A novel backstepping control scheme based on reinforcement fuzzy Q-learning is proposed for the control of container cranes. In this control scheme, the modified backstepping controller can handle the underactuated system of a container crane. Moreover, the gain of the modified backstepping controller is tuned by the reinforcement fuzzy Q-learning mechanism that can automatically search the optimal fuzzy rules to achieve a decrease in the value of the Lyapunov function. The effectiveness of the applied control scheme was verified by a simulation in Matlab, and the performance was also compared with the conventional sliding mode controller aimed at container cranes. The simulation results indicated that the used control scheme could achieve satisfactory performance for step-signal tracking with an uncertain lope length.
Aiming at robotic manipulators subject to system uncertainty and external disturbance, this paper presents a novel adaptive control scheme that uses the time delay estimation (TED) technique and reinforcement learning (RL) technique to achieve a good tracking performance for each joint of a manipulator. Compared to conventional controllers, the proposed control scheme can not only handle the system parametric uncertainty and external disturbance but also guarantee both the angular positions and angular velocities of each joint without exceeding their preset constraints. Moreover, it has been proved by using Lyapunov theory that the tracking errors are uniformly ultimately bounded (UUB) with a small bound related to the parameters of the controller. Additionally, an innovative RL-based auxiliary term in the proposed controller further minimizes the steady state tracking errors, and thereby the tracking accuracy is not compromised by the lack of asymptotic convergence of tracking errors. Finally, the simulation results validate the effectiveness of the proposed control scheme.
In a high-temperature, high-flame-velocity, and high-pressure gas corrosion environment, the intercolumnar pores and gaps of electron beam–physical vapor deposition (EB-PVD) thermal barrier coatings (TBCs) may serve as infiltration channels for molten calcium–magnesium–alumino–silicate (CMAS), leading to the severe degradation of TBCs. In order to clarify the relationship between the roughness of the bond coat and the CMAS corrosion resistance of the EB-PVD TBCs, 7 wt.% yttria-stabilized zirconia (7YSZ) TBCs were prepared on the surfaces of four different roughness-treated bond coats. The effect of the bond coat roughness on the columnar microstructure of the EB-PVD YSZ was investigated. The effect of the change of the bond coat’s microstructure on the CMAS corrosion resistance of the EB-PVD YSZ was studied in detail. The results showed that the reduction in the roughness of the bond coat contributes to the improved formation of the EB-PVD YSZ columns. The small and dense columns are similar to a lotus leaf-like structure, which could reduce the wettability of CMAS and minimize the spread area between the coating and the CMAS melt. Thus, the CMAS corrosion resistance of the coating can be greatly improved. This preparation process also provides a reference for the preparation of other TBC materials, improving the resistance to CMAS hot corrosion.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.