Vision-based control of a knuckle boom crane with online cable length estimation

A vision-based controller for a knuckle boom crane is presented. The controller is used to control the motion of the crane tip and at the same time compensate for payload oscillations. The oscillations of the payload are measured with three cameras that are fixed to the crane king and are used to track two spherical markers fixed to the payload cable. Based on color and size information, each camera identifies the image points corresponding to the markers. The payload angles are then determined using linear triangulation of the image points. An extended Kalman filter is used for estimation of payload angles and angular velocity. The length of the payload cable is also estimated using a least squares technique with projection. The crane is controlled by a linear cascade controller where the inner control loop is designed to damp out the pendulum oscillation, and the crane tip is controlled by the outer loop. The control variable of the controller is the commanded crane tip acceleration, which is converted to a velocity command using a velocity loop. The performance of the control system is studied experimentally using a scaled laboratory version of a knuckle boom crane

Advanced Discrete-Time Control Methods for Industrial Applications

This thesis focuses on developing advanced control methods for two industrial systems in discrete-time aiming to enhance their performance in delivering the control objectives as well as considering the practical aspects. The first part addresses wind power dispatch into the electricity network using a battery energy storage system (BESS). To manage the amount of energy sold to the electricity market, a novel control scheme is developed based on discrete-time model predictive control (MPC) to ensure the optimal operation of the BESS in the presence of practical constraints. The control scheme follows a decision policy to sell more energy at peak demand times and store it at off-peaks in compliance with the Australian National Electricity Market rules. The performance of the control system is assessed under different scenarios using actual wind farm and electricity price data in simulation environment. The second part considers the control of overhead crane systems for automatic operation. To achieve high-speed load transportation with high-precision and minimum load swings, a new modeling approach is developed based on independent joint control strategy which considers actuators as the main plant. The nonlinearities of overhead crane dynamics are treated as disturbances acting on each actuator. The resulting model enables us to estimate the unknown parameters of the system including coulomb friction constants. A novel load swing control is also designed based on passivity-based control to suppress load swings. Two discrete-time controllers are then developed based on MPC and state feedback control to track reference trajectories along with a feedforward control to compensate for disturbances using computed torque control and a novel disturbance observer. The practical results on an experimental overhead crane setup demonstrate the high performance of the designed control systems.Comment: PhD Thesis, 230 page

Robust Control of Crane with Perturbations

In the presence of persistent perturbations in both unactuated and actuated dynamics of crane systems, an observer-based robust control method is proposed, which achieves the objective of trolley positioning and cargo swing suppression. By dealing with the unactuated and unknown perturbation as an augmented state variable, the system dynamics are transformed into a quasi-chain-of-integrators form based on which a reduced-order augmented-state observer is established to recover the perturbations appearing in the unactuated dynamics. A novel sliding manifold is constructed to improve the robust performance of the control system, and a linear control law is presented to make the state variables stay on the manifold in the presence of perturbations in unactuated dynamics. A Lyapunov function candidate is constructed, and the entire closed-loop system is proved rigorously to be exponentially stable at the equilibrium point. The effectiveness and robustness of the proposed observer-based robust controller are verified by numerical simulation results

Vision-Based Control of the Overhead Crane

Through the years, cranes have been used extensively for many different tasks. For example construction work, shipbuilding and cargo transportation. One of the tedious tasks that cranes perform is loading and unloading containers. This is a process which can be optimized with the aid of control theory. While moving cargo, the crane operator must make sure that crane motion does not cause the cargo to accelerate too quickly, as the cargo may then start to oscillate. Such motion may disrupt unsecured cargo and put unnecessary stress on secured cargo. Today the crane movement is directly controlled by the crane operator; this however severely limits the crane speed due to human inability to control the crane optimally. This thesis presents and investigates a method of controlling the crane movement. The method utilizes computer vision to determine cargo position which is used to control crane movement optimally

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

Integrated sensing, dynamics and control of a moble gantry crane

This thesis investigates the dynamics and control of a Rubber Tyred Gantry (RTG) crane which is commonly used in container handling operations. Both theoretical and experimental work has been undertaken to ensure the balance of this research. The concept of a Global Sensing System (GSS) is outlined, this being a closed loop automatic sensing system capable of guiding the lifting gear (spreader) to the location of the target container by using feedback signals from the crane's degrees of freedom. To acquire the crucial data for the coordinates and orientation of the swinging spreader a novel visual sensing system (VSS) is proposed. In addition algorithms used in the VSS for seeking the central coordinates of the clustered pixels from the digitised images are also developed. In order to investigate the feasibility of different control strategies in practice, a scaleddown, 1/8 full size, experimental crane rig has been constructed with a new level of functionality in that the spreader in this rig is equipped with multiple cables to emulate the characteristics of a full-size RTG crane. A Crane Application Programming Interface (CAPI) is proposed to reduce the complexity and difficulty in integrating the control software and hardware. It provides a relatively user-friendly environment in which the end-user can focus on implementing the more fundamental issues of control strategies, rather than spending significant amounts of time in low-level devicedependent programming. A control strategy using Feedback Linearization Control (FLC) is investigated. This can handle significant non-linearity in the dynamics of the RTG crane. Simulation results are provided, and so by means of the CAPI this controller is available for direct control of the experimental crane rig. The final part of the thesis is an integration of the analyses of the different subjects, and shows the feasibility of real-time implementation

Proceedings of the 5th Baltic Mechatronics Symposium - Espoo April 17, 2020

The Baltic Mechatronics Symposium is annual symposium with the objective to provide a forum for young scientists from Baltic countries to exchange knowledge, experience, results and information in large variety of fields in mechatronics. The symposium was organized in co-operation with Taltech and Aalto University. Due to Coronavirus COVID-19 the symposium was organized as a virtual conference. The content of the proceedings1. Monitoring Cleanliness of Public Transportation with Computer Vision2. Device for Bending and Cutting Coaxial Wires for Cryostat in Quantum Computing3. Inertial Measurement Method and Application for Bowling Performance Metrics4. Mechatronics Escape Room5. Hardware-In-the-Loop Test Setup for Tuning Semi-Active Hydraulic Suspension Systems6. Newtonian Telescope Design for Stand-off Laser Induced Breakdown Spectroscopy7. Simulation and Testing of Temperature Behavior in Flat Type Linear Motor Carrier8. Powder Removal Device for Metal Additive Manufacturing9. Self-Leveling Spreader Beam for Adjusting the Orientation of an Overhead Crane Loa

Application of neural network predictive control methods to solve the shipping container sway control problem in quay cranes

Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.Web of Science9782657825