Abstract:
Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on both control signals and system states. T...Show MoreMetadata
Abstract:
Model predictive control has emerged as a prominent technique in control engineering due to its ability to handle constraints on both control signals and system states. This capability makes model predictive control a powerful tool, particularly for complex systems with operational limitations. However, a major challenge associated with model predictive control is the "curse of dimensionality" arising from the constrained optimization problem solved at each time step. This problem becomes computationally expensive as the system dimension increases. This study proposes an accelerated model predictive control algorithm that addresses the curse of dimensionality. We achieve this by solving an equivalent suboptimal model predictive control problem within a reduced-dimensional subspace. The subspace is efficiently calculated using singular value decomposition of the Hessian matrix associated with the quadratic cost function. An adaptation law dynamically determines the subspace size, balancing accuracy and computational efficiency of the model predictive control controller.
Date of Conference: 10-12 October 2024
Date Added to IEEE Xplore: 11 November 2024
ISBN Information: