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

Advanced Discrete-Time Control Methods for Industrial Applications

In this thesis, we focus on developing advanced control methods for two industrial systems in discrete-time with the aim of enhancing their performance in delivering the control objectives as well as considering the practical aspects of the designs such as the nature of the industrial process, control configurations, and implementation. In the first part, the problem of dispatching wind power into the electricity network using a battery energy storage system (BESS) is addressed. To manage the amount of energy sold to the electricity market, a novel control scheme is developed based on discrete-time model predictive control to ensure the optimal operation of the BESS in the presence of practical system constraints. The control scheme follows a decision policy to sell more energy at peak demand times and store it at off-peak periods 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 the simulation environment. The second part of this thesis deals with the modeling and control of overhead crane systems for high-performance automatic operation. To be able to achieve high-speed load transportation with high precision in load positioning as well as minimizing load swings, a new modeling approach is developed based on independent joint control strategy which considers the system actuators as the main plant. The nonlinearities of the overhead crane dynamics are then treated as disturbances acting on each actuator. The resulting model enables us to estimate the unknown parameters of the system including coulomb friction constants thanks to its decoupled and linear-in-parameter form. To suppress load swings, a novel load swing control is designed based on passivity-based control. Two discrete-time controllers are then developed based on model predictive control and state feedback control to track the reference trajectories in conjunction with a feedforward control to compensate for the 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