Distributed Estimation and Nonlinear Model Predictive Control Using Community Detection

Set-membership-based distributed moving horizon estimation of large-scale systems

This work is concerned with the design of a two-step distributed state estimation scheme for large-scale systems in the presence of unknown-but-bounded disturbances and noise. The set-membership approach is employed to construct a compact set containing the states consistent with system measurements and bounded noise and disturbances. The tightened feasible region is then provided to a moving horizon estimator that determines the optimal state estimates. Partitioning of the overall problem and coordination of the resulting subproblems are achieved using decomposition of the optimality conditions and community detection. The proposed strategy is tested on a case study based on a reactor-separator system widely used in the literature. Its performance is compared to those of centralized and distributed (without set-membership) implementations, allowing to highlight its effectiveness.

Improved Constrained Predictive Functional Control Using Extended Non-Minimal State Space Formulation for the Cement Production Process

In this article, an improved constraint dealing method and an extended state space model-based constrained predictive functional control (PFC) approach is developed for the denitration process in cement production. To improve the control effect of the presented strategy, first, an enhanced form-based state space model in which the changes of the output predictions can be regulated to achieve smoother system dynamics is constructed. Then, a modified constraint dealing scheme, which integrates the relaxation factors to improve the success probability of solving the relevant optimization, is also introduced for the proposed method. On the basis of the two merits, better ensemble control performance is anticipated. Simulations on the regulation of the NOx concentration in the denitration process prove the validity of the proposed constrained PFC strategy.

Cooperative optimization-based distributed model predictive control for constrained nonlinear large-scale systems with stability and feasibility guarantees

This paper proposes a cooperative distributed model predictive control (DMPC) to control the constrained interconnected nonlinear large-scale systems. The main contribution of this approach is its proposed novel cooperative optimization that improves the global cost function of any subsystem. Each subsystem calculates its optimal control by solving the corresponding global cost function. For each subsystem, the global cost function is defined based on a combination of cost functions of all subsystems. If the sampling time is selected appropriately, then the feasibility of the proposed approach will be guaranteed. Furthermore, the sufficient conditions for stability and consequently, for the convergence of the whole system states towards the neighborhood of the origin's positive region are provided. The effectiveness and performance of the proposed approach are demonstrated via applying it to a nonlinear quadruple-tank system for both minimum-phase and nonminimum-phase models.