Journal of Control Science and Engineering

Volume 2019, Article ID 5208612, 7 pages

https://doi.org/10.1155/2019/5208612

## Desired Terminal State Concept in Model Predictive Control: A Case Study

University of Pardubice, Pardubice 530 02, Czech Republic

Correspondence should be addressed to Daniel Honc; zc.ecpu@cnoh.leinad

Received 20 February 2019; Accepted 21 April 2019; Published 8 May 2019

Academic Editor: Radek Matušů

Copyright © 2019 František Dušek and Daniel Honc. 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

The paper deals with an online optimization control method for dynamical processes called Model Predictive Control (MPC). It is a popular control method in industry and frequently treated in academic areas as well. The standard predictive controllers usually do not guarantee stability especially for the case of short horizons and large control error penalization. Terminal state is one way to ensure stability or at least increase the controller robustness. In the paper, deviation of the predicted terminal state from the desired terminal state is considered as one term of the cost function. Effect of the stability and control quality is demonstrated in the simulated experiments. The application area for online optimization methods is very broad including various logistics and transport problems. If the dynamics of the controlled processes cannot be neglected, the optimization problem must be solved not only for steady state but also for transient behaviour, e.g., by MPC.

#### 1. Introduction

Minimization of a quadratic cost function is a common method for solving many engineering problems. In the control area, this method is fundamental not only for standard control design methods like optimal control, e.g., [1, 2] but also for a state estimation [3]. For example, well known Kalman estimator [4] was published in 1960. Current state of HW and SW technology allows us to look back a bit, modify, and apply some methods well known from the past but not used practically. In the contrary to standard PID controllers such methods have potential to increase control quality and solve more complicated and complex tasks. Usability, reliability, robustness, and of course also the price of such a system is the other side of the coin. In the paper we are introducing MPC desired terminal state calculated from steady state and we test it by simulation for higher order single-input single-output process.

The paper is structured as follows: standard controller design is described in Section 2, modified method is introduced in Section 3, simulated control experiments are presented in Section 4, and conclusions are given in Section 5.

#### 2. Standard Controller Design

Under the assumptions of linear controlled system and quadratic cost function it is possible to formulate the task of the optimal controller design as a standard mathematical problem, extreme finding with an analytic solution. A unique solution exists also in the case of constrains existence in a form of linear inequalities.

The key part of the controller design is to incorporate maximum of the known information and demands into the properly formulated cost function. It is possible to involve various (even conflicting) control demands. Then the controller tuning consists in weightings of the particular demands.

From practical point of view, it is appropriate to formulate the task in discrete-time domain with receding (finite) control horizon [5–8]. The length* N* of the horizon is a parameter in the control design. The general formulation of a set-point tracking task is given by (1a), a state space description of the controlled dynamical linear system with state and input variables constraints, and by (1b), a quadratic cost function* J* (control objective) with three terms. The cost function* J* depends on the horizon length* N*, the initial state** x**(*k*) (initial conditions in time* k*), and the time course of the future set-point (vector along the control horizon). The solution consists in computation of such a vector of system inputs , which leads to the minimum of the cost function and simultaneously respects all constrains.where **A**,** B**,** C**,** D** are parameters of a discrete-time dynamical process model and **H**,** h**,** G**,** g** are parameters of state and input variables constraints.where ,** Q**, and** R** are weighting matrices of particular terms.

The cost function always contains the fundamental control requirement, the term : the controlled outputs** y** of the system should follow the set-points** w**. This basic requirement is usually followed by another term of the cost function. The term implies the control costs: the set-point tracking is desired but not at the cost of arbitrarily large control actions. The term* J*_{x} in the cost function can be used only in the case of finite control horizon and state space description. It introduces into the cost function a dependence on the system state at the end of the control horizon called terminal state. The predictive controller design based on input-output description does not use it in a basic formulation of the cost function. The terminal state is obviously introduced in the extensions concerning the stability and robustness; see, e.g., [9–15]. The terminal state brings into the cost function dependence on all state variables. The standard cost function depends only on the system outputs (or control error) and it can be independent from some state variables—this is given by matrix** C**. Thus some states can increase ad infinitum even if the cost function is finite. In the case of control design based on input-output models, where state does not exist in a nature form, the terminal state is replaced with a sequence of input and output variables. That approach of the terminal state is called in the literature “terminal constrains” [16–20].

