In this dissertation, an adaptive strategy has been presented to reject unknown force disturbances exerted on robot manipulators. In this strategy, the feedback of joint positions and velocities and the feedforward of force have been used to control the system and reject the force disturbances. Position and velocity signals have been supplied by position and velocity sensors. However, due to high cost of force sensor and its technical application problems, force signal has been extracted by an adaptive force estimator. Based on this estimator, adaptive algorithms for robot manipulator control have been proposed such that the control systems are able to reject force disturbances in their corresponding cases, and consequently, robot can track the desired motion trajectory. Proper algorithms have been presented for the case that robot dynamics are completely known and also the case that robot inertia parameters are unknown. In each of these cases, type, domain and conditions of the stability of the closed-loop system have been analyzed and proved. Since unstructured uncertainty can significantly diminish the performance and stability of the system, an adaptive robust control algorithm has been proposed to make the control system robust to this type of uncertainty. Uniform stability of this algorithm has been explained and proved through a theorem. Global asymptotic stability (GUAS) and uniform ultimate boundedness (UUB) of tracking error have been guaranteed for some special cases. To show the suitable performance and effectiveness of the proposed approaches, and also correctness of the presented theoretical topics, in each case some simulations performed on a typical robot manipulator have been presented.