Control of Overhead Crane by Fuzzy-Pid with Genetic Optimisation

Abstract

A fuzzy logic controller with the fuzzy knowledge base: scaling factors of the input/output variables, membership functions and the rules are optimized by the use of the genetic algorithms, is presented in this work, and its application in the highly nonlinear systems. The fuzzy structure is specified by a combination of the mixed Sugeno's and Mamdani's fuzzy reasoning. The mixed, binary-integer, coding is utilized to construct the chromosomes, which define the set of necessary prevailing parameters for the conception of the desired controller. This new controller stands out by a non standard gain (output scaling factor) which varies linearly with the fuzzy inputs. Under certain conditions, it becomes similar to the conventional PID controller with non-linearly variable coefficients. The results of simulation show, well, the efficiency of the proposed controller.

Adaptive Safe Distance Prediction Using MPC for Bridge Cranes Considering Anti-Swing in Dynamic Environment

“…By discretizing (7) and (8) which is the spatial state expression in the continuous temporal domain, we can obtain discrete state space equations as…”

“…The non-zero initial velocity state for braking can introduce strong residual vibrations of the load when the emergency stop is required. Although many works have been proposed to solve the stable control problem of the overhead crane, most of them assume the crane has zero initial velocity state, e.g., PID control [6], fuzzy control [7], [8], optimal control [9], sliding-mode control [10] - [12], model predictive control [13], [14], command shaping control [15] - [17]. To tackle non-zero initial velocity issue, Joaquim Maria Veciana et al designed control inputs by measuring the initial states using a feedback sensor and introducing an appropriate processing time delay in [5].…”

Hierarchical global fast terminal sliding‐mode control for a bridge travelling crane system

“…The traditional PID control method is relatively simple to implement and is currently the most widely used control method in practice. In [14][15][16][17], some improvements to traditional PID have been proposed. Soukkou et al [14] and Ko [15] combined the fuzzy control with the PID control and designed a fuzzy-PID controller.…”

“…In [14][15][16][17], some improvements to traditional PID have been proposed. Soukkou et al [14] and Ko [15] combined the fuzzy control with the PID control and designed a fuzzy-PID controller. Choi proposed a PID controller for the Lagrange system in [16].…”

Safe distance prediction for braking control of bridge cranes considering anti‐swing

“…When an emergency stop is required, the non-zero initial state of the crane can introduce strong residual vibrations to the payload, that is, velocity, swing angle or angular velocity are not zero. Although many works have been proposed to solve the stable control problem of the overhead crane, most of them assume that the cranes have zero initial states, for example, proportional-integral-derivative (PID) control, 8,9 fuzzy control, [10][11][12] optimal control, [13][14][15] sliding-mode control, [16][17][18][19] model predictive control (MPC), [20][21][22][23] trajectory planning control, 24,25 and command shaping control. [26][27][28] To handle the non-zero initial velocity issue, Joaquim Maria Veciana et al introduced a method that considered the initial states for the control inputs, which were obtained from a feedback sensor and this method could be processed with a reasonable time delay.…”