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

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@Article{kusznir:2024:AS2,

• author = "Tom Kusznir and Jaroslaw Smoczek",
• title = "Nonlinear Model Predictive Control with Evolutionary Data-Driven Prediction Model and Particle Swarm Optimization Optimizer for an Overhead Crane",
• journal = "Applied Sciences",
• year = "2024",
• volume = "14",
• number = "12",
• pages = "Article No. 5112",
• keywords = "genetic algorithms, genetic programming, PSO",
• ISSN = "2076-3417",
• URL = "https://www.mdpi.com/2076-3417/14/12/5112",
• DOI = "doi:10.3390/app14125112",
• abstract = "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 using 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 optimisation (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.",
• notes = "also known as \cite{app14125112}",

}

Genetic Programming entries for Tom Kusznir Jaroslaw Smoczek

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