Mathematical Problems in Engineering

Volume 2015, Article ID 307149, 8 pages

http://dx.doi.org/10.1155/2015/307149

## Time-Delay LPV System Control and Its Application in Chatter Suppression of the Milling Process

Shanghai University of Engineering Science, Shanghai 201620, China

Received 14 June 2014; Revised 2 November 2014; Accepted 29 November 2014

Academic Editor: George S. Dulikravich

Copyright © 2015 Dazhong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This paper proposed a methodology for the control of model following control system (MFCS) approach to time-delay linear parameter-varying (LPV) system. The method incorporates a state control law which makes the output error zero, the bounded property of the internal states for the control is given, and the utility of this control design is guaranteed. Numerical example is given to demonstrate the effectiveness and less conservativeness of the proposed methods. The proposed control methodology is demonstrated on chatter suppression in the milling process.

#### 1. Introduction

Time-delay is commonly encountered in various physical and engineering systems such as aircraft, biological systems, and networked control systems. In [1], the authors proposed processing short-term and long-term information with a combination of polynomial approximation techniques and time-delay neural networks. The paper [2] proposed adaptive digital predistortion of wireless power amplifiers/transmitters using dynamic real-valued focused time-delay line neural networks. Also, a new strategy for optimal control of continuous tandem cold metal rolling was designed in [3, 4] obtained robust power control of multilink single-sink optical networks with time-delays. Some other related results can refer to [5–9].

Stability analysis and control synthesis problems of linear parameter-varying (LPV) continuous-time systems where the state-space matrices depend on time-varying parameters, whose values are not known a priori but can be measured in real time, have received considerable attention recently [10–12]. Most of the abovementioned techniques have been applied to practical systems. Control designs for LPV systems such as missiles, aircrafts and spacecrafts, energy production systems, mechatronic systems, congestion in computer-networks, and web servers [13–16] have been investigated. The approach of using LPV models has computational advantages over other controller synthesis methods for nonlinear systems because the formulation is close to the linear system counterpart. The approach also comes with a theoretical validity, since it can be shown that the closed loop system meets certain specifications.

Model following control system (MFCS) methods have received considerable attention over the last decades. The paper [17] deals with robustness to plant parameter perturbations and sensitivity to disturbances of two-loop control structures containing a model of the controlled plant and two PID controllers. In [18], a robust model following control law based on hyper stability theory will be derived for uncertain systems with input nonlinearities.

Design of MFCS for nonlinear neutral system with time-delays and disturbances is discussed; it was proposed by Okubo [19] for a family of plants with separable linear and nonlinear part. In previous studies, a method of linear MFCS for time-delays system was proposed by [20]. These methods are developed to make the ultimate bounded of the MFCS error arbitrarily small or guarantee that MFCS error decreases asymptotically to zero.

The paper [21] has proposed a study of the model following control in which the considered system had linear time-delay. Despite the fact that the method with milling vibration control has already been verified, the considered model is not typical. So, the MFCS uses directly time-delay for an input time-delay system and the system can be transformed into a linear time invariant one by using a variable transformation. Thus, it is easy to design controllers with enough stability and performance based on MFCS theory. The objective of this paper is to design a MFCS scheme for time-delay LPV system whose state-space matrices depend affinely on a set of time-delay parameters that is bounded. The proposed control methodology is demonstrated on chatter suppression in the milling process. This research is a prolongation of paper [21].

This paper is organized as follows. In Section 2, we formulate the time-delay LPV system with disturbances, give the reference model, and tackle the design of the MFCS for the time-delay LPV system. In Section 3, problem of the chatter control in the milling process is proposed. In Section 4, some preliminary numerical simulations and experiment are reported. Finally, Section 5 concludes this paper.

#### 2. MFCS for Time-Delay LPV System

##### 2.1. The Problem Formulation

Consider the following time-delay LPV system: where , , and are constant matrices of appropriate dimensions. , , and are the system state vector, control input vector, and available states output vector of the system, respectively. and are bounded disturbances; are time-delays.

