Distributed Implementation Algorithm Research Articles

Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

Cooperative control of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic, where human-driven vehicles with unknown dynamics coexist, data-driven predictive control techniques allow for CAV safe and optimal control with measurable traffic data. However, the centralized control setting in most existing strategies limits their scalability for large-scale mixed traffic flow. To address this problem, this paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) formulation and its distributed implementation algorithm. In cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple subsystems with one single CAV, which collects local trajectory data for subsystem behavior predictions based on the Willems’ fundamental lemma. Meanwhile, the cross-subsystem interaction is formulated as a coupling constraint. Then, we employ the Alternating Direction Method of Multipliers (ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation. Our simulations on different traffic scales verify the real-time wave-dampening potential of distributed DeeP-LCC, which can reduce fuel consumption by over 31.84% in a large-scale traffic system of 100 vehicles with only 5%–20% CAVs.

Distributed implementation of nonlinear model predictive control for AUV trajectory tracking

This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV). We investigate the nonlinear model predictive control (NMPC) method looking for possible approaches to alleviate the heavy computational burden. Novel distributed NMPC algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately decomposing the original optimization problems into smaller size subproblems and then solving them in a distributed manner, the expected floating point operations (flops) can be reduced dramatically. We show that the convergence of the AUV trajectory can be guaranteed by the proposed contraction constraints in the decomposed subproblems. Recursive feasibility and closed-loop stability are proved. Taking advantage of the guaranteed stability, a real-time distributed implementation algorithm is further developed to automatically trade off between control performance and computational complexity. Extensive simulation studies on the Falcon AUV model demonstrate the effectiveness and robustness of the proposed approach.

Distributed Real-Time Power Balancing in Renewable-Integrated Power Grids With Storage and Flexible Loads

The large-scale integration of renewable generation directly affects the reliability of power grids. We investigate the problem of power balancing in a general renewable-integrated power grid with storage and flexible loads. We consider a power grid that is supplied by one conventional generator (CG) and multiple renewable generators (RGs) each co-located with storage,and is connected with external markets. An aggregator operates the power grid to maintain power balance between supply and demand. Aiming at minimizing the long-term system cost, we first propose a real-time centralized power balancing solution, taking into account the uncertainty of the renewable generation, loads, and energy prices. We then provide a distributed implementation algorithm, significantly reducing both computational burden and communication overhead. We demonstrate that our proposed algorithm is asymptotically optimal as the storage capacity increases and the CG ramping constraint loosens. Moreover, the distributed implementation enjoys a fast convergence rate, and enables each RG and the aggregator to make their own decisions. Simulation shows that our proposed algorithm outperforms alternatives and can achieve near-optimal performance for a wide range of storage capacity.

A Distributed Implementation Algorithm for Physical Layer Security Based on Untrusted Relay Cooperation and Artificial Noise

In this letter, we consider a cooperation system with multiple untrusted relays (URs). To keep the transmitted information confidential, we obtain joint channel characteristics (JCCs) through combining the channels from the source to the destination. Then, in the null space of the JCCs, jammers construct artificial noise to confuse URs when the source node broadcasts its data. Through a distributed implementation algorithm, the weight of each node can be obtained from its own channel state information. Simulation results show that high-level security of the system can be achieved when internal and external eavesdroppers coexist.

A Distributed Implementation Algorithm for Physical Layer Security Based on Untrusted Relay Cooperation and Artificial Noise

A Distributed Implementation Algorithm for Physical Layer Security Based on Untrusted Relay Cooperation and Artificial Noise