Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 760490, 9 pages
http://dx.doi.org/10.1155/2015/760490

Research Article

Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

Xiuyan Peng,¹ Shuli Jia,¹ and Xingmei Wang²

¹College of Automation, Harbin Engineering University, Harbin 150001, China
²Computer Science and Technology College, Harbin Engineering University, Harbin 150001, China

Received 4 February 2015; Accepted 3 March 2015

Academic Editor: Xinggang Yan

Copyright © 2015 Xiuyan Peng 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.

A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in the -infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

1. Introduction

Currently, the study of nonlinear systems mainly includes feedback linearization method, backstepping, forwarding, and passivity method. Backstepping and passivity method apply for nonlinear systems with lower triangular structural features; forwarding method is suitable for systems with upper triangular structural features. These methods for nonlinear systems have a strict requirement in the system form. Feedback linearization method can turn the nonlinear system into a linear system, which is an effective solution to a nonlinear system control. However, analytic model predictive not only eliminates the restrictions of the nonlinear system form but also combines the advantages of predictive control and feedback linearization method; it can be seen as an optimized feedback linearization method applying predictive control thought. Compared with feedback linearization, it improves the control precision of the system. It is a control algorithm based on model optimization, with easy modeling, control performance, robustness, and simple logical structure and other characteristics, and has been widely used in recent years.

Literatures [1–3] propose the analytic model predictive control algorithm. In those papers, the predictive control in the literature requires that the controlled object is known, and the system uncertainties impose greater impact on predictive control. Thus, analytic model predictive control usually requires the controlled object to be precisely known. However, due to uncertainties of various disturbances, modelling errors, and so forth, in the actual project, we are often unable to obtain the accurate controlled plant model. Therefore, how to design a good control effect analytic model predictive control law in the case of uncertainties in system is crucial.

When the controlled object is known, many approaches are given by literatures [4–11] to solve this problem. But we hope to improve analytic model predictive control algorithm to complete control of the system, because the method can avoid the calculation of online optimization predictive control, thus saving the amount of computation and reducing the complexity of solving the problem. Considering that the fuzzy system has the ability to approximate the unknown nonlinear system function and uncertainties, applying the fuzzy system to control nonlinear uncertain systems has become a hot spot theory and engineering research and made a lot of research results (see [12–18]).

For solving the analytic model predictive control law which cannot be determined precisely because of system uncertainty, a fuzzy adaptive analytic model predictive control law is given in this paper based on adaptive fuzzy concept. Using fuzzy system to approximate the uncertainties in the controller, weights of fuzzy systems are based on system feedback error online adjustment, to make fuzzy system approach the unknown functions of controller. Secondly, considering the impacts of the fuzzy modeling errors on the system, the dynamic performance of the system is therefore reduced. A robust compensation term based on -infinity method is introduced to eliminate this influence. According to Lyapunov stability theory, the closed-loop system ultimately bounded stable is proved.

Moreover, in those papers, the solved problem is that the dimensions of output are equal to the dimensions of input. But there were many situations in practical engineering in which the dimensions on input and output of system were not equal. Therefore, Moore-Penrose inverse of matrix is proposed in this paper; it can make the design process of the controller simple.

Rudder roll stabilization system as an emerging antirolling method attracts extensive attention domestically and abroad. The ships’ motion environment and their movement characteristics show that it is difficult to get an accurate mathematical model. Model parameters uncertainty, unmodeled dynamics, and other issues increase the difficulty of determining rudder roll stabilization system control algorithm. The proposed method will be applied on rudder roll stabilization system; the simulation results show good effectiveness. The proposed method has important theoretical and practical value.

2. Problem for Mulation

A class of nonlinear system is given by

where , , , , , , and .

If there exist uncertainties in and , system (1) is a class of uncertain nonlinear systems.

The performance index adopted for system (1) is given by

where is predictive value of output in the is the predictive period, and is reference signal in the .

The following assumptions are imposed on nonlinear system (1) (see [2]).

(1)Each of the system outputs has the same relative degree and the zero dynamics are stable.(2)All states are available.(3)The output and the reference signal are sufficiently many times continuously differentiable with respect to .

If and are known, the control order of is selected . Similarly, the higher derivatives of the output , seeking order derivative, are denoted by :

where is the relative degree of system (1), is control order, and . Consider

where :

Within the moving time frame, the output and at the time is approximately predicted by

where

Take (7) and (8) into the performance function (2):

So the analytic model predictive control law of system (1) can be given by [1]

where

Because of , if , the control law can be improved as follows:

But if , does not exist, so (13) does not describe the control law of system (1).

