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In recent years, owing to the rapidly increasing labor costs, the demand for robots in daily services and industrial operations has been increased significantly. For further applications and human–robot interaction in an unstructured open environment, fast and accurate tracking and strong disturbance rejection ability are required. However, utilizing a conventional controller can make it difficult for the robot to meet these demands, and when a robot is required to perform at a high-speed and large range of motion, conventional controllers may not perform effectively or even lead to the instability.

The main idea is to develop the control law by combining the SMC feedback with the ADRC control architecture to improve the robustness and control quality of a conventional SMC controller. The problem is formulated and solved in the framework of ADRC. For better estimation and control performance, a generalized proportional integral observer (GPIO) technique is employed to estimate and compensate for unmodeled dynamics and other unknown time-varying disturbances. And benefiting from the usage of GPIO, a new SMC law can be designed by synthesizing the estimation and its history.

The employed methodology introduced a significant improvement in handling the uncertainties of the system parameters without compromising the nominal system control quality and intuitiveness of the conventional ADRC design. First, the proposed method combines the advantages of the ADRC and SMC method, which achieved the best tracking performance among these controllers. Second, the proposed controller is sufficiently robust to various disturbances and results in smaller tracking errors. Third, the proposed control method is insensitive to control parameters which indicates a good application potential.

High-performance robot tracking control is the basis for further robot applications in open environments and human–robot interfaces, which require high tracking accuracy and strong disturbance rejection. However, both the varied dynamics of the system and rapidly changing nonlinear coupling characteristic significantly increase the control difficulty. The proposed method gives a new replacement of PID controller in robot systems, which does not require an accurate dynamic system model, is insensitive to control parameters and can perform promisingly for response rapidity and steady-state accuracy, as well as in the presence of strong unknown disturbances.