This paper proposes an iterative distributed stochastic economic model predictive control (MPC) algorithm for linear stochastic systems subject to dynamic couplings and coupled inequality constraints. To handle chance constraints induced by potentially unbounded stochastic disturbances, an indirect feedback mechanism based on a tube-based MPC framework is employed. Then, the concept of probabilistic reachable sets is utilized to tighten the constraints, allowing the chance constraints to be reformulated as time-varying deterministic constraints. Moreover, to enable parallel computation of the distributed optimization problem, the original problem is reformulated as a tractable deterministic one with a set of consensus constraints, which is subsequently solved using the alternating direction method of multipliers. Finally, input-to-state stability in probability of the closed-loop system is established through the construction of a suitably designed Lyapunov function. Numerical examples illustrate the efficacy of the algorithm.
Efficient nitrogen supply is central to sustainable crop production and precision agriculture. This paper develops a distributed nonconvex optimization framework for nitrogen fertilizer management in field crops. A predictive control model with crop nitrogen dynamics is formulated; binary fertilizer on–off decisions make the resulting optimization problem nonconvex. A network-based recurrent neural network (RNN) is constructed, and its convergence to a Karush–Kuhn–Tucker (KKT) point of the formulated model is established. A collaborative neurodynamic mechanism is then used to run multiple distributed RNNs with a population update, so that KKT candidates from different initial states can be compared and refined. In the numerical tests, the computed schedules satisfy the nitrogen constraints and increase yield while reducing nitrogen use. Simulations on a 100-field case and a large-scale 1000-field case over a 30-day horizon verify the proposed framework.
This paper investigates dynamic positioning event-triggered control for unmanned marine vehicle (UMV) systems subject to denial-of-service (DoS) attacks and fading channels. First, the nonlinear UMV system is established as a T-S fuzzy model. Then, an adaptive event-triggered mechanism (ETM) is proposed to optimize system resource utilization. A proper T-S fuzzy extended state observer (ESO) that depends on the DoS attack pattern is constructed to address the unavailability of state information and to estimate unknown disturbances. Based on the estimated information from the ESO, an observer-based fuzzy switching controller is employed to guarantee system performance. Furthermore, sufficient conditions are provided for the mean-square exponential ultimately bounded of the closed-loop system. Finally, a simulation example is presented to verify the effectiveness of the proposed control architecture.
This paper investigates the distributed inverse optimal safety consensus problem for multi-robot systems subject to external disturbances. To address this, a novel hierarchical inverse optimal framework is constructed with two layers. In the upper layer, a distributed optimal trajectory generator is developed, which generates the nominal trajectories for each robot. In the lower layer, an inverse optimal safety controller is presented based on input-to-state safety high-order control barrier functions, including a nominal robust consensus controller and an inverse optimal safety filter. By means of both layers, the multi-robot systems asymptotically achieve infinite-horizon optimal safety consensus by maximizing a meaningful cost functional in the presence of disturbances. Simulations demonstrate the effectiveness of the proposed control strategy.
This paper delves into the demand tracking control problem for a class of coupled re-entrant manufacturing networks (RMNs) utilizing adaptive event-triggered control strategies. The network modeling progresses hierarchically: 1) characterizing the local mass conservation law via explicit dynamical equation at the individual node level; 2) extending to the global mass conservation law for a single RMN; 3) generalizing to a comprehensive global mass balance formulation for a class of coupled RMNs. This multi-scale modeling strategy, transitioning from the local node dynamics to the network-level interactions, yields a rigorous hyperbolic-PDE-based continuum approximation model that efficiently captures the spatiotemporal evolutions of production flows in interconnected manufacturing networks. To reduce unnecessary control actions while maintaining precise regulation of the work-in-progress (WIP) quantity, two novel adaptive event-triggered control schemes incorporating the node-level and the edge-level coordination mechanisms, respectively, are developed for the coupled RMNs. These adaptive triggering mechanisms dynamically adjust triggering thresholds and control gains online, with Lyapunov-based analysis providing sufficient conditions guaranteeing asymptotic tracking of the desired production profiles and exclusion of the Zeno behavior. Numerical simulations validate the effectiveness of the control schemes.
