Resilient consensus control refers to a system's ability to maintain agreement among its components despite disruptions, failures, or malicious attacks. This paper introduces a resilient control algorithm for a group of robotic manipulators to achieve leader-follower consensus within their workspace, despite actuator faults and deception attacks affecting exchanged signals. The proposed approach leverages adaptive control algorithms to develop the control laws. It begins by introducing an adaptive fault-tolerant tracking control to ensure tracking performance despite model uncertainties, actuator faults, and external disturbances. An adaptive observer is then developed to mitigate the effects of false data injections caused by deception attacks. A key feature of this control framework is its ability to operate without requiring fault and attack detection, thereby improving the system's robustness and applicability. The stability of the network and the convergence of the filtered errors are demonstrated using Lyapunov techniques and the equivalence principle. The proposed control framework is validated through numerical simulations involving a network of four heterogeneous manipulators, with results confirming the approach's effectiveness in enhancing system reliability.