Liquid Level Tracking Control of Three-tank Systems
- PDF / 907,108 Bytes
- 11 Pages / 594.77 x 793.026 pts Page_size
- 114 Downloads / 196 Views
ISSN:1598-6446 eISSN:2005-4092 http://www.springer.com/12555
Liquid Level Tracking Control of Three-tank Systems Shuyou Yu*, Xinghao Lu, Yu Zhou, Yangyang Feng, Ting Qu, and Hong Chen Abstract: In this paper, a liquid level tracking controller composed of a feedforward controller and a feedback controller is proposed for three-tank systems. Firstly, the flat property of three-tank systems is verified and a feedforward controller is designed accordingly to track the ideal trajectories. Secondly, in order to eliminate the tracking errors introduced by model uncertainties or unknown disturbances, a nonlinear model predictive controller is designed in which a terminal equality constraint is added for ensuring asymptotic convergence. In addition, an improved cuckoo search algorithm is adopted to solve the optimization problem involved in the nonlinear model predictive control. Finally, the control performance is confirmed by both simulation and experiment results. Keywords: Cuckoo search algorithm, flat system, liquid level tracking, model predictive control.
1.
INTRODUCTION
Liquid level control is important in modern process control since it can potentially improve product quality and enhance economic benefits [1]. Three-tank systems are typical multi-input multi-output (MIMO) systems with the features of strong coupling and nonlinearity, which make it of great research value in the study of liquid level control [2, 3]. Many efforts have been made to solve the liquid level tracking control problem. A neural network based PID controller is proposed in [4], which shows that the standard digital PID controller has faster response and a larger overshoot while the neural network based PID controller can achieve better performance with the price of a relatively slow response. The liquid level control problem of three-tank systems is described as the disturbance attenuation problem of constrained linear systems in [5], which can guarantee both the disturbance attenuation and the time-domain constraint satisfaction. Nonlinear model predictive control (NMPC) can deal with constraints of MIMO systems [6–9], and can achieve faster response without overshoot compared to PID controller [10–12]. However, its application has been limited due to the heavily computational burden [13,14]. A model predictive control scheme based on bees algorithm is proposed to reduce the computational burden in [15], however, the computational burden is still too heavy to implement. A RBF-ARX model-based predictive control strategy is proposed to reduce the computational burden by
locally linearizing the model at each time instant [16]. But the control accuracy is reduced inevitably due to the model error caused by linearization at each time instant. In order to enhance the tracking accuracy and avoid the model error caused by linearization, a controller with a feedforward control based on the property of flatness and a model predictive control is proposed in [17]. Through the ideal flat outputs, the ideal state trajectory and control input traject
Data Loading...