The reference model is given below, which is assumed to be controllable and observable [20]: Here, , , and are the state vector, input vector, and output vector of the reference model system, respectively, and , , and are constant matrices. Output error is given as

The aim of the control system design is to obtain a control law which makes the output error zero and keeps the internal states bounded.

##### 2.2. Design of the System for the Time-Delay

*Definition 1. *The time-delay factor *σ* is defined as [22]
Here, is defined on the continuous space . Let , , and .

Then, using , we can represent system (1) as
Here, and .

Without loss of generality, we make the following three assumptions [23].

*Assumption 2. * is controllable and is observable; that is, the following conditions are held:

*Assumption 3. *Zeros of are stable.

*Assumption 4 (). *It follows from (2) and (6) that

Furthermore, we have by using (3), (7), and (9),

Then, the representations of input-output equations are given as
where

Let
where and .

is fixed matrix of , as well as . Let be a scalar characteristic polynomial of disturbances. Disturbances and satisfy , . The first step of design is a monic and stable polynomial . Then, and can be obtained from
where the degree of each polynomial is , , , , and . And , , , , , and are polynomials. Let , ; is a polynomial matrix which is stable and . Then, the following form is obtained:

The control law can be obtained by making the right-hand side of (12) be equal to zero.

Thus,

For using no derivatives of signals in control input , we assume that , . The state-space expression of can be shown as follows:
where is outside signal. () is satisfying the following constraints [19]: , , and . Since is stable polynomial and is reference input, is bounded. The connections between the polynomial matrices and the system matrices are given as [22]

Therefore, of (17) is obtained from . The MFCS can be realized if the system internal states are bounded.

##### 2.3. The Bounded Property of Internal States

Let ; removing the from (6)~(7) and (18) and (19), the system is given by where

It should be pointed out that the internal state of (1) is equal to the one of (20). So the bounded property of internal states of (1) is turned out to be that is bounded. Characteristic polynomial of the system can be calculated as follows [24]:

From and into (22), we have

On the other hand, where is the zeros polynomial of (left coprime decomposition). So ; we have

Hence, we can conclude that is a stable system matrix due to the fact that , , , and are stable polynomials.

Lemma 5. *(1) If could be factorized into and contained polynomial and contained polynomial , that is, , then, at time when all and , roots of , satisfy , then was stable. While was stable, was stable either; (2) If could not be factorized into polynomial and polynomial , then Nyquist stability criterion should be adopted [20].*

Lemma 6. *For the controlled object (20), the rank of and the degree about of will be [22]
*

*From the above discussions, we have the main following result.*

*Theorem 7. With controlled system (1) and reference model (2) and (3), all the internal states are bounded and output error is asymptotically to converge to zero in the design of the MFCS for the time-delay LPV system with disturbances, if the following conditions are held.(1) is controllable and is observable, that is,(2).(3)Disturbances and are bounded.(4)Zeros of are stable.*

*3. Chatter Suppression in the Milling Process*

*3. Chatter Suppression in the Milling Process*

*3.1. The Time-Delay LPV System in the Milling Process*

*3.1. The Time-Delay LPV System in the Milling Process*

*In the milling process, the workpiece is clamped and fed to a rotating multitooth cutter. The geometry of the milling process is as shown in Figure 1 [25]. The cutter has two blades that are used to remove material from the workpiece. The force acting on the tool is a function of not only the current displacement of the tool but also the surface characteristics and hence the displacement at the previous tool pass. This induces a time-delay into the system. The force depends also on the angular position of the blade, which plays the role of the time-varying parameter. The state-space model of this system can be represented as follows [25, 26]:
where , and are the stiffness of the two springs, is the damping coefficient, and are the masses of the blade and the tool, and and are the displacements of the blade and the tool. The angle depends on the particular material and the tool used.*