- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Journal of Control Science and Engineering
Volume 2013 (2013), Article ID 498461, 7 pages
Optimal Robust Adaptive Fuzzy Tracking Control without Reaching Phase for Nonlinear System
LESSI Laboratory, Department of Physics, Faculty of Sciences Dhar El Mehraz, B.P 1796, FES-Atlas 30000, Morocco
Received 25 January 2013; Accepted 28 March 2013
Academic Editor: Mohamed Zribi
Copyright © 2013 El Mehdi Mellouli 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.
An optimal tracking-based indirect adaptive fuzzy controller for a class of perturbed uncertain affine nonlinear systems without reaching phase is being developed in this paper. First a practical Interval Type-2 (IT2) fuzzy system is used in an adaptive scheme to approximate the system using a nonlinear model and to determine the optimal value of the gain control. Secondly, to eliminate the trade-off between tracking performance and high gain at the control input, a modified output tracking error has been used. The stability is ensured through Lyapunov synthesis and the effectiveness of the proposed method is proved and the simulation is also given to illustrate the superiority of the proposed approach.
After the approximation-based adaptive fuzzy controller (AFC) of Wang  for a class of uncertain affine nonlinear system, many approaches and ideas have been developed in recent years to overcome the difficulty in controller design [2–4].
The primary feature that characterizes the fuzzy logic is its high capacity for representing and modelling the nonlinear systems with imprecise uncertainty, as the universal approximation theorem, by Lee and Tomizuka, illustrates .
Many effective adaptive fuzzy control schemes have been developed to incorporate with human expert knowledge and information in a systematic way, which can also guarantee various stability and performance . The most important issue for Fuzzy Logic Systems (FLSs) is how to get a system design with the guarantee of stability and control performance [7–9].
An adaptive fuzzy control system includes uncertainties caused by unmodeled dynamics, Fuzzy Approximation Errors (FAEs), and external disturbance, which cannot be effectively handled by the FLS and may degrade the tracking performance of the closed-loop system [10, 11]. The AFC combined with control technique is an effective approach for rejecting those uncertainties, ensuring stability and consistent performance [12–14].
The research of fuzzy model under has attracted many attentions in recent years [4, 15], such as the apparent similarities between and fuzzy control which motivate considerable research efforts in combining the two approaches for achieving more superior performance.
Moreover, to the best of our knowledge, the control gain needs to be known in all previous , indirect adaptive fuzzy controller (HIAFC) approaches, such as the arbitrary choice of the gain which does not always give good results, for which we propose in this work a method for extracting automatically the optimum gain from the Lyapunov equation whilst respecting system stability.
The convergence of the system in the initial time needs the appearance of high gain at the control input, and the high gain is unavoidable in all previous tracking-based AFC approaches. The best method to solve the problem of the tradeoffs between tracking performance and high gain at the control input is to eliminate the reaching phase. During the reaching phase the tracking error cannot be controlled directly and the system response is sensitive to parameter uncertainties. Several methods have been proposed to completely eliminate the reaching phase .
This paper focuses on a class of Single-Input Single-Output (SISO) perturbed uncertain affine nonlinear systems involving external disturbances without exact knowledge of dynamic functions. Firstly, we use the type-2 fuzzy technique to determine the optimal value of the gain control. Secondly, a modified output tracking error is used to eliminate the reaching phase .
The paper is organized as follows: Section 2 presents the problem statement. Section 3 gives the control design strategy. An illustration example is described in Section 4. Finally, the simulation results are being used to demonstrate the effectiveness and performance of the proposed approach.
2. Problem Formulations
Considering the following th-order SISO affine nonlinear dynamical system, Chen et al. : Or equivalently where is the state vector of the systems which is assumed to be available for measurement, and are, respectively, the input and the output of the systems. and are two functions that are unknown, nonlinear, and continuous; denotes the external disturbance. For (1) to be controllable, we require that for in certain controllability region. Assume that the given reference is bounded and have up to () bounded derivatives. The reference vector is denoted as . Define the tracking error and the error vector .
Assumption 1. For all , there exist unknown bounded , and such that , and hold, where compact set is a certain controllable region.
During the AFC design, to improve the tracking performance under the external disturbance, an additional compensator associated with an attenuation level is usually suggested to apply, Chen et al. . If the prescribed attenuation level is smaller, the tracking performance is better while the control input gain is higher as the output of the compensator becomes larger.
To avoid high control input gain, we have modified the following output tracking error Yilmaz and Hurmuzlu : where (condition 1) is designed to make small enough at the onset of the motion , and (condition 2) should rapidly vanish as the motion evolves at .
A suggested is given in the following exponential form: with is selected to satisfy condition one and is selected to satisfy condition two. For the selections of one can follow the methods in Yilmaz and Hurmuzlu , on the other side is selected to satisfy condition two .
