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

LMI based antiswing adaptive controller for uncertain overhead cranes

This paper proposes an adaptive anti-sway controller for uncertain overhead cranes. The state-space model of the 2D overhead crane with the system parameter uncertainties is shown firstly. Next, the adaptive controller which can adapt with the system uncertainties and input disturbances is established. The proposed controller has ability to move the trolley to the destination in short time and with small oscillation of the load despite the effect of the uncertainties and disturbances. Moreover, the controller has simple structure so it is easy to execute. Also, the stability of the closed-loop system is analytically proven. The proposed algorithm is verified by using Matlab/Simulink simulation tool. The simulation results show that the presented controller gives better performances (i.e., fast transient response, position tracking, and low swing angle) than the state feedback controller when there exist system parameter variations as well as input disturbances

Adaptive fuzzy observer based hierarchical sliding mode control for uncertain 2D overhead cranes

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. This paper proposes a new approach to robustly control a 2D under-actuated overhead crane system, where a payload is effectively transported to a destination in real time with small sway angles, given its inherent uncertainties such as actuator nonlinearities and external disturbances. The control law is proposed to be developed by the use of the robust hierarchical sliding mode control (HSMC) structure in which a second-level sliding surface is formulated by two first-level sliding surfaces drawn on both actuated and under-actuated outputs of the crane. The unknown and uncertain parameters of the proposed control scheme are then adaptively estimated by the fuzzy observer (FO), where the adaptation mechanism is derived from the Lyapunov theory. More importantly, stability of the proposed strategy is theoretically proved. Effectiveness of the proposed adaptive FO-based HSMC approach was extensively validated by implementing the algorithm in both synthetic simulations and real-life experiments, where the results obtained by our method are highly promising

Autonomous Crane Control (Anti-Swing Controller)

The main objective for this project is to design controller forthe3DCrane Model that helps to overcome the swinging phenomena during the movement of the crane. 3D Crane model is a simulation or a mini model of the real life autonomous gantry crane that industries, suchas portand factories, usesto carries heavy loads. Cranes behavior is similar to pendulum where movement and friction on the load will create a swinging effect on it. In these industries, swinging of the load will affected their productivity, efficiency and most importantly the safety. So by having a controller that have the ability to overcome the swinging effect, this will optimize the productivity, efficiency and also the safety. In designing the "anti-swing" controller, a lot of problems encounter especially when dealing with 3 direction non-linear models. To understand the 3D Crane Model's capability and ability also will take a lot of time. This project will require knowledge in all types of controllers since the best controller out of all the controllers are needed to be use. As for the first part of this project, a PID controller is selected. Then a Fuzzy Controller are designed to compare with PID Controller to see which has better accuracy and precision in reducing the crane's swinging effect

3D Crane Control

The objective of this project is to design controller for the 3D crane Model that helps to overcome the swinging of the load during the movement of the crane. This model is a mini model of the real life autonomous gantry crane that being used in the industries. The swing motion of the load happens because the behavior of the crane is similar to pendulum by the movement and friction on the load

Control strategy for automatic gantry crane systems: a practical and intelligent approach

The use of gantry crane systems for transporting payload is very common in building constructions. However, moving the payload using the crane is not an easy task especially when strict specifications on the swing angle and on the transfer time need to be satisfied. Various attempts in controlling gantry cranes system based on open- loop and closed-loop control systems were proposed. However, most of the proposed controllers were designed based on the model and parameter of the crane system. In general, modeling and parameter identifications are troublesome and time consuming task. To overcome this problem, in this paper, a practical and intelligent control method for automatic gantry crane is introduced and evaluated experimentally. The results show that the proposed method is not only effective for controlling the crane but also robust to parameter variation

An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

© 2019, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature. In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented by two actuated and un-actuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are highly difficult to determine. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising

Consistency of control performance in 3d overhead cranes under payload mass uncertainty

The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Controller design for gantry crane system using modified sine cosine optimization algorithm

The objective of this research paper is to design a control system to optimize the operating works of the gantry crane system. The dynamic model of the gantry crane system is derived in terms of trolley position and payload oscillation, which is highly nonlinear. The crane system should have the capability to transfer the material to destination end with desired speed along with reducing the load oscillation, obtain expected trolley position and preserving the safety. Proposed controlling method is based on the proportional-integral-derivative (PID) controller with a series differential compensator to control the swinging of the payload and the system trolley movement in order to perform the optimum utilization of the gantry crane.  Standard sine cosine optimization algorithm is one of the most-recent optimization techniques based on a stochastic algorithm was presented to tune the PID controller with a series differential compensator. Furthermore, the considered optimization algorithm is modified in order to overcome the inherent drawbacks and solve complex benchmark test functions and to find the optimal design's parameters of the proposed controller. The simulation results show that the modified sine cosine optimization algorithm has better global search performance and exhibits good computational robustness based on test functions. Moreover, the results of testing the gantry crane model reveal that the proposed controller with standard and modified algorithms is effective, feasible and robust in achieving the desired requirements