###### Abstract
An analytic solution to state-constrained optimum tracking control issues for continuous-time linear time-invariant (CT-LTI) systems using model-based prediction, the quadratic penalty function and the variational technique is presented in [14]. As a result, study [15] describes how to control the speed of a DC motor using a buck converter controlled using zero average dynamics and fixed point induction control (ZAD-FPIC) methods. Proportional-integral, fuzzy and adaptive neuro fuzzy controllers were developed for a DC servo motor in [16]. In [17] different metaheuristic algorithms for creating PID controllers for DC motor are investigated, and these algorithms and classical methodologies are thoroughly compared in order to identify the optimum methodology for PID controller design and parameter tuning. There are several applications of fuzzy logic that may be used for control reasons. Study [18] proposes a method for creating two-variable fuzzy logic controllers (FLCs). The equivalent relationship between a typical PID controller and a FLC was proposed and clearly identified in [19]. Furthermore, the analogous FLC designed in discrete form was presented and simulated, with system responses comparable to the original conventional PID controller. Fuzzy control based on the Takagi-Sugeno fuzzy model is used in this study. Research and results in the application of Takagi-Sugeno type fuzzy systems in the tasks of modeling and control of dynamic systems are given in [20; 21]. Review paper [22] gives an idea of past, present and future trends in the application of this type of system. Multiple articles on the TS discrete-time fuzzy models can be found in the literature, including [23; 24; 25]. The fuzzy model proposed by [PERSON] and [PERSON] [26] is described by fuzzy IF-THENrules, which depict local linear input-output relations of a nonlinear system. Paper [27] describes the construction of a fuzzy controller for the output-voltage regulation problem in switching converters based on a TS fuzzy model. Research [28] presents a unique method for constructing a TS fuzzy model of an unknown nonlinear system using experimental data. The observer-based fuzzy controller design technique for nonlinear discrete-time singular systems represented by TS fuzzy models is investigated in [29]. The second application of fuzzy systems, for example, is the PDC. In the work [30], a fuzzy controller is built using a PDC technique and applied in an experimental tank-level control system. In [31], a unique modification of the original PDC technique is proposed, such that, in addition to the stability issue, the system closed-loop performance may be addressed at the design stage. Initially, the nonlinearity can be successfully modeled by a number of local linear input-output connections. The observer-based fuzzy controller is then built using PDC technology and the proportional-derivative (PD) feedback scheme. Another study [32] provided a unified method for a nonlinear model following control that includes the regulation and servo control issues as specific examples. An overview of some papers dealing with various optimizations follows below. The challenge of distributed controller design for linear discrete-time systems is discussed in [33]. The solution to optimum control issues for limited discrete-time linear hybrid systems based on quadratic or linear performance criteria is investigated in [34]. The paper has two objectives. The first is to provide some fundamental theoretical conclusions about the construction of the optimum state-feedback solution and the objective function. The second is to show how to combine multiparametric programming with dynamic programming to create the state-feedback optimum control law. A comparison of the linear and nonlinear controllers is given in [35]. More precisely, a robust pole-placement discrete time controller for a linearized uncertain polytopic model is compared to a nonlinear continuous-time control based on feedback linearization.