Abstract
Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included.
Highlights
In the control discipline, some systems are MIMO in their nature; the control theories for such systems will notice direct applications in a wide assortment of fields, such as space innovation, electric machines, and robotic control
The coupling inputs is included in the generalized disturbance Fi0 which is expressed as, the MIMO nonlinear system is written in a simple form given p p
To validate the proposed scheme performance for nonlinear MIMO system, we examine the following nonlinear multi-input-multi-output system
Summary
Some systems are MIMO in their nature; the control theories for such systems will notice direct applications in a wide assortment of fields, such as space innovation, electric machines, and robotic control. The control of MIMO systems is a challenging task because of the state and input couplings. If the MIMO systems are uncertain and nonlinear, the control task turns out to be more challenging. In this respect, theoretical outcomes and beneficial practices for structuring satisfactory controllers are tremendously scarce. In [18], a novel decentralized optimal control strategy was developed using the online learning of neural networks to stabilize a class of continuous-time nonlinear interconnected large-scale systems. A linear periodic controller with decentralized and centralized settings that provide linear quadratic regulator (LQR) optimal performance is demonstrated in [19]
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