Bandwidth-Aware Transmission Scheduling and Event- Triggered Distributed MPC for Vehicle Platoons

A tube-based distributed model predictive control (DMPC) scheme is proposed for dynamically coupled linear systems. The control scheme is designed to guarantee local performance even when neighboring controllers are not complying with the requirements of the algorithm (e.g., they are malicious or faulty). The resulting conservativeness is minimized, for controllers aim to minimize their state and input constraint sets to reduce mutual disturbances. Also, sufficient conditions for feasibility and exponential stability are given. Finally, these ideas are illustrated and assessed with respect to other robust DMPC via a simulated example.

Using aggregated heterogeneous thermostatically controlled loads (TCLs) to provide ancillary services in a smart grid is a promising technique. This paper proposes an improved population model with lockout time considered to describe the aggregate dynamics. Based on that, a distributed model predictive control (DMPC) scheme is investigated to control a population of TCLs for the regulation service. Full derivations of the system model and the DMPC scheme are provided. A flexibility penalty is developed and added to the objective function to preserve the regulation flexibility. Simulation results, tested by real data, reveal the feasibility and effectiveness of the proposed method.

This paper presents a dual-mode distributed model predictive control (DMPC) strategy for platooning of connected vehicles with nonlinear dynamics. A third-order nonlinear model is employed to describe the dynamic characteristics of vehicles. In order to deal with the system nonlinearity and enhance the control precision, a DMPC based optimization problem is formulated for the vehicle platoon control, in which the nonlinear dynamics and the input boundaries are both considered as its constraints. Then, a dual-mode structure with the control scheme from the optimization and a local state feedback controller is proposed to drive the vehicles to the desired platoon. Comparing with other vehicle platoon algorithms, the proposed dual-mode DMPC strategy can significantly reduce the computational burden and save the communication resources. Furthermore, the iterative feasibility and the stability of proposed control system are strictly analyzed. In final, numerical simulations are provided to validate the effectiveness of proposed approaches.

Distributed Model Predictive Control with Asymmetric Adaptive Terminal Sets for the Regulation of Large-scale Systems

Vehicle platoon, as an important application of connected automated vehicles (CAVs), is a hot topic in the intelligent transportation systems (ITSs). Most existing results on platoon control used the exact feedback linearization method to transform nonlinear vehicle dynamics into linear ones, which required complete prior knowledge of parameters. As the measurement errors and the environment disturbance exist, the accurate parameters are difficult to obtain in real world. This paper considers a nonlinear uncertain vehicle platoon under a network topology (NT) modeled by a spanning tree in a digraph. Radial basis function neural networks (RBFNNs) are used to approximate the uncertain function and an adaptive distributed controller is designed by backstepping control method. To reduce the update rate of the controllers, an event-triggered control scheme is proposed with two different kinds of triggering conditions. It is proven that vehicle platoon practical consensus can be achieved under the Zeno-free adaptive event-triggered control scheme. Moreover, for bidirectional (BD) NT and two predecessor-following (TPF) NT, vehicle platoon reaches the string stability. Finally, a vehicle platoon under three different kinds of NTs is simulated to demonstrate the effectiveness of the theoretical results.

This study proposes a distributed model predictive control (DMPC) scheme based on population games for a system formed by a set of sub-systems. In addition to considering independent operational constraints for each sub-system, the controller addresses a coupled constraint that involves the sum of all control inputs. This constraint models an upper bound on the total amount of energy supplied to the plant. The proposed approach does not need a centralised coordinator when having a coupled constraint involving all the decision variables. The proposed methodology, which takes advantage of evolutionary game theory concepts, provides an optimal solution for the described problem. Moreover, it is shown that the methodology has plug-and-play features, i.e. for each already designed local MPC controller nothing changes when more sub-systems are added/removed to/from the global constrained control problem. Furthermore, the stability analysis of the proposed DMPC scheme is presented.

Sampled-data output feedback control based on a new event-triggered control scheme

Two-stage inverters composed of the front DC-DC stage and the rear DC-AC stage are broadly employed for the current tracking. Structures vary according to the different control objectives and application occasions. The concerning structure in this paper is made up of the buck-boost converter stage and the single-phase inverter stage. The simulation results show that the intermediate voltage will affect the total harmonic distortion (THD) values. Moreover, under the same other conditions, a minimum THD can be attained by regulating the intermediate voltage. In this paper, a time-varying observer is introduced for the interface parameters identification based on the robustness of model predictive control (MPC) scheme. Combined with the input voltage, the reference current and grid voltage, the optimal intermediate voltage with lower THD can be determined. Besides, a cascaded proportional-integral (PI) MPC scheme is adopted for the intermediate voltage tracking without steady-state error. Meanwhile, based on the accurate interface parameters obtained by the observer, an adaptive MPC scheme is applied for the output current tracking. The simulation results on MATLAB/Simulink and experimental results on FPGA test bench validate the accuracy of the voltage determination and the effectiveness of the control scheme.

Partitioned Based Cooperative Distributed Model Predictive Control for Large-Scale Nonlinear Systems

A Study On Distributed Model Predictive Consensus

This paper presents a distributed model predictive control(DMPC) strategy to achieve cooperative tasks like consensus and synchronization of linear systems with coupled constraints. Based on the error of state between each subsystem and the center of its cooperative set, a novel DMPC algorithm is designed with its robust feasibility guaranteed by permitting only one subsystem to optimize at each time instant, while renew other subsystems' plans. Illustrative examples are finally conducted to verify the effectiveness of the DMPC strategy.

This paper studies the optimal control problem for discrete-time switched linear systems with quadratic cost. A model predictive control (MPC) scheme is proposed which results in closed-loop strategies for both switching and control inputs. To reduce the online computation and ensure stability of the MPC scheme, a two-stage pruning algorithm is constructed which is performed offline. The resulting MPC scheme ensures exponential stability, and the cost function is suboptimal. Stability, feasibility, and suboptimality of the MPC scheme are studied. Simulation results are given for the MPC scheme which shows the proposed approach results in reduced computation and acceptable performance.

Game Theory Based Distributed Model Predictive Control for a Hydro-Power Valley Control

This brief proposes a distributed model predictive control scheme based on satisficing theory in which the relative importance of each subsystem changes dynamically according to the current conditions. The controllers implement a situational altruism, where less satisfied controllers will automatically receive more importance. The global cost, instead of being fix as in other approaches, emerges from the cooperation process. The proposed control scheme guarantees both that the control input is Pareto-optimal and that each controller is guaranteed a minimum degree of optimality, even under coupled constraints. The main properties of the proposed approach are demonstrated through a traffic control example.

In this paper, we present a distributed model predictive control strategy for discrete-time interconnected linear systems. The overall system is composed of a number of discrete-time interconnected linear subsystems. The MPC controllers of subsystems communicate their information to handle the interconnection effects. It is known that distributed MPC relies on communication networks to transmit and share information among subsystems. Communication delays, resulting in delayed information exchange among subsystems, may inhibit the use of any distributed MPC approach. To address this problem, a delay handling mechanism is employed. Besides, it is showed that the proposed method guarantees input-to-state stability characterization for both local subsystems and the global system under some predetermined assumptions. The simulation results are exploited to illustrate the effectiveness of the proposed method.