In some cases the terminal state is important from the mathematical point of view. In case of LQ control design on finite horizon, the mathematical importance of the terminal state is that the matrix determines the initial value of a working matrix which is developing by iterating solution of the discrete Riccati equation.

In literature the terminal state is obviously mentioned only in the context of the controller stability. The use of the terminal state has also an implication to the controller performance. The standard formulation of the terminal state in the form of (1b) leads to the permanent steady state control error in case of nonzero set-point. This problem can be easily solved by the terminal state in a form of the deviation from a the desired terminal state . The desired terminal state is a function of the set-point and/or other demands. Additional optimization in steady state can be an integral part of the controller due to the desired terminal state concept. Under the “optimization in steady state” we understand that controller ensures minimum of the weighted quadratic norm of a vector of deviations between desired and calculated terminal state.

Clear and unique additional requirements can be formulated because the state vector contains complete information about the state of the system. The predictive controller can ensure, e.g., demand of minimum energy cost of a system with more inputs than outputs (nonsquare, overactuated system). The problem of how to determine an optimal steady state for such systems is discussed, e.g., in [21].

Application area of predictive control methods is not limited to refinery, chemical, pulp, and paper industries, but it is becoming very broad. It can be advantageously applied also in transport industry, as demonstrated in [22] for traffic signal control based on traffic density prediction or in [23], where the authors propose the MPC algorithm for automatic train operation system.

#### 3. Modified Controller Design

The controller design starts from a discrete-time state space model of the controlled MIMO (Multi-Input Multi-Output) system with* n*_{u} inputs,* n*_{x} state variables, and* n*_{y} outputs. The model is in a standard form (3a); we suppose matrix** D** =** 0**.where **u**(*k*) is vector of inputs with size [*n*_{u},1], **x**(*k*) is state vector with size [*n*_{x},1] and **y**(*k*) is the vector of outputs with size [*n*_{y},1].

Matrix equations (3b) describe vector of predicted system outputs on the control horizon of length* N*. Vectors and terminal state** x**(*k+N+1*) depend on the actual state** x**(*k*) and on a vector of future inputs .

Matrices , , a depend on the state space model parameters according to (4c).

With respect to a terminal state in the cost function (5) in time instant* k+N+*1, the input vector has to be of length* k+N* and thus the vector is marked as . On the other hand the last item in the vector does not influence output vector . Because of this the last column of the matrix (4c) is filled with zeros.

The cost function in matrix form (5) changes from (1b) because of the terminal state application as a deviation from the desired terminal state and the vector of manipulated variable is calculated as a deviation from the supposed future inputs .where *N* is length of control horizon, is desired terminal state, is vector of future set-points with size [*N*×*n*_{y},1], is vector of supposed future inputs with size [(*N+1)*×*n*_{u},1], is vector of optimal future inputs with size [(*N+1)*×*n*_{u},1], is terminal state ∆**x** weighting matrix with size [*n*_{x},* n*_{x}], **Q** is control error weighting matrix with size [*N*×*n*_{y},* N*×*n*_{y}] and **R** is manipulated variable weighting matrix with size [(*N+1)*×*n*_{u}, (*N+1)*×*n*_{u}].

First item of the vector is applied as a control action** u**(*k*) every time instant and whole procedure is repeated. Constant vector filled with values of** u**(*k-1*) is used as supposed future inputs (vector ) in the following simulations. Another possibility how to choose the supposed future input vector is to use shifted vector from the previous calculation step. Both approaches are identical in principle, but the control response differs because of the effect of changed weighting proportions.

##### 3.1. Desired Terminal State

Computation of the desired terminal state is trivial in case of the system with identical number of inputs and outputs and if we consider steady state. The controlled system steady state behaviour is given by

The solution for the desired output** y**_{0}=**w**_{0} is

#### 4. Simulated Experiments

The aim of the following control simulations is to demonstrate the effect of the terminal state in predictive controller design to the control quality and stability. The simulations are supposed as an ideal case; controlled system is identical with the process model used for the controller design and neither noises nor disturbances are considered. The controller is designed for the set-point tracking task.

Two different controlled systems are treated in the simulations. The first system is a standard system of a higher order (8a) and the second one is a system with nonminimum phase (8b), with unstable zero. Both systems have similar settling time (cca. 50 s). The step and impulse responses of both systems are in Figure 1