Controller design for gantry crane system using modified sine cosine optimization algorithm

Abstract

The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane. Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements.

New methods for proportional-integral controller design for time-delay systems

“…The value of 𝜔 𝑐 is the smallest value of 𝜔 at which the interception of the graphs of the left-and right-hand sides function of (8) against 𝜔 occurred. The PI stability region in the 𝑘 𝑝 -𝑘 𝑖 plane is the area bounded by the line produced by (5) for 𝜔 = 0 and the stability locus generated by ( 6) and (7) for 𝜔 = [0, 𝜔 𝑐 ].…”

“…Using (5), (6), and (7), the system stability region of Figure 3 was obtained. It can be seen in Figure 3 that system convex stability region is bounded by the real roots line generated by (5) and the stability locus produced by ( 6) and (7).…”

“…Therefore, designing a controller for this class of system requires full knowledge of how the system performance can be adversely influenced by time delay, and even be distabilised in worst case [6]. Among the innovations that contributes to the field of instrumentation and control in the 20 th century, proportional integral derivative (PID) controller was rated second [7]. PID controllers are commonly used in industrial applications [8] due to their popularity [9], simple algorithm [10]- [13], robustness [12], [14], [15], simple design methodology and effectiveness in system control [16].…”