Now, the objective of this paper is to determine the optimal value of the gain control, in a way to force to follow a given bounded reference signal .
Let us denote the parameter tracking error for some parameter estimate and optimal parameter estimate of Type-2 Fuzzy Logic System (T2FLS). Let denote the sum of error due to fuzzy modelling approximations. Then our design objective is to impose an adaptive fuzzy control algorithm so that the following asymptotically stable tracking is achieved while (i.e., in the case of perfect fuzzy approximation and free of external disturbance). While appears, the following tracking performance is requested : where . is the Lyapunov function, , is the prescribed attenuation level, and .
, , and will be defined in the next subsection.
Remark 2. (i) The roots of polynomial in the characteristic equation of (6) are all in the open left-half plane via an adequate choice of coefficients .
(ii) If the system starts with initial condition , then the performance in (7) can be rewritten as where , and , that is, the -gain form to the tracking error must be equal to or less than .
3. Control Design Strategy
3.1. Indirect Adaptive Control Scheme
In this section, we propose a new optimal tracking-based indirect adaptive output-feedback fuzzy controller that eliminates the reaching phase, with guaranteed stability of the closed loop system. Based on the combination of the optimal control with fuzzy logic control, using fuzzy identifier and fuzzy logic control, the control design relies on the solution of an algebraic Riccati equation.
If the system (1) is well known and then the control should be designed to have the following idealized control law: where .
However, in practice the functions and are unknown, thus the ideal controller in (9) cannot be realized, and the choice of the gain control does not always give good results. In this case the nonlinear functions and are approximated using T2-fuzzy systems universal approximation property and by the same technique we determine the optimal gain control. Hence, the fuzzy adaptive control law is as follows: where defined the auxiliary control employed to attenuate the approximation error of the fuzzy model and to eliminate the external disturbance.
, , and are the type-2 fuzzy approximation of ,, and .
3.2. Interval Type-2 Fuzzy Logic System (IT2FLS)
For an Interval Type-2 Fuzzy Logic System IT2FLS with , total number of IF-THEN rules in the rule base, the th rule can be written as follows: Equation (11) represents a T2 fuzzy relation between the input and the output spaces of the FLS, where ’s are antecedent type-2 sets, is the output, and ’s are the consequent T2 fuzzy singleton.
Since fuzzy sets are type-2, we need to perform the reduction operation type. This operation will give each function estimated two vector of the fuzzy basis functions  is the interval set determined by two end points and , and is the firing interval.
Accordingly, the firing interval bounds for the th rule of an IT2FLS with inputs, and , can be rewritten as follows: Using the centre of gravity, the defuzzified crisp output for each output is given by Liang and Mendel : can be represented as a vector of fuzzy basis functions (FBFs) expansion as follows: is the FBF vector of such that whose components are given by represents the conclusion of T2FLS.
The previous equation (18) will be used, in an indirect adaptive control, to approximate the unknown system dynamics and to determine the optimal gain control.
Therefore the expression (18) can be expressed as: Define the compact sets , where , and are given constants.
The minimum approximation error is defined by where , , and are an optimal parameter vector defined as where is a positive constant used below.
3.3. Tracking Performance Design in Indirect Adaptive Fuzzy System
Choose the compensator as where is the solution of the following Riccati equation: where is prescribed attenuation level and is positive constant verified .
Theorem 3. If we select the following adaptive fuzzy control law in the nonlinear system (1) where With is the solution of the Riccati equation (25), then the tracking performance in (7) is achieved for a prescribed attenuation level .
Proof. We have
And the equation of the control already proposed
Utilising (3) and substituting (30) into (31), the output error dynamics may be expressed as
The error dynamics can be represented by where
Consider the following Lyapunov function: where Utilizing (25) and (32) into (38) where is defined in (21), the can be written as By consideration of the update law (27), (28), (23), and (29), can be written as Integrating the above equality from to yields (): Since the above inequality implies the following inequality: Hence, the inequality (7) holds. This completes the proof of theorem. So the system is stable and the error will asymptotically converge to zero; that is, a performance is achieved.
4. An Illustrative Example
4.1. The Dynamic Model
In this section consider a single-link robot manipulator governed by the following dynamic model : where : Position, : Velocity, : Nonlinear term depending on , , : Mass and damping, : Length of the manipulator, : is the gravitational acceleration and is the disturbance.
We assume that the position and the velocity are available for measurements, where , , , and .
4.2. Controller Parameters Design
Step 1. In the first step, we need to define the type-2 fuzzy sets for modelling the unknown functions entering into the creation of the control law and to determine the optimal value of the gain control. The choice of the number of fuzzy sets and constant , , and are related to knowledge of expert on the system.
The fuzzy membership functions are chosen as such as , , , and with , , and .
Step 3. Design parameters of the control law.
The control parameters for simulation are chosen as follows: , , , , , eye (2), and .