In order to deal with this problem, we give a new definition.

Definition 1. When , ; is said to be the Moore-Penrose inverse of , if can satisfy the Penrose conditions (see [19]):

(i);(ii);(iii);(iv),

where means conjugate transpose of matrix.
So the control law can be improved as follows when :

where is defined as Moore-Penrose inverse of and formula (14) is the minimum norm least-squares solution of formula (11) (see [19], for minimal norm least-squares solution).

Remark 2. If the Moore-Penrose inverse of is used for formula (14), it can solve the problems of nonlinear system that the dimension of input and output cannot be necessarily equal. This is an expansion [1] in this paper, and it is more favorable for engineering applications.

Remark 3. When the Moore-Penrose inverse is used for formula (14), minimal norm least-squares solution for formula (11) can be obtained; it means that formula (14) is the minimum norm solution of (11), when is compatibility linear equation; if is incompatible linear equation, formula (14) is the least-squares solution of formula (11).

are elements of the front row of the matrix :

By the analytic model predictive control law (14) and its calculation, in order to get the control law of system (1), the and are known; otherwise and of predictive control law equation (14) cannot be accurately determined. When and are unknown, the controller will be unable to accurately calculate, so control performance will be affected. To solve this problem, we propose a fuzzy adaptive analytical model predictive controller.

3. Controller Design

3.1. Fuzzy Adaptive Analytical Model Predictive Controller Design

Fuzzy controller uses IF-THEN rules of fuzzy logic system, which uses a single point of obfuscation, product inference, and center of gravity defuzzification. -rule is as follows.

 : if is ,    is , and and   is , then is , , where is the number of rules for fuzzy systems.

Define the output of fuzzy system:

where is the weight coefficient that can be adjusted and is fuzzy basis function:

where is the membership function of the fuzzy system.

If the controlled object exists uncertainty or unknown function, and are unknown; then the predictive control law equation (14) cannot be accurately determined, so we propose using and of fuzzy system to approach and , respectively:

where and .

Assumption 4. There exist optimal parameter vectors and of and , which can make the output of fuzzy system approach and arbitrary precision:

Define the approximation error of fuzzy system:

The equivalent fuzzy predictive controller of (14) is given by

where is Moore-Penrose inverse of .

In order to overcome the impact of the approximation error on system, it is necessary to introduce a robust compensation term to improve the performance of the whole system.

So the new control law is given by

where the matrices , , and and parameter are given in Section 3.2.

The adaptive law of , is given by

where are adaptive parameters of fuzzy system.

3.2. Stability Analysis and Proof

Theorem 5. If uncertain nonlinear system (1) chooses control law (24) and the adaptive law of formula (26), it can be satisfied:

(i)closed-loop system is uniformly ultimately bounded;(ii)for a given inhibition level , output tracking error can achieve performance:

Proof. of formula (4) is given by

Take (24) into (28):

Take (24) into (29):

It can be also written as

where .
So error equation is obtained that the th error equation is given by

where is the th row elements of the matrix and the is determined by the predictive period , the relative degree , and control order .
So

If we choose reasonable, it can make formula (33) as Hurwitz Polynomial.
Formula (33) can be also written as

where

Lyapunov function is constructed as follows:

where represents trace of matrix.
, is positive symmetric matrix, and it satisfies the positive definite solution of Riccati equation:

where is design parameter.
is derivative along the trajectory of the system:

where and

So

Because of , , and we choose .
Now it can obtain

where is the smallest eigenvalues of matrix.
It can be known from (41), if

So the closed-loop system is uniformly ultimately bounded.
The integration of (41) from to is given by

Because of ,

So output tracking error can achieve performance (27).

4. Simulation Analysis

In order to verify the control performance of the controller and engineering applications, apply it to rudder roll stabilization system. This paper is a transport ship (see [20]). The controlled object model is given by

where and are unknown functions that are caused by changes of the speed and metacentric height; generally, these variations cannot be accurately determined. is wave disturbance and its calculation is given by literature (see [20]).

If there are no unknown functions, it can be known:

where

where are roll velocity, yaw velocity, yaw, and roll angle, respectively.

The initial speed is ; assume that have perturbation of 15% and have perturbation of 20%. In the simulation, simulate the variations and using a set of random numbers within the given range.

It can be calculated by the known conditions:

From (48), we know that , so ; if we choose , , and , the is calculated:

We use the following fuzzy linguistic variables:

  (negative large), (negative middle), (negative small), (zero), (positive small), (positive middle), and (positive large).

