Comparative Analysis of Input Shaping Techniques for Sway Control of Nonlinear Crane System

This paper compares output based input shaping with a conventional command shaping in suppressing sway of a nonlinear crane system. The output-based input shaping filter is designed using the output signal of the target system while conventional input shaping filters are designed using the natural frequency and damping ratio of the system. Zero vibration (ZV), zero vibration derivative (ZVD), zero vibration derivative, derivatives (ZVDD) and zero vibration and triple derivative (ZVDDD) were designed and compared with the output based filter to investigate the performances and robustness of the filters. Level of sway reduction of the payload and time response analysis is used to assess the performance of the shapers. Simulation results showed that the output based filter has a better performance and it is more robust compared to the conventional input shapers and does not require model information of the system

Input shaping-based control schemes for a three dimensional gantry crane

The motion induced sway of oscillatory systems such as gantry cranes may decrease the efficiency of production lines. In this thesis, modelling and development of input shaping-based control schemes for a three dimensional (3D) lab-scaled gantry crane are proposed. Several input shaping schemes are investigated in open and closed-loop systems. The controller performances are investigated in terms of trolley position and sway responses of the 3D crane. Firstly, a new distributed Delay Zero Vibration (DZV) shaper is implemented and compared with Zero Vibration (ZV) shaper and Zero Vibration Derivative (ZVD) shaper. Simulation and experimental results show that all the shapers are able to reduce payload sway significantly while maintaining desired position response specifications. Robustness tests with ±20% error in natural frequency show that DZV shaper exhibits asymmetric robustness behaviour as compared to ZV and ZVD shapers. Secondly, as analytical technique could only provide good performance for linear systems, meta-heuristic based input shaper is proposed to reduce sway of a gantry crane which is a nonlinear system. The results show that designing meta-heuristic-based input shapers provides 30% to 50% improvement as compared to the analytical-based shapers. Subsequently, a particle swarm optimization based optimal performance control scheme is developed in closed-loop system. Simulation and experimental results demonstrate that the controller gives zero overshoot with 60% and 20% improvements in settling time and integrated absolute error value of position response respectively, as compared to a specific designed PID-PID anti swing controller for the lab-scaled gantry crane. It is found that crane control with changing cable length is still a problem to be solved. An adaptive input shaping control scheme that can adapt to variation of cable’s length is developed. Simulation with real crane dimensions and experimental results verify that the controller provides 50% reduction in payload sway for different operational commands with hoisting as compared to the average travel length approach

ADVANCED ANTI-SWAY CONTROL FOR OVERHEAD CRANES

This particular project focuses on a complex system whose dynamics are not very well understood and hence control designs are not straightforward. The project deals with the control of industrial overhead cranes. The project has the potential of bringing many rewards to industries, which are concerned with optimising lifting equipment performance. Such a system will allow these industries to save time and consequently costs as the volume of loaded and unloaded goods increases. Part of this project is to model the system surrounding the crane system and then design a suitable algorithm for load anti-sway purposes. The objective of this project is to design and implement an intelligent based controller that can be used to assist a crane operator in the difficult parts of the operation. The designed controller should give the appropriate control signal to the crane system such that the time taken to reach the target position is minimised with a zero sway angle at the destination. Earlier part of the project consisting of analysing and improving if required the existing 3-D mathematical linear and non-linear crane models. Two different models have been investigated: one with a constant cable length and the other with a variable cable length. The implementation of the controller is based on Fuzzy Logic Control (FLC). Two types of FLC have been used and compared the Fixed FLC and the FLC based on Adaptive Neuro Fuzzy Inference System (ANFIS). Heuristic approaches have been used for tuning the Fixed FLC. Data obtained from the Fixed FLC are then used for training ANFIS FLC. The results prove that it is possible to model an off-line expert fuzzy logic controller for an overhead crane. The controller achieved satisfactorily results for a constant and a variable rope length with minimal tuning than the fixed fuzzy method. Proposals for further work are also briefly discussed

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

The bridge crane system is a typical under-actuated system that is widely used in production and life. Although various scholars have conducted extensive research on the bridge crane system in recent years, there are still many problems, such as the trajectory planning of the cart and the anti-sway control of the cargo. In order to tackle the problem of the anti-sway control of the cargo, a hierarchical global fast terminal sliding-mode control (H-GFTSMC) is developed in this work. First, the Lagrange equations are used to model the system dynamics. Then, an appropriate hierarchical global fast terminal sliding-mode controller is designed to achieve anti-sway control of the cargo, and it is proved that each sliding-mode surface is progressively stable. A series of simulations were implemented to verify the effectiveness of the control method. The simulation results show that the H-GFTSMC has better control performance compared with the proportional–integral–derivative control method. When changing the cable length or adding non-negligible noise to the system, the H-GFTSMC still has good robustness

Data driven neuroendocrine pid controller for mimo plants based adaptive safe experimentation dynamics algorithm

