2 research outputs found
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
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