LMI-Based Model Predictive Control for a Class of Constrained Uncertain Fuzzy Markov Jump Systems

An extended model predictive control algorithm is proposed to address constrained robust model predictive control. New upper bounds on arbitrarily long time intervals are derived by introducing two external parameters, which can relax the requirements for the increments of the Lyapunov function. The main merit of this new approach compared to other well-known techniques is the reduced conservativeness. The proposed method is proved to be effective for a class of uncertain fuzzy Markov jump systems with partially unknown transition probabilities. A single pendulum example is given to illustrate the advantages and effectiveness of the proposed controller design method.


Introduction
Model predictive control (MPC), a powerful strategy for dealing with input and state constraints for a control system, has first attracted notable attention in industrial applications [1].Since stability analysis approach for MPC was proposed, many significant advances in understanding MPC from a control theoretician's viewpoint have then been acquired [2][3][4].Meanwhile, the MPC formulation for constrained linear systems has been naturally extended to not only nonlinear systems [5][6][7] but also some more complex systems, for example, stochastic systems [8,9], time-delay systems [10], hybrid systems [11], uncertain systems [12], and so on.By employing the idea of control structure optimization, for example, distributed MPC [13], centralized MPC, and coordinated MPC, the theory of MPC has been further refined.As is well known, for a long time, efficient setting of the large set of tunable parameters has been a hard problem for MPC.Fortunately, many available methods have already been developed (see [14] and the references therein for more details).Nowadays, many scholars tend to develop "fast MPC" in order to ease the huge online computational burden [15][16][17].However, it should be noticed that MPC algorithm may perform very poorly when model mismatch occurs in spite of the inherent robustness provided by the feedback strategy based on the plant measurement at the next sampling time.
Therefore, in the past decades, the issues of MPC algorithm for uncertain systems have been addressed much in the literature.A robust constrained MPC scheme considering two classes of system uncertainty, that is, polytopic paradigm and structured feedback uncertainty, has been analyzed by means of linear matrix inequalities (LMIs) by Kothare et al. [18].As opposed to a single linear static state-feedback law in [18], Bloemen et al. [19] divided the input sequence into two parts, the first   inputs being computed by finite MPC method and the other inputs being calculated based on linear static state-feedback law.Because   was variable, the end-point state-weighting matrix and the invariant ellipsoid were transformed into variables in the online optimization, and thus this algorithm achieved the trade-off between feasibility and performance.This work has been improved by applying parameter-dependent Lyapunov function [20][21][22].In [23][24][25], an efficient robust constrained MPC with a timevarying terminal constraint set was developed.The proposed algorithm can obtain a perfect control schema achieving lower online computation and larger stabilizable set of states while retaining the unconstrained optimal performance as much as possible.It is worth mentioning that in order to analyze the stability of the system and obtain an upper bound of cost function, the optimal cost function has been qualified as a constraint on the increments of Lyapunov function [26].However, this constraint is too strict, since to guarantee the system stability, the increments of Lyapunov function are only required to be negative.Motivated by this, in the present paper, a modified MPC scheme in which two extra parameters are introduced is proposed based on the characteristics of convergent series in order to reduce the conservativeness.
Many real systems, such as solar thermal receivers, economic systems, and networked control systems, may experience random abrupt changes in system inputs, internal variables, and other system parameters.Uncertainties like these are best represented via stochastic models [27][28][29], such as fuzzy Markov jump system (MJS) in which the subsystems are modeled as fuzzy systems.Based on the approximation property of the fuzzy logic systems [30][31][32], fuzzy MJSs are developed for the nonlinear control systems with abrupt changes in their structure and parameters.
In this paper, the problem of MPC controller design for a class of uncertain fuzzy MJSs with partially unknown transition probabilities is addressed.This kind of system fits into a very wide range of practical dynamic systems combining nonlinear behaviors with changes or uncertainties of structure or parameters.Meanwhile, constraints, such as energy limitation, levels in tanks, flows in piping, and maximum of pH value, can be systematically included during the controller design process.The system concerned is much more complex than fuzzy systems with uncertainties or MJSs with partially unknown transition probabilities [33,34], because here the uncertainties will consist of four levels (the system parameter uncertainty, the membership degree uncertainty, the mode uncertainty, and the transition probability uncertainty) and they are not mutually independent.Therefore, although the formulation seems similar, the results for fuzzy systems with uncertainties or MJSs with partially unknown transition probabilities cannot be directly used in this scenario.A comparison with the method in [20] (modified in [22]) is carried out by simulation on a single pendulum control problem.Due to LMIs' prevalence in convex optimization problems, especially for the cases with high order matrices, and the availability of reliable general commercial solvers, the LMI algorithm is employed to deal with the underlying optimization problems in this study.The remainder of this paper is organized as follows.The mathematical model of the concerned system is formulated and some preliminaries are given in Section 2. Section 3 is devoted to deriving the results for the controller design.Numerical examples are provided in Section 4 and this paper is concluded in Section 5.
Notation.The notation used throughout the paper is fairly standard.The superscript "" stands for matrix transposition.R  denotes the -dimensional Euclidean space.The notation  > 0 (≥ 0) means that  is real symmetric and positive (semipositive) definite and  >  (≥ ) means  −  > 0 (≥ 0).In symmetric block matrices or complex matrix expressions, we use an asterisk ( * ) to represent a term that is induced by symmetry and diag{⋅ ⋅ ⋅ } stands for a blockdiagonal matrix. and 0 represent identity matrix and zero matrix, respectively.Matrices, if their dimensions are not explicitly stated, are assumed to be compatible for algebraic operations.‖ ⋅ ‖ 2 stands for the usual Euclidean norm.

