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 negative problem 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 control algorithm that enables on-line adaptation within the Dynamics Based Control (DBC) framework. In our experiments, we performed a range of tests with increasing model incoherence induced by three types of exogenous environment perturbations: catastrophic, periodic and deviating. The results show that EA-EMT resolved model incoherence and significantly outperformed the best currently available DBC solution by up to $95$$%$.