This paper aims to explain the design of a novel time-varying sliding mode control of variable parameter (VP-TVSMC), which can effectively solve the anti-swing and positioning problem of distributed-mass double pendulum bridge crane system with its quick responsiveness and strong robustness to external interference. More specifically, this model initiates with the establishment of the dynamic equation of double pendulum crane model based on distributed-mass, then followed by the design of a time-varying parameter to realize the dynamic adjustment of the sliding mode surface and enhance the adjustment ability of the sliding mode surface, which is conducive to the global robustness of the double pendulum crane system under VP-TVSMC. With Lyapunov method and LaSalle's invariance principle, the asymptotic stability of the system can be sufficiently proved. Finally, the adoption of three kinds of external interference signals and uncertain system parameters successfully verified the preeminent control performance and global robustness against external interference of the proposed controller. The simulation results indicate that compared with the conventional CSMC, the proposed control method can reduce the driving force of the trolley, ensure the rapid and precise positioning of the trolley, as well as restrain the load swing angle within 5° in an effective manner. In addition, compared with the symbolic function sgn(S), the designed continuous function th(S) possesses a better anti-chattering effect, thus strengthening of the control performance of VP-TVSMC.INDEX TERMS Bridge crane, distributed-mass, time-varying sliding mode control, LaSalle's invariance principle, asymptotic stability, global robustness.
A wide application of sliding mode variable structure control as a nonlinear robust control method, has been witnessed in anti-swing positioning control of bridge crane system. Aiming at the problem that the sliding mode variable structure control system of bridge crane is not robust in approaching process, a new Global-Equivalent Sliding Mode Controller (GESMC) based on bridge crane system is proposed. This controller can realize the anti-sway positioning control of the bridge crane system under the condition of uncertain model parameters and external disturbance. The proposed controller, different from the traditional sliding mode control, excels in improving system robustness through keeping the system states in the sliding surface during the whole response process. Specifically, it initiates with the design of a global sliding surface, which can eliminate the sliding mode approach process of the system and achieve global robustness in the system. Afterwards, a new switching function combined with the equivalent sliding mode control method is incorporated to effectively reduce the chattering generated when the system reaches the sliding mode manifold. Its asymptotic stability is proven without a priori knowledge on the bounds of unknown disturbances by using the Lyapunov stability theory. Lastly, the simulation conducted verifies the effectiveness and robustness of the GESMC proposed in this paper and meanwhile demonstrates a comparatively favorable performance for the GESMC in reducing chattering.INDEX TERMS Bridge crane, Anti-swing positioning control, Global-equivalent sliding mode control, Global robustness, chattering.
In this study, an improved African vulture optimization algorithm (IAVOA) that combines the African vulture optimization algorithm (AVOA) with both quasi-oppositional learning and differential evolution is proposed to address specific drawbacks of the AVOA, including low population diversity, bad development capability, and unbalanced exploration and development capabilities. The improved algorithm has three parts. First, quasi-oppositional learning is introduced in the population initialization and exploration stages to improve population diversity. Second, a differential evolution operator is introduced in the local search position update of each population to improve exploration capability. Third, adaptive parameters are introduced to the differential evolution operator, thus balancing the algorithm exploration and development. A numerical simulation experiment based on 36 different types of benchmark functions showed that the IAVOA can enhance the convergence speed and solution accuracy of the basic AVOA and two variants of AVOA while exhibiting superior performance compared to those of other swarm intelligence algorithms.
During the transference of a ladle by the casting crane, the antiswing control of the ladle is particularly difficult due to liquid sloshing, so we designed a model based on a radial spring damper. During the ladle swings, the centrifugal force causes the spring damper to move radially, thereby generating a Coriolis force that inhibits the sloshing of the liquid. In addition, a sloshing analysis of the liquid in the ladle is carried out, and a double-pendulum casting crane model based on viscous damping and radial spring damper is established. On the basis of this model, the Enhanced Coupled Adaptive Sliding Mode Control (ECASMC) method is proposed. By introducing an enhanced coupling variable and constructing a new coupling deviation signal, we enhance the relationship among state quantities. Then, a new type of sliding surface is designed based on the enhanced coupling deviation signal, and adaptive technology is used to adjust the sliding mode parameters, which enhances the system's robustness for system parameter variations and external disturbances. Using LaSalle's invariance principle and Lyapunov theorem, we prove that the casting crane system is asymptotically stable near the desired equilibrium point. The simulation results verify the effectiveness and superior control performance of the proposed method even in the presence of uncertainties.
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