Problem Formulation and Preliminaries
where () ∈ R  is the state vector, () ∈ R  is the control input, () ∈ R  is the system output,  ≜ [ As commonly done in the literature, it is assumed that the premise variable vector  does not depend on the control variables and the disturbance.The center-average defuzzification method is used as follows: where In what follows, we use the following notation for simplicity: A more compact presentation of system (1) is given by The process {  ,  ≥ 0} is described by a discrete-time homogeneous Markov chain, which takes values in the finite set I with mode transition probabilities where   ≥ 0, ∀(, ) ∈ I×I, and ∑  =1   = 1.For example, the transition probability matrix (TPM) can be given by The transition probabilities described above are considered to be partially available and can be divided into two parts as In addition, if K is further described as where K  ∈ N + ,  ∈ {1, 2, . . .,   }, represents the index of the th known element in the th row of matrix Π.And we denote The following fuzzy control law is chosen: Under the control law, the closed-loop system of Σ is given by where     =   (  , ) ≜     +       (  ).For the fuzzy MJS, the following definition will be adopted in the rest of this paper.
Definition 1.The fuzzy MJS in ( 12) is said to be stochastically stable, if for any initial condition (0) =  0 ,  0 ∈ I, the following inequality holds: where {⋅} stands for the mathematical expectation.
The objective of this paper is to design a state-feedback model predictive controller such that the closed-loop system ( 12) is stochastically stable.

Model Predictive Control.
Model predictive control (MPC) makes use of a receding horizon principle, which means that at each sampling time , an optimization algorithm will be applied to compute a sequence of future control signals.The used performance index depends on the predicted future states of the plant (+ | ),  ≥ 0, which can be calculated through the newly obtained measurements () and the predictive model.Here, we use (+ | ), (+ | ) as the state and output, respectively, of the plant at time  +  predicted by utilizing the measurements at time ; (+ | ) represents the control action moves at time  +  computed by the optimization problem (14);  is the prediction horizon and  is the control horizon.In what follows, let P and M represent {0, 1, . . ., } and {0, 1, . . ., }, respectively.
In this section, the problem formulation for MPC using the model ( 12) is discussed.The goal is to find an input sequence {( | ), ( + 1 | ), . . ., ( +  − 1 | )} at each sampling time  which minimizes the following performance index (): where and then it can be replaced by min The matrices  > 0 and  > 0 are symmetric weighting matrices.In particular, we will consider the case where  = , which means that the control horizon is equal to the prediction horizon.The input and output constraints focused on in this paper are Euclidean norm bounds and componentwise peak bounds, given, respectively, as

Model Predictive Control Using Linear Matrix Inequalities
In this section, the problem is solved by deriving an upper bound on the objective function () based on the statefeedback control law ( +  | ) = ∑  =1 ℎ     (  )( +  | ),  ∈ P. Therefore, via minimizing this upper bound at each sampling time , we will obtain the sequence of control input signals.

