Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

Abstract

This paper presents a novel decentralized filtering adaptive constrained tracking control framework for uncertain interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, a piecewise constant adaptive law will generate total uncertainty estimates by solving the error dynamics between the host system and decentralized state predictor with the neglection of unknowns, whereas a decentralized filtering control law is designed to compensate both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. In the control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low-pass filters, while the trade-off between tracking and constraints violation avoidance is formulated as a numerical constrained optimization problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. The uniform performance bounds are derived for the system states and control inputs as compared to the corresponding signals of a bounded closed-loop reference system, which assumes partial cancelation of uncertainties within the bandwidth of the control signal. Compared with model predictive control (MPC) and unconstrained controller, the proposed control architecture is capable of solving the tracking control problems for interconnected nonlinear systems subject to constraints and uncertainties.

Decentralized adaptive neural fixed‐time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

“…Theorem 1. Consider the interconnected nonlinear systems (2) with time-varying output constraints. Under the controller and adaptive laws ( 15)- (25), if Assumptions 1-3 are satisfied, the control objectives are achieved in fixed time.…”

“…During the few decades, the adaptive backstepping control method has attracted much attention in the control field (see References 1‐12). By incorporating fuzzy logic systems (FLSs) 13‐16 or radial basis function neural networks (RBFNNs) 17‐21 into the backstepping control method, the control issue of nonlinear systems containing uncertain functions and unknown parameters can be effectively solved.…”

Distributed extended object tracking information filter over sensor networks

“…Potential future works are devoted to investigating extensions of the two pair linear measurement models, for example, state estimation over time-varied networks, 44 heterogeneous networks, 45 and state estimation under multiple constraints. 46,47 How to cite this article: Li Z, Liang Y, Xu L, Ma S. Distributed extended object tracking information filter over sensor networks. Int J Robust Nonlinear Control.…”

Distributed H∞ fuzzy filtering for nonlinear systems interconnected over graphs

“…With the popularity of large scale interconnected systems, many theories on decentralized fuzzy filtering and control problems have been increasingly established by a set of coupled LMIs approach 29‐36 . However, these decentralized fuzzy filtering and control methods ignore the effect of the spatially interconnection structures of large scale interconnected systems on the stability of the whole system.…”

“…With the popularity of large scale interconnected systems, many theories on decentralized fuzzy H ∞ filtering and control problems have been increasingly established by a set of coupled LMIs approach. [29][30][31][32][33][34][35][36] However, these decentralized fuzzy H ∞ filtering and control methods ignore the effect of the spatially interconnection structures of large scale interconnected systems on the stability of the whole system. In References 37-39, the problem of nonlinear distributed control is considered for continuous-time networked nonlinear heterogeneous systems with respect to state feedback.…”