In this paper, an unmanned bicycle (UB) with a reaction wheel is designed, and a second-order mathematical model with uncertainty is established. In order to achieve excellent balancing performance of the UB system, an adaptive controller is designed, which is composed of nominal feedback control, compensating control using extreme learning machine observer and reaching control via integral terminal sliding mode (ITSM) and barrier function (BF)-based adaptive law. Owing to the features of BF-based ITSM (BFITSM), not only any uncertainty or disturbance upper bound is not needed any longer but also the finite-time convergence of the closed-loop system can be ensured with a predefined error bound. Moreover, the BF-based control gain can be adaptively adjusted according to the update of the lumped uncertainty such that the overestimation is removed. The stability analysis of the closed-loop system is given according to Lyapunov theory. Comparable experimental results on an actual UB are carried out to validate the superior balancing performance of the proposed controller.
When generating simultaneous joint movements of a humanoid with multiple degrees of freedom to replicate human-like movements, the approach of joint synergy can facilitate the generation of whole-body robotic movement with a reduced number of control inputs. However, the trade-off of minimizing control inputs and keeping characteristics of movements makes it difficult to improve movement performance in a simple control manner. In this paper, we introduce an approach by connecting and constraining these joints. It is inspired by the fascia network of the human body, which constrains the whole-body movements of a human. Compared to when only joint synergy is used, the effectiveness of the proposed method is verified by calculating the errors of joint positions of generated movements and human movements. The paper provides a detailed exploration of the proposed method, presenting simulation-experimental results that affirm its effectiveness in generated movements that closely resemble human movements. Furthermore, we provide one possible method on how these concepts can be implemented in actual robotic hardware, offering a pathway to improve movement control in humanoid robots within their mechanical limitations.
A negative pressure wall-climbing robot is a special robot for climbing vertical walls, which is widely used in construction, petrochemicals, nuclear energy, shipbuilding, and other industries. The mobility and adhesion of the wheel-track wall-climbing robot with steering-straight mode are significantly decreased on the cylindrical wall, especially during steering. The reason is that the suction chamber may separate from the wall and the required driving force for movement increases, during steering. In this paper, a negative pressure wall-climbing robot with omnidirectional movement mode is developed. By introducing a compliant adjusting suction mechanism and omni-belt wheels, an omnidirectional movement mode is formed instead of the steering-straight mode, and the performances of adhesion and mobility are improved. We establish the safety adhesion model for the robot on a cylindrical wall and obtain the safety adhesion forces. We designed and manufactured an experimental prototype based on the analysis. Experiments showed that the robot has the ability of full maneuverability in cylindrical walls.
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