The membership functions are given by

where , , . This paper uses the seven fuzzy rules to approximate each variable of and . The rules are as follows:

 : if is , is , and … and is , then is , .

Remark 6. Because of single input and multiple outputs for system (45) in this paper, we choose . The inverses of and are used Moore-Penrose inverse.

Initial weights values of fuzzy systems are given by

So .

The following simulation conditions and analysis of the simulation results to verify the effectiveness of the proposed method are given.

Condition 1. In system with the wave disturbance, wave period , significant wave height , and relative damping coefficient , encounter angle is , a reference yaw angle is , and wave disturbance calculation is given by [14].

(i)If there is no uncertain term in system (45), the control law is only analytic model predictive control for system simulation; the simulation results are shown in Figure 1.(ii)If there are uncertain terms in system (45), the control law is only analytic model predictive control for system simulation; the simulation results are shown in Figure 2.(iii)If there are uncertain terms in system (45), the control law is fuzzy adaptive analytic model predictive control for system simulation; the simulation results are shown in Figure 3.

Figure 1: Analytic model predictive control on the accurate model.

Figure 2: Analytic model predictive control on the uncertain model.

Figure 3: Fuzzy adaptive analytic model predictive control on the uncertain model.

Figures 1(a), 2(a), and 3(a) are output of roll angle; the dotted line is the ship rolling motion without adding the controller, and the solid line is the rolling motion using controller. Figures 1(b), 2(b), and 3(b) are the output of yaw angle.

In order to compare the effect of roll stabilization under different conditions, the roll reduction rate (RRR) is given by literature [12]:

where and are the standard deviation of roll angle with and without the roll damping system. Assume that is standard deviation of yaw angle.

The roll reduction rate and standard deviation of yaw angle in each case can be seen in Table 1.

Table 1: Roll reduction rate and standard deviation of yaw when  m.

Parameters
AP
RCS
RRR
CP

i
4.8626
1.8355
62.25%
3.1008

ii
4.8682
2.3629
51.46%
3.2504

iii
4.8682
1.9163
58.58%
3.1723

It is apparent that it achieves better tracking performance on fuzzy adaptive analytic model predictive control law and the roll reduction rate achieves a satisfactory result when there are uncertain terms in system (45).

Condition 2. In a system with the wave disturbance, wave period , significant wave height , relative damping coefficient , encounter angle is , and a reference yaw angle is 20°.

(i)If there is no uncertain term in system (45), the control law is only analytic model predictive control for system simulation; the simulation results are shown in Figure 4.(ii)If there are uncertain terms in system (45), the control law is only analytic model predictive control for system simulation; the simulation results are shown in Figure 5.(iii)If there are uncertain terms in system (45), the control law is fuzzy adaptive analytic model predictive control for system simulation; the simulation results are shown in Figure 6.

The roll reduction rate and standard deviation of yaw angle in each case can be seen in Table 2. As can be seen from Figure 5 when system (45) is uncertain, in dealing with wave disturbance strengthened, if we use only analytic model predictive control law, it will make the roll reduction rate significantly lower and part of roll angle will be increased. Figure 6 and Table 2 show that the proposed method in this paper has strong robustness.

Table 2: Roll reduction rate and standard deviation of yaw when  m.

Parameters
AP
RCS
RRR
CP

i
7.4459
2.9333
60.61%
3.1643

ii
7.4459
3.6529
50.94%
3.3310

iii
7.4459
3.1594
57.57%
3.2635

Figure 4: Analytic model predictive control on the accurate model.

Figure 5: Analytic model predictive control on the uncertain model.

Figure 6: Fuzzy adaptive analytic model predictive control on the uncertain model.

The simulation results under Condition 2 are as shown in Figures 4, 5, and 6 and Table 2.

5. Conclusions

Robustness and control effect are important factors to measure the control quality of a nonlinear system. In the presence of uncertainty, the analytic model predictive control performance will be reduced significantly, and system cannot meet the robustness requirements. Using fuzzy system to approximate the uncertainties in the controller, weights of fuzzy systems are based on system feedback error online adjustment, to make fuzzy system approach the unknown functions of controller. Utilizing Taylor equation, the fuzzy adaptive predictive control law is achieved, and since online optimization is not required, the huge calculation burden of predictive control can be avoided. A robust compensation term is introduced to eliminate this influence which is the impact of the fuzzy modeling errors on the system.

Finally, we apply it to the rudder roll stabilization system control, which is the ship motion model with four freedom degrees. Simulation results show that the proposed algorithm has better control effect and robustness.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors are grateful to the guest editors and anonymous reviewers for their constructive comments based on which the presentation of this paper has been greatly improved.

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