For robot manipulators, there are two types of disturbances. One is model parametric uncertainty; the other is unmodelled parameters such as joint friction forces and external disturbances. Unmodelled joint frictions and external disturbances reduce performance in terms of positioning accuracy and repeatability. In order to compensate for unmodelled parameters, the design of a new controller is considered. First, the modelled and unmodelled parameters are included in a dynamic model. Then, based on the dynamic model, a new Lyapunov function is developed. After that, new nonlinear joint friction and external disturbance estimation laws are derived as an analytic solution from the Lyapunov function; thus, the stability of the closed system is guaranteed. Better values of the adaptive dynamic compensators can be extracted by fuzzy rules according to the tracking error. Limitations and knowledge about friction and external disturbances are not required for the design of the controller. The controller compensates for all possible model parameter uncertainties, all possible unknown joint frictions and external disturbances.

Based on the author's knowledge the paper gives a brief account of some of the scientific achievements of robotics that were of crucial importance to its development.

In a rough chronological order these are: zero-moment concept and semi-inverse method; recursive formulation of robot dynamics; computer-aided derivation of robot dynamics in symbolic form; dynamic approach to generation of trajectories of robotic manipulators; centralized feedforward control in robotics; robot dynamic control; decentralized control and observer applied to strongly coupled active mechanisms; force feedback in dynamic control of robots; decentralized control stability tests for robotic mechanisms; underactuated robotic systems; practical stability tests in robotics; unified approach to control laws synthesis for robot interacting with dynamic environment; modeling and control of multi-arm cooperating robots interacting with environment; connectionist algorithms for advanced learning control of robots interacting with dynamic environment; fuzzy logic robot control with model-based dynamic compensation, and internal redundancy – a new way to improve robot dynamic performance.