In this paper we extend the control methodology based on Extended Markov Tracking (EMT) by providing the control algorithm with capabilities to calibrate and even partially reconstruct the environment's model. This enables us to resolve the problem of performance deterioration due to model incoherence, a problem faced in all model-based control methods. The new algorithm, Ensemble Actions EMT (EA-EMT), utilises the initial environment model as a library of state transition functions and applies a variation of prediction with experts to assemble and calibrate a revised model. By so doing, this is the first hybrid control algorithm that enables on-line adaptation within the egocentric control framework which dictates the control of an agent's perceptions, rather than an agent's environment state. In our experiments, we performed a range of tests with increasing model incoherence induced by three types of exogenous environment perturbations: catastrophic-the environment becomes completely inconsistent with the model, deviating-some aspect of the environment behaviour diverges compared to that specified in the model, and periodic-the environment alternates between several possible divergences. The results show that EA-EMT resolved model incoherence and significantly outperformed its EMT predecessor by up to 95%.