Mobile robots have played a vital role in the transportation industries, service robotics, and autonomous vehicles over the past decades. The development of robust tracking controllers has made mobile robots a powerful tool that can replace humans in industrial work. However, most of the traditional controller updates are time-based and triggered at every predetermined time interval, which requires high communication bandwidth. Therefore, an event-triggered control scheme is essential to release the redundant data transmission. This paper presents a novel parameter-adaptive event-trigger sliding mode to control a two-wheeled mobile robot. The adaptive control scheme ensures that the mobile robot system can be controlled accurately without the knowledge of physical parameters. Meanwhile, the event trigger sliding approach guarantees the system robustness and reduces resource usage. A simulation in MATLAB and an experiment are carried out to validate the efficiency of the proposed controller.

In this paper, a novel backstepping terminal super-twisting sliding mode (TSTSM) with high order sliding mode observer (HOSMO) is proposed to control the two degrees of freedom (DOFs) Serial Elastic Actuator (SEA), inspired by a lower limb of humanoid robots. First, the dynamic model, extended from our previous study, is presented for developing the control algorithm. Secondly, the backstepping technique is utilized to separate the overall system into two subsystems. One of the challenges of SEA is to deal with the evident oscillations caused by the elastic element, which might lead to the degrading performance of load position control. In order to reduce this adverse effect, a TSTSM is proposed to control the position tracking of two subsystems. The advantages of TSTSM are the finite-time convergence despite the bounded perturbation and the dramatic reduction of the chattering phenomenon. To construct and implement the TSTSM controller, it requires the knowledge of all states, which is not available in the current lower limb system setup. Therefore, a HOSMO is utilized to estimate the unknown states. Finally, experiment results are carried out to assess the effectiveness of the proposed controller and compare it with those of different control schemes.

Purpose: Humanoid robots have been utilized in many fields such as medical, construction, and disaster response. While humanoid robots nowadays can achieve great capabilities, the one-leg balancing task still poses a challenging problem. This paper aims to propose a novel approach to solve the problem.Design/methodology/approach: To aid the balance of one leg in humanoid robot, an external balance mechanism is inserted to the back of the humanoid robot. First, a dynamic model of the humanoid robot with balance mechanism and its simplified model are introduced. Second, a backstepping-based control method is utilized to build the proposed controller for one-leg stance system through two steps. For the first step, a minimum observer-based controller with a virtual control input is used to control the first sub-system reaching the desired reference input. For the second step, a virtual control input is considered as a reference input of a second sub-system, then a model reference adaptive controller (MRAC) is employed to control the second sub-system reaching the virtual control input in presence of uncertainties. By using the external balance mechanism, the sideway balancing task is separated from normal walking function. Furthermore, the utilization of the balance mechanism ensures the humanoid robot's hip adduction does not exceed the threshold of a human when walking. Finally, a simulation study is carried out to evaluate the effectiveness of the proposed method.Findings: This paper proposes a model reference adaptive control using state observer for balancing one leg of humanoid robot in stance phase that extends our previous research (Tran et al., 2021).Originality/value: The main research contents have been introduced.