h > e j ðtÞ > T h in accordance to the normalized Gaussian function as formerly stated in equation (26). Observed through Figure 14, multiple nodes were activated ensuing a comparatively higher control variable error response that surpassed the error threshold of T h ¼ ± 0.05 yielded between respective simulated intervals of 0-1.1 s, 0-0.6 s, and 0-1.4 s for the individual factors of e 1 ðtÞ, e 2 ðtÞ, and e 3 ðtÞ.…”

Section: Implementation and Resultsmentioning

confidence: 99%

“…24 Comparative study in terms of position and swing angle was further undertaken by Solihin et al 25 in the year 2019 among the current metaheuristic-based algorithms such as PSO, cuckoo search (CS), and differential evolution (DE) towards the optimization of fuzzy controller within a gantry crane system. However, Bayesian optimizer (BO) was especially operationalized by Bao et al 26 against the PID controller in the year 2020 for the tuning of data-driven model predictive controller (MPC) with absence of analytical model knowledge concerning the implemented crane system. Such was prior to the suggested implementation of fully informed particle swarm optimization (FIPSO) by Valluru et al 27 within both multi-loop linear-PID and nonlinear-PID controllers of a crane system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Tumari

Ahmad

Suid

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

Conventionally, researchers have favored the model-based control scheme for controlling gantry crane systems. However, this method necessitates a substantial investment of time and resources in order to develop an accurate mathematical model of the complex crane system. Recognizing this challenge, the current paper introduces a novel data-driven control scheme that relies exclusively on input and output data. Undertaking a couple of modifications to the conventional marine predators algorithm (MPA), random average marine predators algorithm (RAMPA) with tunable adaptive coefficient to control the step size ( CF) has been proposed in this paper as an enhanced alternative towards fine-tuning data-driven multiple-node hormone regulation neuroendocrine-PID (MnHR-NEPID) controller parameters for the multi-input–multi-output (MIMO) gantry crane system. First modification involved a random average location calculation within the algorithm’s updating mechanism to solve the local optima issue. The second modification then introduced tunable CF that enhanced search capacity by enabling users’ resilience towards attaining an offsetting level of exploration and exploitation phases. Effectiveness of the proposed method is evaluated based on the convergence curve and statistical analysis of the fitness function, the total norms of error and input, Wilcoxon’s rank test, time response analysis, and robustness analysis under the influence of external disturbance. Comparative findings alongside other existing metaheuristic-based algorithms confirmed excellence of the proposed method through its superior performance against the conventional MPA, particle swarm optimization (PSO), grey wolf optimizer (GWO), moth-flame optimization (MFO), multi-verse optimizer (MVO), sine-cosine algorithm (SCA), salp-swarm algorithm (SSA), slime mould algorithm (SMA), flow direction algorithm (FDA), and the formally published adaptive safe experimentation dynamics (ASED)-based methods.

show abstract

Section: Implementation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

Tumari

Ahmad

Suid

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

View full text Add to dashboard Cite

show abstract

“…In this paper, we use a Bayesian optimization (BO) strategy to do so. Similar to the work [37], parameter tuning is summarized in Algorithm 1. Our goal is to update effectively the hyperparameters when new data is observed.…”

Section: P-mpc Controller Parameter Tuningmentioning

confidence: 99%

“…It mainly includes three modules: a closed-loop experimental module, a closed-loop control module, and a Bayes-based controller parameter optimization and model learning module [36]. Following [37], we designed a dual-layer control architecture. An inner controller aims to quickly suppress the swing angle, while an outer one can handle control constraints and state ones.…”

Section: Introductionmentioning

confidence: 99%

A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes

Bao

Kang

et al. 2021

Machines

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Para asegurar el movimiento sin oscilaciones y la precisión de posicionamiento al trasladar cargas se han planteado sistemas que utilizan múltiples cables actuados para que, combinando la tensión entre ellos pueda disminuirse la oscilación del gancho [5]. Otras soluciones incluyen sistemas de control predictivo [6] y algoritmos de inteligencia artificial que contienen redes neuronales, lógica difusa y algoritmos genéticos [7]. Pero la implementación de estos algoritmos necesita de entradas reales tales como el ángulo de oscilación del cable, el cual se puede obtener mediante visión artificial [8] [9].…”

unclassified

Bridge Crane Monitoring using a 3D LiDAR and Deep Learning

García

Martínez

Reina

2023

IEEE Latin Am. Trans.

View full text Add to dashboard Cite

The use of overhead cranes in warehouses and factories has advantages for handling and transporting bulky and/or heavy loads. But it also involves risks such as collisions with other fixed or mobile elements in the working environment. Different types of sensors have been used for monitoring its operation, mainly artificial vision. In this paper, it is employed a three-dimensional (3D) LiDAR to capture the workspace of a bridge crane. The point clouds generated by this laser sensor are delivered to a convolutional neural network to detect the position of the bridge and its carriage, which allows to locate the hook and the suspended load afterwards. Additionally, the laser scans can also be used to warn the operator of possible collisions with fixed elements of the warehouse. The tests carried out show that the proposed system can be successfully used for monitoring overhead cranes.

show abstract

A Data-driven MPC Algorithm for Bridge Cranes

Cited by 3 publications

References 21 publications

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

An improved marine predators algorithm tuned data-driven multiple-node hormone regulation neuroendocrine-PID controller for multi-input–multi-output gantry crane system

A Performance-Driven MPC Algorithm for Underactuated Bridge Cranes

Bridge Crane Monitoring using a 3D LiDAR and Deep Learning

Contact Info

Product

Resources

About