Learning-Based Distributed Model Predictive Control Approximation Scheme With Guarantees

Transitioning from traditional electrical grids to smart grids is currently an ongoing process that many nations are striving for due to their access to renewable resources. Energy management is one of the key parameters that decides the performance of such complex systems. Distributed Model Predictive Control (DMPC) is a promising technique that can be used to improve the energy management of grid-connected systems. This paper analyzes a grid-connected inverter system with DMPC that exchanges key operating parameters with the grid to optimize coordinated power sharing between its respective loads. The state-space model for the inverter is derived and verified to ensure controllability and observability. A state observer for an inverter system is then developed to estimate the nominal states in the derived state-space model. The system performance is evaluated with MATLAB simulation by implementing load disturbances, which validate the effectiveness of the proposed power management control algorithm.

Distributed Model Predictive Control with Event-Based Optimization

In distributed model predictive control (DMPC), where a centralized optimization problem is solved in distributed fashion using dual decomposition, it is important to keep the number of iterations in the solution algorithm small. In this technical note, we present a stopping condition to such distributed solution algorithms that is based on a novel adaptive constraint tightening approach. The stopping condition guarantees feasibility of the optimization problem and stability and a prespecified performance of the closed-loop system.

A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark

This paper proposes a dual decomposition method for solving distributed model predictive control. This controller is designed for systems subject to communication constraints, in which nonlinear subsystems interconnected via dynamics and by constraints. The interconnections are relaxed by using gradient method, accelerated gradient and alternating direction methods of multipliers. Also, an event-based communication is proposed to handle the issue of communication constraints especially in embedded systems. In the proposed event-based communication strategy, each controller solves the optimization problem and communicate only if the prices are updated significantly, which can reduce the computation load and release the burden of the network while achieving global performance. Finally, the simulations study of the four-tank benchmark is presented to demonstrate the effectiveness of the proposed schemes.

A distributed approach to model-predictive control of radiant comfort delivery systems in office spaces with localized thermal environments

This study proposes a novel distributed Model Predictive Control (MPC) framework incorporating virtual tube constraints to address the dual challenges of safe navigation and efficient target attainment for Unmanned Aerial Vehicle (UAV) swarms in complex low-altitude traffic environments. An adaptive neighbor interaction mechanism is proposed that dynamically adjusts based on real-time perception and communication capabilities, enabling effective swarm coordination regardless of communication availability. Then, a multi-objective optimization formulation is constructed integrating maximum velocity pursuit, trajectory tracking precision and lateral deviation minimization within virtual tube boundaries. To ensure the stability of terminal velocity, an adaptive speed weight adjustment mechanism was specifically designed. Thus, a computationally efficient distributed MPC is designed utilizing dynamic local state information on switching topology. Through rigorous Lyapunov-based stability analysis and constraint satisfaction proofs, we establish the theoretical guarantees for both asymptotic stability and recursive feasibility of the proposed algorithm. Extensive numerical simulations demonstrate superior performance in complex traffic scenarios, showing faster target convergence while maintaining safe inter-agent distances and lateral deviation margins across various communication-restricted conditions.

Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way. Compared to a centralized MPC setup, where a global optimal control problem must be solved on-line with respect to all actuator commands given the entire set of states, in DMPC the control problem is divided into a set of local MPCs of smaller size, that cooperate by communicating each other a certain information set, such as local state measurements, local decisions, optimal local predictions. Each controller is based on a partial (local) model of the overall dynamics, possibly neglecting existing dynamical interactions. The global performance objective is suitably mapped into a local objective for each of the local MPC problems.

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

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

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.

This paper deals with the distributed model predictive control problem in multi-agent systems under fixed interaction topologies. we propose a distributed model predictive control schemes with guaranteed closed-loop stability. In this distributed structure, the problem is decomposed into a set of distributed optimal control problems. Simulation results show that the proposed scheme is effective under fixed interaction topologies among subsystems.

A distributed model predictive control scheme that allows for the communication links between subsystems being either fully connected or partially connected, or totally disconnected is presented in this paper. By incorporating the inner product of manifest variables into the supply rate, an extended input-to-power-and-state stabilisability (IpSS) condition is firstly derived for nonlinear interconnected systems. Dynamic stabilising constraints are subsequently developed for subsystem predictive controllers based on a virtual perturbed-state-feedback strategy that accepts changes in the communication network. The stabilising constraint is rendered by a dissipation-based constraint with respect to manifest variables for nonlinear input-affine systems.

In its present form, for a fixed plant model GPC (generalized predictive control) guarantees stability only for two specific choices of horizons in the cost function. In one instance, GPC has been shown to result in a deadbeat control law while in the other, the control law has been shown to converge to that given by the solution of the corresponding algebraic Riccati equation. However a lower bound on the costing horizon that results in a stabilizing controller is not known a priori. This paper presents sufficient conditions for stability of closed loop systems that result from implementing solutions of the finite horizon LQ problem for arbitrary fixed costing horizons. On this basis, a class of predictive control laws that ensures frozen-time stability of the closed loop system is proposed. When the plant is required to track a known reference signal that is bounded, the sufficient conditions for frozen-time stability of the closed loop are used to derive a controller structure that guarantees the tracking error to be bounded.

This article investigates denial-of-service (DoS) attacks and bandwidth limitations in vehicle platooning, addressing the demand for resilient security solutions in connected vehicle electronics. To this end, a dynamic event-triggered distributed model predictive control (DMPC) algorithm with a disturbance compensation mechanism is proposed. Specifically, a disturbance observer is integrated into the DMPC problem to actively compensate for state prediction deviation caused by external disturbances. Meanwhile, a novel dynamic event-triggered mechanism that accommodates DoS attacks is developed to determine whether to solve the DMPC problem. The proposed event-triggered mechanism can dynamically adjust the threshold parameter to reduce the communication and computational overheads. Particularly, considering the incompleteness of information interaction and the limited horizon of neighboring state predictions caused by DoS attacks and asynchronous communication, an information prediction strategy is established. The feasibility of the optimization problem and the stability of the platoon system are strictly analyzed. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation and comparison results.