This paper presents an adaptive control framework that enables a unmanned robot to follow its planned trajectory including cases when the reliability of its sensor suite changes unpredictably. The main contribution is the adaptive mode of operation: the system dynamically adjusts the threshold value of each sensor stream, recalculates their weights in real time, and recombines control system. While high precision measurements are available, the controller runs in a nominal linear quadratic mode. When observability drops – because a sensor drifts, saturates, or fails – the algorithm transitions smoothly to a prediction only mode that relies on the process model, and, if necessary, to a robust H infinity mode that preserves stability under worst case disturbances. The underlying estimation layer is based on an innovation driven weighting rule embedded in an Extended Kalman Filter, making the approach independent of any particular sensor type. Hardware simulation and outdoor testing confirm that adaptive mode significantly improves tracking accuracy, shortens recovery time after faults, and eliminates the need for manual retuning when the sensor configuration changes.