###### Abstract  
This paper proposes an active disturbance rejection contouring control scheme for robotic excavators suffering from model uncertainties, external disturbances, and unmeasurable states. A sliding mode observer (SMO) is firstly designed to precisely estimate both joint velocities and lumped uncertainties and disturbances. These estimations are then fed back into the main controller which is constructed based on the task coordinate frame (TCF) approach. Furthermore, to meet the requirements of high-accuracy control performance, the barrier Lyapunov function (BLF) is utilized in the control design together with the previous techniques, which guarantees the stability of the whole system. Finally, numerical simulation is conducted with a high-reliability excavator model to verify the effectiveness of the proposed control algorithm under various operating conditions. In future work, further practical problems will be conducted to realize the application of robotic excavators in construction. contouring control; robotic excavator; sliding mode observer; output constraint +
Footnote †: journal: Applied  
1
Footnote 1: School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Korea; [EMAIL_ADDRESS]  
2
Footnote 2: School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan 44610, Korea; [EMAIL_ADDRESS]  
30 June 2022  
Accepted: 22 July 2022  
25 July 2022  
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**Acticle**  
**Active Disturbance Rejection Contouring Control of Robotic Excavators with Output Constraints and Sliding Mode Observer. _Appl. Sci._**2022**, _12_, 7453. [[https://doi.org/10.3390/appl2157453](https://doi.org/10.3390/appl2157453)]([https://doi.org/10.3390/appl2157453](https://doi.org/10.3390/appl2157453))  
**Publisher's Note:**  
**MMPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **  
**Acticle**  
together with the main position tracking control, which provided simple structures, but the final control performance was not reliable and accurate since the excavator model information was not utilized. In contrast, this information was integrated into the task coordinate frame (TCF) approach [4], which provided a general framework for further model-based control algorithms in order to separately regulate the contouring, tangential, and orientation errors. In [17], an MPC was also developed for the contouring control problem of excavators, which relies on the lifting-linearization method with the Koopman operator. Based on the linearized data-driven model of the excavator, the optimal control algorithm was derived to minimize the pre-defined cost function including contouring performance with other criteria. However, since this method requires many computational efforts, the realization of real-time performance in practical conditions will be a challenge. Like the developments in model-based control approaches, uncertainties and disturbances must be considered in the control design for robotic excavators since these difficulties always exist and deteriorate the system performance during interaction with the environment. Therefore, uncertainties and disturbances attenuation techniques are important to guarantee high-accuracy control performance. Overall, two main approaches have been applied for general robotic applications, i.e., adaptive control and disturbance observer (DOB). For the adaptive control, it was originally proposed to deal with structured uncertainties, i.e., known uncertainty functions with unknown slow-varying parameters in linear regression form [18; 19]. Moreover, this method was further extended to the adaptive mechanism with intelligent approximators such as neural networks (NNs) [20; 21] or fuzzy logic systems (FLSs) [22; 23], which can handle unstructured uncertainties. However, these approaches were not designed to deal with external disturbances due to the states-dependent characteristics.