The solution to Riccati equation for is
4.3. Simulation Result
Simulation result is presented to validate performance and robustness of the proposed approaching that we have been using fuzzy logic to determine automatically the gain of the control and modifying the output tracking error to eliminate the reaching phase.
Three fuzzy sets for each input have been found sufficient for an efficient system design. Fuzzy sets for inputs and are defined according to the membership functions presented forward in Step 1.
The sampling time is defined as and the running time as .
Figure 1 present a responses of the output versus the desired output .
In this paper, we have proposed a new method to determine the optimal value of the gain control based on type-2 fuzzy logic systems, and to eliminate the trade-off between tracking performance and high gain at the control input, we have used the modification in the output tracking error.
The parameters of the dynamics systems are estimated by using the fuzzy model. Furthermore, the parameters can be tuned on-line by the adaptive law based on Lyapunov synthesis.
- L. X. Wang, “Stable adaptive fuzzy control of nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 1, no. 2, pp. 146–155, 1993.
- J. A. Farrell and M. M. Polycarpou, Adaptive Approximation based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, John Wiley & Sons, Hoboken, NJ, USA, 2006.
- K. Haisen and L. Jiang, “Adaptive control for a class of nonlinear system with redistributed models,” Journal of Control Science and Engineering, vol. 2012, Article ID 409139, 6 pages, 2012.
- Y. Pan, M. Joo Er, D. Huang, and Q. Wang, “Fire-rule-based direct adaptive type-2 fuzzy tracking control,” Engineering Applications of Artificial Intelligence, vol. 24, pp. 1174–1185, 2011.
- H. Lee and M. Tomizuka, “Robust adaptive control using a universal approximator for SISO nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 1, pp. 95–106, 2000.
- M. Bernal and T. M. Guerra, “Generalized non-quadratic stability of continuous-time Takagi–Sugeno models,” IEEE Transactions on Fuzzy Systems, vol. 18, pp. 815–822, 2010.
- Y. Zhang, C. Liu, and X. Mu, “Hybrid feedback stabilization of fuzzy nonlinear systems,” Journal of Control Science and Engineering, vol. 2011, Article ID 579871, 7 pages, 2011.
- S. K. Nguang and W. Assawinchaichote, “ filtering for fuzzy dynamical systems with D stability constraints,” IEEE Transactions on Circuits and Systems I, vol. 50, no. 11, pp. 1503–1508, 2003.
- N. Golea, A. Golea, and K. Benmahammed, “Stable indirect fuzzy adaptive control,” Fuzzy Sets and Systems, vol. 137, no. 3, pp. 353–366, 2003.
- S. Tong, J. Tang, and T. Wang, “Fuzzy adaptive control of multivariable nonlinear systems,” Fuzzy Sets and Systems, vol. 111, no. 2, pp. 153–167, 2000.
- S. Tong, B. Chen, and Y. Wang, “Fuzzy adaptive output feedback control for MIMO nonlinear systems,” Fuzzy Sets and Systems, vol. 156, pp. 285–299, 2005.
- G. G. Rigatos, “Adaptive fuzzy control with output feedback for tracking of SISO nonlinear systems,” International Journal of Neural Systems, vol. 18, no. 4, pp. 305–320, 2008.
- J. Yoneyama, M. Nishikawa, H. Katayama, and A. Ichikawa, “Design of output feedback controllers for Takagi-Sugeno fuzzy systems,” Fuzzy Sets and Systems, vol. 121, no. 1, pp. 127–148, 2001.
- B. S. Chen, C. H. Lee, and Y. C. Chang, “ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach,” IEEE Transactions on Fuzzy Systems, vol. 4, no. 1, pp. 32–43, 1996.
- Y. Pan, Y. Zhou, T. Sun, and M. Joo Er, “Composite adaptive fuzzy tracking control of uncertain nonlinear systems,” Neurocomputing, vol. 99, pp. 15–24, 2013.
- C. Yilmaz and Y. Hurmuzlu, “Eliminating the reaching phase from variable structure control,” Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME, vol. 122, no. 4, pp. 753–757, 2000.
- J. C. Doyle, K. Glover, P. P. Khargonekar, and B. A. Francis, “State space solution to standard control problems,” IEEE Transactions on Automatic Control, vol. 34, no. 8, pp. 831–847, 1989.
- J. M. Mendel, Rule-Based Fuzzy Logic Systems’: Introduction and New Directions, Prentice-Hall, Englewood Cliffs, NJ, USA, 2001.
- Q. Liang and J. Mendel, “Interval Type-2 fuzzy logic systems’: theory and design,” IEEE Transactions on Fuzzy Systems, vol. 8, pp. 535–550, 2000.
- F. Mei, Z. Man, and T. Nguyen, “Fuzzy modeling and tracking control of nonlinear systems,” Mathematical and Computer Modeling, vol. 33, no. 6-7, pp. 759–770, 2001.