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This paper presents a control architecture in which a direct adaptive controltechnique is used within the model predictive control framework, using theconcurrent learning based approach, to compensate for model uncertainties. Ateach time step, the control sequences and the parameter estimates are both usedas the optimization arguments, thereby undermining the need for switchingbetween the learning phase and the control phase, as is the case withhybrid-direct-indirect control architectures. The state derivatives areapproximated using pseudospectral methods, which are vastly used for numericaloptimal control problems. Theoretical results and numerical simulation examplesare used to establish the effectiveness of the architecture.

Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation