Masood Askari

Model predictive control of an inverted pendulum

In this paper, model predictive control is applied to an inverted pendulum apparatus and the effect of input disturbance are studied. The optimization problem is solved on-line using quadratic programming approach on a PC hardware platform.

Multivariable Offset-Free Model Predictive Control for Quadruple Tanks System

The design and implementation of a robust multivariable model predictive control (MPC) on a quadruple tanks system (QTS) is addressed in this paper. Mismatch between the MPCs model and the process may cause constraint violation, nonoptimized performance, and even instability. It is the objective of this paper to offset-free control the process in the presence of constraints and model mismatch. It is shown in this paper how this model mismatch is compensated by augmented state disturbances, and also how the steady-state error is eliminated. In the proposed method, an observer is designed to estimate the disturbances and states. The results show how the proposed control method increases the robustness of the model predictive controller in simulation and in real-time implementations on a new QTS proposed in this work together with techniques designed to identify the parameters of this novel plant.

Stability of Soft-Constrained Finite Horizon Model Predictive Control

This paper addresses the stability of soft-constrained model predictive control (MPC). It is shown that the infinite horizon soft-constrained MPC problem can be solved as a finite horizon soft-constrained MPC problem if the prediction horizon is greater than an upper bound. The contribution of this paper is a procedure to compute the prediction horizon upper bound, which guarantees the stability. The proposed technique is verified using two simulation examples. The second example (inverted pendulum) is verified through practical implementation.

Parameter tuning of model predictive controller; a case study

This paper presents parameter tuning of model predictive controller. The effect of prediction horizon on the stability of the system is studied. The performances of model predictive controller and linear quadratic regulator on the inverted pendulum are compared.

Hysteresis parameter identification of hybrid dynamical systems

This paper discusses the identification of a class of hybrid dynamical systems whose discrete states have hysteresis phenomenon. The identification model is piecewise affine model along with hysteresis switching law. The first step of the proposed identification method is the estimation of the local parameter vectors for small neighbourhood of each measured data point. Then the local parameter vectors are clustered. Finally, the threshold levels of the hysteresis function which defines the discrete state are estimated.

Nonlinear system identification by fuzzy piecewise affine models

In this paper, a new identification method of a piecewise affine model for a nonlinear system based on input-output data measurements is presented. In particular the identification of piecewise affine models of nonlinear single-input-single-output systems through Takagi-Sugeno models is considered. The basic idea in this paper is to decompose the nonlinear system into a set of piecewise affine systems. First, the least mean square method is used to identify the system in the neighborhood of each data point. Then the obtained parameter vectors are classified into groups. The center point of each group is considered as the parameter vector of the corresponding submodel. Groups are considered as fuzzy sets and their membership functions values at each data point is calculated using the distance between the parameter vector, which corresponds to the data point, and the center point. Using interpolation, the value of each membership function can be calculated at all points. Finally, the estimated output is obtained by Takagi-Sugeno fuzzy inference.