Development of controller and observer for 2D Crane System via State-space approach

This report actually presents researches and studies and progress that are being achieved for the chosen topic which is Development of controller and observer for 2D Crane Systems via State-space approach. This report contains an introduction and background studies about cranes and how does this topic it related to my studies as a final year & control system student all being represented in scope of studies section, in fact the main objectives of this research are getting the dynamic equation for the 2D crane systems and applies state-space approach to develop a controller and an observer for them. The dynamic equations for the systems are being obtained by using Euler-Langrange formulation for obtaining the state-space representation of the systems. Furthermore, control & observer canonical forms have been designed and then simulated using Matlab Simulink for testing the stability of the system before designing the controller and the observer for the syste

Development of controller and observer for 2D Crane System via State-space approach

This report actually presents researches and studies and progress that are being achieved for the chosen topic which is Development of controller and observer for 2D Crane Systems via State-space approach. This report contains an introduction and background studies about cranes and how does this topic it related to my studies as a final year & control system student all being represented in scope of studies section, in fact the main objectives of this research are getting the dynamic equation for the 2D crane systems and applies state-space approach to develop a controller and an observer for them. The dynamic equations for the systems are being obtained by using Euler-Langrange formulation for obtaining the state-space representation of the systems. Furthermore, control & observer canonical forms have been designed and then simulated using Matlab Simulink for testing the stability of the system before designing the controller and the observer for the syste

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

Application of constrained predictive control on a 3D crane system

This paper describes the SIMULINK implementation of a constrained predictive control algorithm based on quadratic programming and linear state space models, and its application to a laboratory-scale 3D crane system. The algorithm is compatible with Real Time. Windows Target and, in the case of the crane system, it can be executed with a sampling period of 0.01 s and a prediction horizon of up to 300 samples, using a linear state space model with 3 inputs, 5 outputs and 13 states