Minimum time control of a gantry crane system with rate constraints

Stein, Adrian; Singh, Tarunraj

doi:10.1016/j.ymssp.2023.110120

Cited by 8 publications

(2 citation statements)

References 54 publications

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“…This problem is extensively studied in the literature, and various open-loop and closed-loop control strategies are comprehensively reviewed in [2], while the more current state of the art focusing on the modeling and control of overhead cranes is provided in [3]. Recent works include input shaping [4,5], time-optimal [6], command shaping [7], and feedforward [8] anti-sway crane control strategies. New concepts in the field of closed-loop control have been developed using PID-like coupling control [9,10], sliding mode control [11] combined with a state observer [12] or PD controller [13], passivity-based control [14,15], H-infinity [16], and back-stepping-based [17] control schemes.…”

Section: Introductionmentioning

confidence: 99%

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

Kusznir,

Smoczek

2024

Applied Sciences

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This paper presents a new approach to the nonlinear model predictive control (NMPC) of an underactuated overhead crane system developed using a data-driven prediction model obtained utilizing the regularized genetic programming-based symbolic regression method. Grammar-guided genetic programming combined with regularized least squares was applied to identify a nonlinear autoregressive model with an exogenous input (NARX) prediction model of the crane dynamics from input–output data. The resulting prediction model was implemented in the NMPC scheme, using a particle swarm optimization (PSO) algorithm as a solver to find an optimal sequence of the control actions satisfying multi-objective performance requirements and input constraints. The feasibility and performance of the controller were experimentally verified using a laboratory crane actuated by AC motors and compared with a discrete-time feedback controller developed using the pole placement technique. A series of experiments proved the effectiveness of the controller in terms of robustness against operating condition variation and external disturbances.

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Section: Introductionmentioning

confidence: 99%

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

Kusznir,

Smoczek

2024

Applied Sciences

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“…Interestingly, many scholars (such as in (Rigatos, 2023;Shao et al, 2023;Xu et al, 2023)) have engaged in analysing the dynamic characteristics of crane systems and proposed numerous control strategies to optimize their performance. Nonetheless, most existing studies for the analysis and payload vibration suppression focused on gantry cranes (Lei et al, 2023;Stein & Singh, 2023). As described in (Rauscher & Sawodny, 2020), implementation of control strategies on tower cranes is rare due to its complexity and lack of reliable structural dynamics.…”

mentioning

confidence: 99%

Hybrid input shaping and fuzzy logic‐based position and oscillation control of tower crane system

Alhassan,

Assawinchaichote,

Zhang

et al. 2023

Expert Systems

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Effective control of the tower crane system (TCS) is crucial for the safe and quick transport of goods from one point to another in industries and construction sites. However, the existing literature either depends on the dynamic model of the system, which is prone to errors and assumptions, or requires the feedback of the states to be controlled, which can be costly and or complex. In particular, the TCS has many states and is highly nonlinear. Thus, a non‐model‐based control approach that depends on minimal feedback sensors is needed. The primary contribution of this work is that the potential hybrid configurations of the input shaping control (ISC) and fuzzy logic control (FLC) were proposed and investigated. The study provides the best hybrid configuration of the ISC and FLC (non‐model‐based controllers) for optimal positioning and anti‐swing control of the TCS. Three configurations of the ISC + FLC, namely shaper as reference input (SARI), shaper as input to the FLC (SAI2F), and shaper as input to the plant (SAI2P) were investigated to assess the optimal hybrid configuration of the ISC + FLC. The results demonstrated that the hybrid ISC + FLC scheme had improved the response of ISC by at least 150% and enhanced the oscillation reduction of FLC by 72%. Finally, these analyses showed that the ISC + FLC could achieve reasonable control of the tower crane by only considering the output of one state (position). This makes the control scheme cheaper and reduces the complexity and computational time compared to other full‐state feedback controllers.

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