Dissipativity based distributed model predictive control for process network reconfiguration

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.

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

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 concerns a distributed model predictive control (DMPC) strategy in which each controller views the signals from other subsystems as disturbance inputs in its local model. The DMPC controllers exchange predictions on the bounds of their state trajectories and incorporate this information into their local DMPC problems. They also impose their own predicted state bounds as constraints in subsequent DMPC iterations to guarantee their subsystem satisfies the bounds broadcast to the other controllers. Each controller solves a local min-max problem on each iteration to optimize performance with respect to worst-case disturbances. Parameterized state feedback is introduced into the DMPC formulation to obtain less conservative solutions and predictions. The paper presents sufficient conditions for feasibility and stability. The approach is illustrated with an example.

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

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.

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.

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

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.

Iron precipitation is a key process in zinc hydrometallurgy. The process consists of a series of continuous reactors arranged in descending order, overflowing zinc leach solution from one reactor to the next. In this paper, according to the law of mass conservation and the reaction kinetics, a continuously stirred tank reactor model of a single reactor is first established. Then, a distributed model of cascade reactors is built with coupled control based on the single reactor model, considering the unreacted oxygen in leaching solution. Secondly, four reactors in the iron precipitation process are considered as four subsystems, the optimization control problem of the process is solved by a distributed model predictive control strategy. Moreover, the control information feedback between successive subsystems is used to solve the optimization problem of each subsystem, because of the existing control coupling in their optimization objective function of pre and post subsystems. Next, considering the intractability of the optimization problem for subsystems with various constraints, a distributed dual iterative algorithm is proposed to simplify the calculation. With the consideration of its cascade structure and control couplings, the proposed algorithm iteratively solves the primal problem and the dual problem of each subsystem. The application case shows that distributed model predictive control based on dual iteration algorithm can handle coupled control effectively and reduce the oxygen consumption.

In this paper, an observer‐based event‐triggered distributed model predictive control method is proposed for a class of nonlinear interconnected systems with bounded disturbances, considering unmeasurable states. First of all, the state observer is constructed. It is proved that the observation error is bounded. Second, distributed model predictive controller is designed by using observed value. Meanwhile, the event‐triggered mechanism is set by using the error between the actual output and the predicted output. The setting of event‐triggered mechanism not only ensures the error between the actual output and the predicted output within a certain range, but also reduces the calculation amounts of solving the optimization problem. The states of each subsystem enter the terminal invariant set by distributed model predictive control, and then are stabilized in the invariant set under the action of output feedback control law. In addition, sufficient conditions are given to ensure the feasibility of the algorithm and the stability of the closed‐loop system. Finally, the numerical example is given, and the simulation results verify the effectiveness of the proposed algorithm.

A Study On Distributed Model Predictive Consensus

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.

Model predictive control for energy management in buildings Part 2: Distributed Model Predictive Control