The separation principle for control and estimation is the traditional method for stochastic optimal control of linear systems. However, such an approach does not hold true as systems become increasingly nonlinear. This paper presents a new method for control through estimation based on the Path Integral (PI) formulation and the Riemann Manifold Hamiltonian Monte Carlo (RMHMC) method. The approach uses the path integral method to formulate the control problem as an estimation problem and adds the estimation of the model parameters to the problem. Then, a solution is found by solving the estimation problem by Riemann Manifold Hamiltonian Monte Carlo (RMHMC) sampling which includes a solution for the control parameters. By solving the nonlinear control and estimation problem using the path integral method via RMHMC, no open algorithmic tuning parameters other than exploration noise are required and the control solution has numerically robust performance to high dimensionality in the system control input. The methodology is specifically applied to the spacecraft attitude control problem, though it should be noted that such an approach is generalizable. Simulation results are presented which demonstrate good performance when utilizing the PI via RMHMC method.