Abstract
Abstract Rotary cranes (tower cranes) are common industrial structures that are used in building construction, factories, and harbors. These cranes are usually operated manually. With the size of these cranes becoming larger and the motion expected to be faster, the process of controlling them became difficult without using automatic control methods. In general, the movement of cranes has no prescribed path. Cranes have to be run under different operating conditions, which makes closed-loop control preferable. In this work a fuzzy logic controller is introduced with the idea of split-horizon; that is, fuzzy inference engines (FIE) are used for tracking the position and others are used for damping the load oscillations. The controller consists of two independent controllers: radial and rotational. Each of these controllers has two fuzzy inference engines (FTEs). Computer simulations are used to verify the performance of the controller. Three simulation cases are introduced: radial, compound, and damping. The results from the simulations show that the fuzzy controller is capable of keeping the load-oscillation angles small throughout the maneuvers while completing them in a relatively reasonable time.
Highlights
The crane can be considered as one of the most important tools used in industry to transfer loads and cargo from one spot to another
And Trabia [9] applied a distributed fuzzy logic controller to a bidirectional gantry crane. They introduced the idea of using two separate fuzzy controllers each of which has two fuzzy inference engines: one to track the desired position commanded by the operator and another to correct for payload oscillations
We extend this work to rotary cranes in which both the trolley and jib are allowed to move
Summary
The crane can be considered as one of the most important tools used in industry to transfer loads and cargo from one spot to another. Even though experimental results showed reduced load pendulations throughout the travel, a delay of up to 2.5 seconds occurred between the operator input and the actual input from the filter to the cranes. And Trabia [9] applied a distributed fuzzy logic controller to a bidirectional gantry crane They introduced the idea of using two separate fuzzy controllers each of which has two fuzzy inference engines: one to track the desired position commanded by the operator and another to correct for payload oscillations. The controller was used to drive the crane along a path generated by an input-shaping strategy They obtained good results for damping the oscillations and at the same time reducing the payload travel time. We extend this work to rotary cranes in which both the trolley and jib are allowed to move
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