This paper considers the presence of movable bridges in inland waterway transport, and presents a control framework for the joint dynamic coordination of bridge operations and autonomous vessel navigation to minimize waiting times of vessels at bridges. Simultaneous evolution of bridge occupancy and vessel position is captured by a control-oriented model that incorporates qualitative behavior in the form of propositional logic expressions. A model predictive control (MPC) strategy is designed considering adaptable bridge opening regimes to exploit vessel passage demand, and operational preferences of both vessel skippers and bridge operators are taken into account to reach fair trade-off decisions. A realistic case study pertaining to the Rhine-Alpine corridor is used to demonstrate the effectiveness of the approach. Appropriate key performance indicators (KPIs) are defined and employed for a quantitative comparison with a mixed-integer programming (MIP) strategy with fixed opening regimes. Furthermore, sensitivity to the main MPC parameters is examined by carrying out extensive testing to assess the effect of each design parameter on the solution.

This article presents the design of a control strategy for the Calais canal, a navigation canal located in a lowland area in northern France that is affected by tides. Moreover, the available actuators are discrete-valued and the hierarchy of operational objectives is time-varying. All these circumstances render water level regulation of the Calais canal a challenging problem. In view of this situation, the design of the overall control architecture is divided into a sequence of structured tasks, which are distributed among layers. The upper layer determines the current operating mode based on the analysis of several environmental and operational aspects. Information regarding the current mode is taken into account at the intermediate layer to select the appropriate optimization-based control problem, which is solved using lexicographic minimization. The optimal control setpoints are determined and sent to the lower layer, where scheduling problems are solved to select low-level control actions from a finite set to minimize the mismatch with respect to the optimal setpoints. Different realistic simulation scenarios are tested to demonstrate the effectiveness of the proposed approach.

This paper presents the design of a model predictive scheduling strategy to address the inland waterborne transport (IWT) problem considering bridges that must open to enable vessel passage. The main contribution is the formulation of a control-oriented model of the problem, including propositional logic expressions that characterize system behavior and their conversion into (in)equality constraints. The resulting model is embedded into a predictive scheduling approach to determine bridge opening timetables and vessel passage times in a coordinated manner. The effectiveness of the strategy is demonstrated on a realistic case study based on the Rhine-Alpine corridor.

This work presents the design of a combined control and state estimation approach to simultaneously maintain optimal water levels and maximize hydroelectricity generation in inland waterways using gates and ON/OFF pumps. The latter objective can be achieved by installing turbines within canal locks, which harness the energy generated during lock filling and draining operations. Hence, the two objectives are antagonistic in nature, as energy generation maximization results from optimizing the number of lock operations, which in turn causes unbalanced upstream and downstream water levels. To overcome this problem, a two-layer control architecture is proposed. The upper layer receives external information regarding the current tidal period, and determines control actions that maintain optimal navigation conditions and maximize energy production using model predictive control (MPC) and moving horizon estimation (MHE). This information is provided to the lower layer, in which a scheduling problem is solved to determine the activation instants of the pumps that minimize the error with respect to the optimal pumping references. The strategy is applied to a realistic case study, using a section of the inland waterways in northern France, which allows to showcase its efficacy.

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.