Minimization of the
where  > 0 and   ( + ) are obtained from the solution (if it exists) to the following linear objective minimization problem: with Defining Q  (  ) ≜ P −1  (  ) > 0 and using Schur complements, this is equivalent to min The parameter-dependent function  is required to satisfy (27).By substituting and the state-space equations in (12), inequality (27) becomes This is equivalent to Then, ( 34) is satisfied for all  ∈ P if where ĉ is the minimum term of  + ,  ∈ [0 ].Substituting P  (  ) = Q −1  (  ), Q  (  ) > 0, using Schur complements, and then pre-and postmultiplying the above inequality by diag{Q  ( + ), , , } and diag{Q  ( + ), , , }, we can see that this is equivalent to with Inequality (37) is affine in [  ( + ,   )   ( + ,   )].Following the logic of de Oliveira, Bernussou and Geromel [35], and Cuzzola et al. [20], (37) is satisfied for all if and only if there exist  > 0,   (  ) =   (  ), and a positive  such that Mathematical Problems in Engineering 9 with The feedback matrix is then given by   ( + ) =   ( + ) −1 .
Remark 3. Based on the above analysis, we can see that the choice of control horizon  can amount to the choice of series ĉ .That is, given a constant control horizon , we can construct some infinite convergent series whose corresponding series ĉ possess some properties, such as ĉ = 1/( + 1) 2 .Therefore, we will discuss neither the control horizon  nor the predictive horizon  which is supposed to be equal to .
Remark 4. In particular, when the control horizon  = ∞, the conditions for the above derivation can be satisfied if and only if the series ĉ = 0,  ≥ 0. Then it follows that the method is equivalent to the approach in [20] (modified in [22]).

Minimization of the
By virtue of Schur complements, we have Using the similar logic in [35], (46) is proved equivalent to (42).The proof of ( 43)-( 44), which can be achieved by an analogous argument, is omitted for the sake of brevity.
Remark 6.The obtained theorems can be easily reduced to simple situations, for example, fuzzy systems with uncertainties and Markov jump systems, and can be solved via commercial solvers such as LMITOOL, YALMIP, and GloptiPoly.

Numerical Simulation
In this section, we present a numerical example that clearly illustrates the improvement obtained with Theorem 2. We will compare under different situations the improved method with the approach in [20] (modified in [22]) which can be seen as ĉ = 0,  > 0 and  = 1.Consider the single pendulum system: Set () = [ 1 ()  2 ()]  , and choose the membership functions as For the sake of simplicity, we use two T-S fuzzy rules to approximate this system. where Our purpose here is to illustrate the advantages of the proposed method by comparing the optimal parameter  for different situations.First of all, supposing ĉ = 1/( + 1) 2 ,  = 0.5, and the initial states (0) = [3/8; 0.5], the steady-state responses of the closed-loop fuzzy MJS with input constraints ‖( +  | )‖ 2 ≤ 3,  > 0,  ∈ M, are shown in Figure 1.Meanwhile, the optimal results of  compared for different initial conditions are shown in Table 1.One may note that the average values of  become much smaller when the additional parameters ĉ and  are introduced.Then, assuming initial states (0) = [/4; 0.5],  = 0.5, we can obtain the corresponding values of  for different ĉ listed in Table 2.In the same way, by assuming initial states (0) = [/4; 0.5], ĉ = 1/( + 1) 2 , the information about  for different values of  is given in Table 3.It is easy to observe from Tables 2 and 3 that the optimal performance is closely related to the two parameters.

Conclusion
In this paper, the problem of controller design based on MPC algorithm for uncertain systems is discussed.A relaxed scheme which has less conservativeness than traditional approaches is derived through introducing two additional parameters.Based on this scheme, a new set of criteria for model predictive controller design is obtained based on the fuzzy Markov jump system with partially unknown TPMs in an arbitrarily large horizon.A practical example is presented to show the effectiveness and applicability of the developed method.It is expected that the methods and ideas behind the paper could be extended to other systems or issues, such as filter design for the underlying system.

Table 2 :
The value of  for different ĉ ,  > 0 with constraints.

Table 3 :
The value of  for different  with constraints.