A fuzzy recurrent neural network controller with fault tolerable thrust allocation control strategy has been proposed for Remote Operated Vehicles (ROV). Extended functional link is issued for fuzzy recurrent neural network controller to improve the ability of identification and response. Online training algorithm is developed based on gradient descent method. The learning rate parameters are determined according to particle swarm optimization, hence the whole network convergence is guaranteed. On the aspect of force allocation, a model has been established according to thruster positions for open frame remote operated vehicles based on which the fault tolerable problem has been transformed into dual optimization problem. The energy function concerning ROV control has been established so as to obtain thrust optimal allocation under fault conditions. Experiments have demonstrated that the controller can improve the ROV capacity to handle nonlinear characteristics and strong disturbance, reduce control errors. When one of main or side thrust fails, the controller can still accomplish precise horizontal position control through thrust optimization allocation strategy. Therefore, the reliable operation of ROV for the long time-span has been improved.