Abstract
This paper considers a new design of model predictive control based on specific models in the form of adaptive orthogonal polynomial networks, built around a specially tailored basis of generalized orthogonal functions. Polynomial model has a single layer structure and a smaller number of model parameters than classical neural networks, usually used for model predictive control design, leading to lower complexity and shorter calculation time. Desired property of adaptability of the model is achieved by using additional variable factors inside the orthogonal basis. The designed controller was applied in control of twin-rotor aero-dynamic system as a representative of nonlinear multiple input-multiple output systems and compared to the other state-of-the-art control algorithms.
Highlights
Model predictive control (MPC) is a feedback control algorithm that uses the model of the plant to predict future plant outputs over a specified time horizon
1Abstract—This paper considers a new design of model predictive control based on specific models in the form of adaptive orthogonal polynomial networks, built around a specially tailored basis of generalized orthogonal functions
Validation of the proposed control algorithm, i.e., Model Predictive Control based on Adaptive Orthogonal Polynomial Networks (AOPNMPC), given in Fig. 2, was performed by comparing the performances with the other two control strategies already proven to be suitable for twin-rotor aerodynamic system (TRAS)
Summary
Model predictive control (MPC) is a feedback control algorithm that uses the model of the plant to predict future plant outputs over a specified time horizon. Authors of the papers in [15], [16] deal with MPC design for achieving positioning or trajectory tracking for TRAS in coupled form by using linearized models These solutions provide a simplification of the control problem to a series of direct matrix algebra calculations that are fast and robust. NN complexity directly affects both the training time and real time calculations of the network’s outputs essential for successful MPC The solution for these drawbacks, proposed in this paper, is to use a special type of networks (known as polynomial neural networks [22]) for modelling of the plant.
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