In this thesis, filtering, estimation and indirect adaptive control algorithms applied to bilateral teleoperation systems are proposed. The objective of this work was to identify and to implement indirect adaptive control in a slave master system. In a growing scenario of remote access to environments and equipment, as in industry 4.0, there is a need to quantify and characterize dynamic system parameters in order to improve the performance of control systems.In this manuscript three contributions regarding the teleoperated systems estimation and control are proposed. First, the Extended Kalman Filter (EKF) algorithm was combined with the Recursive Least Squares State-Variable Filter (RLSSVF), giving rise to the Hybrid Algorithm (AH1). The AH1 was able to estimate the parameters of a continuous time variant system in the state space form. In the second contribution, the Kalman Filter (KF) algorithm was combined with the Recursive Least Squares Estimator with Forgetting Factor Ú (RLSEFFÚ), originating the Hybrid Algorithm (AH2). AH2 was able to estimate specific parameters of a continuous time variant state space system. In the last contribution, the Method for Parameter and Delay Time Estimation (MEPTA) was combined with the Pole Allocation Method, giving rise to the Hybrid Algorithm for Identification and Adaptive Control (AHICA). Thus, AHICA was able to perform indirect adaptive control of a process with discrete time variant parameters. Such control system meets transient and stationary constraints, as well as the setpoint tracking.The proposed methods presented a good performance, especially regarding the algorithms coupling, what can be verified through the simulated results. Such methods can be applied to similar problems from new mathematical arrangements and computational constructions.