Control strategies for crane systems: A comprehensive review

Motion-induced oscillations of crane payloads seriously degrade their effectiveness and safety. For the transport of a large object, the payload is modeled as a distributed-mass model, and is typically attached to the hook by four rigging cables. The manipulation task can be more challenging because of the payload swing toward the driving direction and the payload twisting about the rigging cables. The dynamics of bridge cranes transporting distributed-mass payloads are derived. A control scheme is designed for suppressing both the payload swing and twisting. Simulations of a large range of motions and various system parameters are used to analyze the dynamic behavior and the robustness of the control scheme. Experimental results obtained from a bridge crane transporting distributed-mass crate validate the simulated dynamic behavior and the effectiveness of the control scheme.

Control of Bridge Cranes With Distributed-Mass Payload Dynamics

In this paper, a novel online motion planning method for double-pendulum overhead cranes is proposed. The proposed trajectory is made up of two parts: anti-sway component and trolley positioning component. The objective of the first part is to suppress and eliminate the swings of hook and payload. Lyapunov techniques, LaSalle’s invariance theorem, and Barbalat’s lemma are used to demonstrate that the trolley can be driven to arrive at the desired position, while the unexpected hook swing and payload swing are damped out simultaneously with the proposed trajectory. Numerical simulation results indicate that the proposed control trajectory achieves superior performance and admits strong robustness against parameter variations and external disturbances.

A novel online motion planning method for double-pendulum overhead cranes

Abstract We introduce a control strategy to solve the regulation control problem, from the perspective of trajectory planning, for an uncertain 3D overhead crane. The proposed solution was developed based on an adaptive control approach that takes advantage of the passivity properties found in this kind of systems. We use a trajectory planning approach to preserve the accelerations and velocities inside of realistic ranges, to maintaining the payload movements as close as possible to the origin. To this end, we carefully chose a suitable S-curve based on the Bezier spline, which allows us to efficiently handle the load translation problem, considerably reducing the load oscillations. To perform the convergence analysis, we applied the traditional Lyapunov theory, together with Barbalat’s lemma. We assess the effectiveness of our control strategy with convincing numerical simulations.

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A trajectory planning based controller to regulate an uncertain 3D overhead crane system

In this paper, a Fuzzy based Linear Quadratic Regulator (FLQR) and Linear Quadratic Gaussian (FLQG) controllers are developed for stability control of a Double Link Rotary Inverted Pendulum (DLRIP) system. The aim of this work is to study dynamic performance analysis of both FLQR and FLQG controllers and to compare them with the classical LQR and LQG controllers, respectively. A dynamic mechanical simulation model of the DLRIP was obtained using both the numerically SimMechanics toolbox in MATLAB and the analytically dynamic nonlinear equations. To determine the control performance of the controllers, Settling Time (Ts), Peak Overshoot (PO), Steady-State Error (Ess), and the total Root Mean Squared Errors (RMSEs) of the joint positions are computed. Furthermore, the dynamic responses of the controllers were compared based on robustness analysis under internal and external disturbances. To show the control performance of the controllers, several simulations were conducted. Based on the comparative results, the dynamic responses of both FLQR and FLQG controllers produce much better results than the dynamic responses of the classical LQR and LQG controllers, respectively. Moreover, the robustness results indicate that the FLQR and FLQG controllers under the internal and external disturbances were effective.

Development of a Fuzzy-LQR and Fuzzy-LQG stability control for a double link rotary inverted pendulum

Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection

This paper presents three disease diagnosis systems using pattern recognition based on genetic algorithm and neural networks. All systems deal with feature selection and classification. Genetic algorithm chooses subsets of features for the input of the classifier (neural network) and the accuracy of the classifier determine the percentage of effectiveness of each subsets of features. The classifiers using in this paper are general regression neural network (GRNN), radial basis function (RBF) and radial basis network exact fit (RBEF). We use breast cancer and hepatitis disease datasets taken from UCI machine learning database as medical dataset. The system performances are estimated by classification accuracy and they are compared with similar methods without feature selection.

http://www.wseas.us/e-library/conferences/2011/Florence/AIASABEBI/AIASABEBI-03.pdf

Automatic disease diagnosis systems using pattern recognition based genetic algorithm and neural networks

A new linear model for active loads in islanded inverter-based microgrids

In this paper, the modeling, simulation and control of 3 degrees of freedom articulated robotic manipulator have been studied. First, we extracted kinematics and dynamics equations of the mentioned manipulator by using the Lagrange method. In order to validate the analytical model of the manipulator we compared the model simulated in the simulation environment of Matlab with the model was simulated with the SimMechanics toolbox. A sample path has been designed for analyzing the tracking subject. The system has been linearized with feedback linearization and then a PID controller was applied to track a reference trajectory. Finally, the control results have been compared with a nonlinear PID controller.

Modeling, Simulation and Position Control of 3DOF Articulated Manipulator

Due to high death rate in women with breast cancer, the detection will play a major role in the treatment of this type of cancer. Therefore, the early detection of breast cancer will increase the patients' chances of survival. The main tendency in feature extraction has been illustrating the data in a lower dimensional and different feature space, for instance, using principal component analysis (PCA). In this paper, we argue that feature selection depend on top of eigenvalue certainly is not proper because they may not encode useful information for classilcation purposes, features should be selected form all the components by feature selection methods. So, Genetic Algorithm (GA) is used in the most favorable selection of principal components instead of using classical method. We have applied PCA for dimension reduction, genetic algorithms for feature selection and support vector machines for classification. The estimate of this Algorithm has been done based on Wisconsin Breast Cancer Dataset (WBCD) which is commonly used among researchers who use machine learning methods for breast cancer diagnosis. The performance of this approach is given. In addition, the methods used in the past have been compared to the performance of the chosen approach. This approach affords optimal classification which is capable to minimize amount of features and maximize the accuracy sensitivity, specificity and receiver operating characteristic (ROC) curves. 10-fold cross-validation has been used on the classification phase. The average classification accuracy of the developed PCA+GA+SVM system is obtained 100% for a subset that contained two features. This is very favorable compared to the previously reported results.

Hassan Zarabadipour

Papers

Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection

Automatic disease diagnosis systems using pattern recognition based genetic algorithm and neural networks

A new linear model for active loads in islanded inverter-based microgrids

Modeling, Simulation and Position Control of 3DOF Articulated Manipulator

Feature subset selection and parameters optimization for support vector machine in breast cancer diagnosis