Abstract
This paper presents an output-based tracking controller for a class of car-like mobile robot (CLMR) subject to slipping and skidding. The slipping and skidding are regarded as external disturbances, and an event-triggered extended state observer (ET-ESO) is utilized to recover the velocities as well as to estimate the uncertainties and disturbances. The constrained longitudinal velocity is established, conforming to the traffic flow theory on the kinematic level. The velocity control law and heading angle control law are developed on the dynamic level, respectively. The input to state stability (ISS) of the closed-loop system is analyzed via cascade theory. Simulation results are given to demonstrate the effectiveness of the proposed tracking controller for CLMR subject to slipping and skidding.
Highlights
With the increasingly widespread application of robotics, wheeled mobile robots (WMRs) tracking control problems have been studied in the past two decades
To address the slipping and skidding problem, tracking controllers were presented for WMRs with longitudinal and lateral slippage based on improved linear active disturbance rejection control (ADRC) in [15], a disturbance observer in [16] and the reinforcement learning method in [17]
Based on the above observations, this paper considers the constrained tracking control for a class of car-like mobile robot (CLMR) subject to slipping and skidding
Summary
With the increasingly widespread application of robotics, wheeled mobile robots (WMRs) tracking control problems have been studied in the past two decades. In the existing results [12,15,16,17], the virtual velocity input or given velocity is unlimited, which means that negative longitudinal velocity may come out during movement Noting that it violates the traffic flow theory in the application of WMRs such as logistics system and self-driving automobile, which intensifies the potential collision risk, it is highly desirable to develop a tracking control scheme with velocity constraints. Another challenge in the tracking control of CLMR is to obtain the desired performance with the least resources in the presence of model uncertainties and disturbances, including least sensors and communication bandwidth. The derivatives of gv and gw are bounded, such that gv + |gw| ≤ g∗ with g∗ ∈ being a positive constant
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