# Methods of Pre-Identification of TITO Systems
## 1 Introduction  
Most processes in practice are processes that have multiple inputs and multiple outputs, and these are influenced by interactions. These systems can be controlled by a centralized or decentralized controller. The main advantages of decentralization include simplifying the overall task by dividing it into a set of sub-tasks. Decentralized control is very often used in practice. Decentralized charge control of electric vehicles is a nice application. There was introduced a fully decentralized and participatory learning mechanism for privacy-preserving coordinated charging control of electric vehicles that regulates three Smart Grid socio-technical aspects: (i) reliability, (ii) discomfort and (iii) fairness [1]. Another good application of decentralized control is a quadrocopter, namely outdoor flocking of quadcopter drones with decentralized model predictive control [2]. From the theoretical point of view, there was proposed a decentralized explicit (closed-form) iterative formula that solves convex programming problems with linear equality constraints and interval bounds on the decision variables [3], or decentralized control problem for non-affine large-scale systems with nonaffine functions possibly being discontinuous [4], or decentralized adaptive tracking control strategy consisting of a steady-state controller andmodified optimal feedback controller. Design parameters-dependent feasibility conditions were formulated by using Lyapunov theory to guarantee the existence of the proposed decentralized control scheme [5]. Decentralized voltage control is another example of a decentralized strategy. It includes network partitioning strategy for the optimal voltage control of Active Distribution Networks actuated by means of a limited number of Distributed Energy Storage Systems [6]. Another paper is concerned with the problem of decentralized event-triggered dynamic output feedback control for large-scale systems with unknown interconnections. By using a modified cyclic-small gain condition and introducing a free-matrix-based integral inequality, a sufficient condition was derived to ensure that the overall closed-loop system is asymptotically stable with a prescribed Hoo performance [7]. There was also implemented fuzzy decentralized control, for example an adaptive fuzzy decentralized control approach for a class of nonlinear systems with unknown control direction and different performance constraints. In the control design, the different performance constraints, that were the prescribed performance error constraints for some subsystems and the asymmetric time-varying output constraints for the others, could be unified as one form by selecting proper performance functions [8]. Adaptive control is another area that has expanded the use of decentralized control, and here are at least a few such examples. A minimal-neural-networks-based design approach was presented for the decentralized output feedback tracking of uncertain interconnected strict-feedback nonlinear systems with unknown time varying delayed interactions unmatched in control inputs [9]. The decentralized output-feedback adaptive backstepping control scheme was also proposed for a class of interconnected nonlinear systems with unknown actuator failures by introducing a kind of high gain K-filters [10], or decentralized output-feedback adaptive control scheme for a class of interconnected nonlinear systems with input quantization. Both logarithmic quantizers and improved hysteretic quantizers were studied, and a linear time-varying model was introduced to handle the difficulty caused by quantization [11]. A decentralized adaptive backstepping control scheme was also proposed for a class of interconnected systems with nonlinear multisource disturbances and actuator faults. The nonlinear multisource disturbances comprised two parts: one was the time-varying parameterized uncertainty; the other was the dynamic unexpected signal formulated by a nonlinear exogenous system [12].