Concurrent Learning Based Tracking Control of Nonlinear Systems using Gaussian Process

Bhandari, Vedant; Kayacan, Erkan

doi:10.1109/aim46487.2021.9517642

Cited by 3 publications

(1 citation statement)

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“…The inherent presence of uncertainties in a system model results in a mismatch between the model that is used to synthesise the control algorithm and the real system, resulting in performance degradation for classical and optimal control methods [10][11][12][13]. Adaptive control seeks to compensate for uncertainties in the system model by either (i) estimating the system model explicitly and modifying the control law accordingly or (ii) estimating the representative matched and unmatched uncertainties present in the system, and compensating for them in the control law [14][15][16][17][18]. Both categories aim to retain the nominal design performance of the nonadaptive controller in the presence of model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Simulation and Performance Characterisation of an Adaptive Model Predictive Control Method for Booster Separation and Re-Entry

Chai

Kayacan

2023

Electronics

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This paper evaluates the L1 adaptive model predictive control (AMPC-L1) method in terms of its control performance and computational load. The control performance is assessed on the basis of the nonlinear simulation of a fly-back booster conducting stage separation and re-entry, and compared to baseline nonadaptive MPC and as a pole placement controller in both longitudinal and lateral control tasks. Simulation results show that AMPC-L1 exhibits superior control performance under nominal conditions, and aerodynamic and guidance law uncertainties. The computational load of AMPC-L1 is also evaluated on an embedded platform to demonstrate that AMPC-L1 preserves the efficiency properties of AMPC while improving its performance.

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