Tuning Function-Based Light Computational Adaptive Fixed-Time Control for Overhead Cranes With Multiple Uncertainites

Overhead cranes are important transportation equipments in practice, however, their existing control methods have encountered many difficulties in applications due to the underactuation, input limitation and computation complexity. This paper proposes an adaptive fixed-time control scheme for the underactuated overhead crane with multiple uncertainties to deal with the above challenges simultaneously. A coordinate change is employed to address the underactuated structure by reformulating the crane dynamics as a strict-feedback system. A series of time-varying tuning functions are designed to guarantee the input signal varies within a small range to meet the practical input requirement of the overhead crane system. Moreover, a second-order nonlinear tracking differentiator (NLTD) is set up to avoid the repetitive derivative calculation of the virtual controllers. Then, an adaptive law is designed to tackle multiple uncertainties with no need of introducing any other control algorithms but only the single one of itself. Further, a light computational adaptive fixed-time control scheme is proposed by consisting of the tuning functions, NLTD, and the adaption law to achieve a fast location of the overhead crane system. The simulation experiments illustrate the effectiveness of the presented method. Note to Practitioners—This work is to develop an adaptive fixed-time control method for a kind of overhead cranes, which is of important significance in the field of ocean engineering. The time-varying tuning function is proposed to guarantee an even start and a desired swing range of the crane, which is necessary in practice. Furthermore, a second-order nonlinear tracking differentiator (NLTD) is built to eliminate the repetitive derivative of the virtual control signals. Only the single adaptive law is designed to deal with multiple uncertainties with no need of introducing any other control techniques. Therefore, the presented adaptive fixed-time control algorithm has a light computation burden, which is more suitable for practical applications. The designed scheme can achieve a fast location of the overhead crane system, which saves the control cost to some extent. Moreover, the light computation method is also suitable for the platform with weak computing ability. The elementary experiments are carried out based on the self-built crane hardware test platform. In the future, the designed control scheme will be further applied to an industrial overhead crane system to enhance the efficiency of the presented algorithm.