Fuzzy predictive control of three-tank system based on a modeling framework of hybrid systems

Abstract

In this article, a fuzzy predictive control scheme is proposed for controlling liquid level in a three-tank system in the presence of disturbances and uncertainties. The three-tank system is considered as a hybrid system, and a hybrid model of the system is obtained using the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized based on a piecewise affine linear method. Then, a model predictive control is designed based on the hybrid model and applied to the three-tank system. Although the performance of the model predictive control method is satisfactory in normal condition, it suffers from the problem of bias in output in the presence of disturbance and uncertainty. In this article, a fuzzy supervisor is utilized to adjust the main predictive controller such that the effects of disturbance and uncertainties are degraded by using zero-offset tracking. The proposed fuzzy predictive control scheme has advantages of simplicity and efficiency in normal operation, and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method.

Identification and control of nonlinear systems using PieceWise Auto-Regressive eXogenous models

“…In fact, we consider two examples of non linear systems. The first one is a simulated level control system that is frequently used in the nonlinear system literature (Kardos and Kutasi, 2017;Pan et al, 2005;Sarailoo et al, 2014;Wen et al, 2012) and the second is a Hammerstein system (Wang et al, 2014) implemented in real time using an electronic circuit connected to a computer by an interface card (Lassoued, 2015). Simulation example: Level control system…”

A computationally efficient torque and flux ripple reduction by active vector optimization approach using direct torque control–based model predictive torque control for matrix converter–fed permanent magnet synchronous motor

“…The MPC is an advanced and simple technique that effectively utilizes all the available MC voltage vectors including rotational vectors for generating the switching pulse with ease of implementation (Fang et al, 2021). The MPC algorithm has attained its reputation in controlling the drive application suitable for orthopaedic drilling (Ramya and Sivaprakasam, 2020), reduction of switching times in parallel hybrid electric vehicles (Fu et al, 2020), reduction of fuel consumption in the gas pipeline networks (Moetamedzadeh et al, 2019), controlling the nonlinear systems like evaporator system (Rajabi et al, 2014), three-wheeled omnidirectional mobile robot (Ren et al, 2021) and liquid level control of the three-tank system (Sarailoo et al, 2014). The uncertainties and disturbances associated in the controlling of unmanned aerial vehicles like quadrotor helicopters are eliminated by implementing a hybrid combination of MPC algorithm and fuzzy logic controlling model (Jalili et al, 2018).…”

A novel framework of model predictive control for controlling a class of nonlinear system in the presence of uncertainty

“…Contrary to classical control methods, MPC covers a large group of industrial systems such as multivariable systems and hybrid systems, considering constraints on state, input, or output variables. MPC is popular both in industry and academic research (Baocang, 2018; Camacho and Bordons, 2007; Fei et al, 2019; Sarailoo et al, 2014b; Wang et al, 2018). MPC is a control technique that is based on the receding horizon principle (RHP) in which at each sampling instant, an optimization problem is solved and this procedure is repeated for the next optimization step.…”