This study focused on data-driven tools and controller structure in the data-driven control scheme. Data-driven tools are an optimization method to find the optimal controller parameters using the system’s input and output data. Meanwhile, the controller structure refers to the controller design that is highly dependent on the input and output system. The existing data-driven neuroendocrine-PID (NEPID) utilizes the simultaneous perturbation stochastic approximation (SPSA) algorithm as the data-driven tool. However, this SPSA-based method is unable to find the optimal value of the design parameter due to unstable convergence obtained that degrades the controller performance in MIMO systems. Thus, a safe experimentation dynamics (SED) algorithm is selected to solve this unstable convergence but still not enough to achieve high accuracy because the update designed parameter only depends on the fixed step size gain. For the controller structure, the hormone secretion rate parameter of the existing NEPID is constant during the experimental time. However, control accuracy is insufficient because the secretion rate and control variable error are not able to interact directly and limits the controller capability. Besides, in the existing NEPID controller structure of the SISO system, only a single node of hormone regulation is used due to a single control variable. Meanwhile, in the MIMO systems, many control variables available that interact with each other, and the single node hormone regulation of NEPID is still inadequate in producing better control accuracy of nonlinear MIMO systems. Therefore, this study proposed the adaptive safe experimentation dynamics (ASED) algorithm to improve the SED algorithm performance accuracy by minimizing its objective function in terms of mean, best, worst, and standard deviation analysis. In order to increase the control accuracy of the existing NEPID controller, this study also established the sigmoid-based secretion rate neuroendocrine- PID (SbSR-NEPID) controller structure by varying the hormone secretion rate according to the change of error. Finally, this study also focused on developing a multiple node hormone regulation neuroendocrine-PID (MnHR–NEPID) controller structure to improve the control accuracy of existing NEPID by prioritizing the control regulation of each node from their level of error. The performance of PID and NEPID controllers was compared with those of SbSR-NEPID and MnHR-NEPID performances based on error and input tracking. The results show that the ASED- and SED-based methods produced stable convergence. The ASED-based method provided better tracking performance than the SED method by obtaining the objective function’s lower values. Besides, from the simulation work, the SbSR-NEPID and MnHR-NEPID designs provided better control accuracy in terms of lower objective function, total norm of error, and total norm of input compared to those of the PID and NEPID controllers. Moreover, the SbSR-NEPID controller achieved control accuracy improvement of 4.95% and 5.89% for the container gantry crane and TRMS systems, respectively. Besides, the MnHR-NEPID controller achieved control accuracy improvement of 5.7% and 5.1% for the container gantry crane and TRMS systems, respectively. The ASED-based method significantly improved the SED method’s accuracy by using adaptive terms based on changing the objective function in the updated procedure. Besides, the SbSR-NEPID was effective in reducing the error in a transient state, and MnHR-NEPID provided effective interaction between nodes available in MIMO systems which contributed to accuracy improvement

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Safe Experimentation Dynamics Algorithm for Identification of Cupping Suction Based on the Nonlinear Hammerstein Model

The use of cupping therapy for various health benefits has increased in popularity recently. Potential advantages of cupping therapy include pain reduction, increased circulation, relaxation, and skin health. The increased blood flow makes it easier to supply nutrients and oxygen to the tissues, promoting healing. Nevertheless, the effectiveness of this technique greatly depends on the negative pressure's ability to create the desired suction effect on the skin. This research paper suggests a method to detect the cupping suction model by employing the Hammerstein model and utilizing the Safe Experimentation Dynamics (SED) algorithm. The problem is that the cupping suction system experiences pressure leaks and is difficult to control. Although, stabilizing the suction pressure and developing an effective controller requires an accurate model. The research contribution lies in utilizing the SED algorithm to tune the parameters of the Hammerstein model specifically for the cupping suction system and figure out the real system with a continuous-time transfer function. The experimental data collected for cupping therapy exhibited nonlinearity attributed to the complex dynamics of the system, presenting challenges in developing a Hammerstein model. This work used a nonlinear model to study the cupping suction system. Input and output data were collected from the differential pressure sensor for 20 minutes, sampling every 0.1 seconds. The single-agent method SED has limited exploration capabilities for finding optimum value but excels in exploitation. To address this limitation, incorporating initial values leads to improved performance and a better match with the real experimental observations. Experimentation was conducted to find the best model parameters for the desired suction pressure. The therapy can be administered with greater precision and efficacy by accurately identifying the suction pressure. Overall, this research represents a promising development in cupping therapy. In particular, it has been demonstrated that the proposed nonlinear Hammerstein models improve accuracy by 84.34% through the tuning SED algorithm

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

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of bridge cranes based on its online physical states and control model. Due to the swing of the payload, the safe braking distance cannot be a constant value. Therefore, we here propose a model prediction control (MPC)-based anti-swing method for non-zero initial states, where a new reference trajectory and a new cost function for optimization are proposed, such that the proposed MPC method can control the crane to follow the proposed reference trajectory and achieve a stable stop state with anti-swing. Furthermore, an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance achieved by using the proposed MPC braking control method. In this way, we can predict how far the crane would require to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. Experiments using both a simulated crane and a real crane demonstrate that the proposed safe braking distance prediction method is effective for safe braking control of the bridge cranes

Review on auto-depth control system for an unmanned underwater remotely operated vehicle (ROV) using intelligent controller

This paper presents a review of auto-depth control system for an Unmanned Underwater Remotely operated Vehicle (ROV), focusing on the Artificial Intelligent Controller Techniques. Specifically, Fuzzy Logic Controller (FLC) is utilized in auto-depth control system for the ROV. This review covered recently published documents for auto-depth control of an Unmanned Underwater Vehicle (UUV). This paper also describes the control issues in UUV especially for the ROV, which has inspired the authors to develop a new technique for auto-depth control of the ROV, called the SIFLC. This technique was the outcome of an investigation and tuning of two parameters, namely the break point and slope for the piecewise linear or slope for the linear approximation. Hardware comparison of the same concepts of ROV design was also discussed. The ROV design is for smallscale, open frame and lower speed. The review on auto-depth control system for ROV, provides insights for readers to design new techniques and algorithms for auto-depth control