## 1 Introduction
### Related Works
The authors in Reference [47] presented a fuzzy-based disturbance observer for MIMO nonlinear systems and demonstrated it for the output feedback control of a permanent Magnet Synchronous Motor (PMSM). In Reference [48], an adaptive fuzzy controller for uncertain MIMO nonlinear systems with unknown dead-zones and a triangular control is proposed. In Reference [49], an intelligent adaptive control system is proposed for MIMOuncertain nonlinear systems. In Reference [50], an adaptive fuzzy decentralized control is designed for interconnected nonlinear systems. In Reference [51], an adaptive fuzzy tracking control is designed for uncertain nonlinear MIMO systems with the external disturbances. A novel adaptive Neural Network(NN) control is proposed in Reference [52] for uncertain MIMO nonlinear time-delay systems. In Reference [53], an adaptive fuzzy backstepping dynamic surface control output feedback control is designed for MIMO strict-feedback nonlinear systems with time-varying disturbances and unmeasured states. A neural network adaptive controller is suggested in Reference [54] for MIMO systems with unknown nonlinear function. In Reference [55], two stable adaptive neural control schemes for MIMO nonlinear systems are presented. The authors in Reference [56] developed a novel data-driven multivariate nonlinear controller design for MIMO nonlinear systems via virtual reference feedback tuning and neural networks. In Reference [57], a prescribed performance tracking control is designed for MIMO nonlinear systems with immeasurable states and unknown control direction. Finally, the authors in Reference [58] designed an event-triggered adaptive fuzzy control for MIMO switched nonlinear systems to estimate the whole information of the system states. Although the previously mentioned studies of adaptive control strategies have a lot higher influence and fundamentally improve the capability of a feedback control system in managing uncertainties outside robust control, this improvement is compelled by the variation rates of the uncertain coefficients. Moreover, the performance exacerbates immediately if these variation rates exceed a specific boundary. These conventional strategies in dealing with deterministic adaptive control have some natural limitations [59; 60]. Most surprisingly, if the unknown parameters fluctuate in a complicated fashion, it may be quite challenging to build up a \"continuously parameterized\" class of contender controllers. Furthermore, regular adjustment over some undefined time frame may be a strenuous task. These matters become exceptionally cruel if robustness and superior performance are in demand. In this manner, the strategy of adaptive control methods incorporates a generous number of specific steps and often depends on experimentations [39]. Far from adaptive control, the sliding mode control approach is thought to be one of the proficient techniques to build powerful controllers for highly complicated nonlinear plants with the opportunity of stabilizing some nonlinear systems that can't be stabilized by continuous state feedback laws. The significant obstruction in the success of these methods is the chattering phenomenon, which makes the actuators needed to adapt to the high-frequency control activities that could produce premature wear and tear. In this work, for controlling highly coupled MIMO nonlinear systems with external disturbances, internal unmodelled dynamics, and uncertainties, a proposed control scheme was proposed, namely, the decentralized control scheme based on improved ADRC (IADRC) configuration. This ADRC scheme is systematic and achieves, in large scale, the estimation/cancelation of the generalized disturbance in a single shot. The proposed scheme does not need extensive parameter tuning as in the case of neural network-based adaptive control techniques. Moreover, the chattering in the control signal, a common phenomenon in the sliding mode control, was avoided in the proposed scheme.