Decentralized Model Predictive Control Research Articles

A decentralized optimization approach to the power management of electric vehicles parking lots

To reduce the transportation sector's environmental impact, a transition to electric mobility is being witnessed worldwide. However, to avoid any issues on the grid, the charging process of electric vehicles (EVs) must be carefully managed. One valid solution to this problem is aggregating EVs and managing their collective charge and discharge. Parking lots equipped with charging stations are likely to become more prevalent in the future. Careful management of such infrastructures is essential to avoid local overloads and to provide flexibility to the electrical grid. In this context, this paper formalizes an optimization model for the optimal power management of a smart PL, with the objective of minimizing the power withdrawn from the grid while considering customers’ needs. The main novelties regard how the PL is modeled and how the solution to the optimization problem is achieved. In fact, as far as the modeling is concerned, both single- and three-phase EVs are considered, and batteries are modeled via a non-linear charging profile. As regards the solution method, an augmented Lagrangian-based decentralized model predictive control technique is proposed. The adopted algorithm has been tested on a smart PL in a Genova Municipality (Italy) and its performance has been evaluated by implementing two different scenarios: in the first one only 5 EVs are considered, while the second one regards a scalability analysis where the number of served EVs is increased to 40. The conducted tests confirm that the proposed algorithm can provide a computationally efficient solution, while preserving an accurate model.

Encrypted decentralized model predictive control of nonlinear processes with delays

This work focuses on enhancing the operational safety, cybersecurity, computational efficiency, and closed-loop performance of large-scale nonlinear time-delay systems. This is achieved by employing a decentralized model predictive controller (MPC) with encrypted networked communication. Within this decentralized setup, the nonlinear process is partitioned into multiple subsystems, each controlled by a distinct Lyapunov-based MPC. These controllers take into account the interactions between subsystems by utilizing full state feedback, while computing the control inputs only corresponding to their respective subsystem. To address the performance degradation associated with input delays, we integrate a predictor with each LMPC to compute the states after the input delay period. The LMPC model is initialized with these predicted states. To cope with state delays, the LMPC model is formulated using differential difference equations (DDEs) that describe the state-delays in the system. Further, to enhance cybersecurity, all signals transmitted to and received from each subsystem are encrypted. A stability analysis is carried out for the encrypted decentralized MPC when it is utilized in a time-delay system. Bounds are set up for the errors arising from encryption, state-delays, and sample-and-hold implementation of the controller. Guidelines are established to implement this proposed control structure in any nonlinear time-delay system. The simulation results, conducted on a nonlinear chemical process network, illustrate the effective closed-loop performance of the decentralized MPCs alongside the predictor with encrypted communication when dealing with input and state delays in a large-scale process.

Swarms of modular satellites decentralized guidance and target assignment strategy

This paper presents a decentralized guidance and target assignment (DGTA) strategy for reconfiguring swarms made up of hundreds of modules that have limited communication capabilities. This strategy provides a decentralized onboard computation method to address both near-optimal target assignment and collision-free trajectory planning for swarms of modules. To effectively estimate the transfer cost between modules and targets for target assignment, a multilayer perceptron (MLP) is developed via the backward generation method. The decentralized target assignment problem is solved by greedy algorithm according to the result of the efficient estimating solver. A decentralized model predictive control approach is proposed to guide swarm modules towards their assigned targets, while avoiding collisions with other modules. The feasibility of this guidance framework has been demonstrated through rigorous theoretical analysis. Numerical experiment confirms that the MLP-based method shows an accurate and real-time advantage for estimating transfer cost. Furthermore, the DGTA strategy has been shown to possess robustness and generalization ability, as evidenced by its successful application in a pinpoint assembly task involving 125 and 182 modules. The simulation results confirm the feasibility of this approach for in-orbit assembly application.

Integrated planning and control for formation reconfiguration of multiple spacecrafts: A predictive behavior control approach

This paper addresses the trajectory planning and control problem of multi-spacecraft formation reconstruction in the presence of obstacles. By expressing spacecraft dynamics relative to a leader spacecraft in the formation, an integrated planning and control approach named predictive null-space-based behavior control (PNSBC) is proposed. First, a planner using null-space-based behavior control (NSBC) is designed at an upper layer to resolve multi-task conflicts. Here, both global tasks, e.g. formation keeping, and local tasks, e.g. obstacle avoidance, are considered. Second, a tracking controller is designed at the bottom layer using decentralized model predictive control. Unlike traditional two-layer approaches that treat planning and control separately, the proposed PNSBC integrates the planning and control in two ways: 1) the planner provides reference trajectories for the controller to track; 2) the model predictive control (MPC) controller provides predicted trajectories that can be employed by the planner for future task priority predictions, which extends the capability of NSBC from one-step planning to multi-step predicting. In addition, the computational burden of the MPC controller is greatly reduced by putting the nonlinear obstacle avoidance constraints into the planner as a local task. Simulation results show that such integrated approach has better performance in terms of safety constraint guarantee, fuel consumption and travel distance when compared against traditional non-integrated approaches, or all-in-one MPC methods, while constraints and task objectives are fully satisfied.

A Longitudinal Velocity CF-MPC Model for Connected and Automated Vehicle Platooning

To optimize a vehicle platoon system in terms of car-following behavior, a decentralized model predictive control (MPC) strategy for longitudinal velocity control was established (namely, CF-MPC). Firstly, considering the influence of car-following behavior on vehicle states, a longitudinal velocity control model for platoons of connected and automated vehicles (CAV) was designed. Based on that model, an upper-level MPC controller was built to obtain the desired acceleration of the vehicles. Secondly, a lower-level controller received the desired acceleration signal and converted it into the expected throttle opening/braking pressure, to control acceleration/deceleration. Then, the Lyapunov stability method was used to detect the stability conditions that the model should satisfy. Finally, three simulation procedures—constant speed, acceleration, and deceleration were tested, and the validity of the CF-MPC method was verified from the perspectives of a model strategy and a control strategy. The simulation results show that with the proposed CF-MPC method, CAV platoons quickly completed velocity tracking and maintained a safe distance, thereby improving traffic efficiency, fuel economy, driving safety, and transportation capacity.

Agent-Based Decentralized Model Predictive Control for Plants With Multiple Identical Actuators

This article proposes a decentralized model predictive control (DMPC) algorithm without communication for systems consisting of multiple identical, independent actuators acting on a single central plant. The particular system design is relevant for applications where modularity is paramount and for highly dynamic systems, e.g., battery emulator systems controlled by multiple dc–dc converter modules. Each agent, consisting of an actuator and a DMPC, controls a virtually scaled version of the plant to implicitly consider the effects of other agents. The set of DMPCs achieves the same plant performance as a corresponding centralized model predictive controller (CMPC) in unconstrained operation. Also, the states of the independent agents converge toward the globally optimal CMPC solution. This is obtained by dividing the CMPC’s objective function into local objective functions related to the subsystems. The optimality and stability of the DMPC in unconstrained operation are shown analytically. The stability of control input-constrained operation is analyzed by computing the region of attraction. Numerical studies of a battery emulator system compare the performance of the DMPC with the global optimum in detail.

Distributed Scheme for Main Engine Power Control Systems of Small and Medium Sized Container Ships Based on Nash Equilibrium

As a main force of ocean shipping, the energy consumption of container ships is considerably high due to the frequent load shifts of main engine and other disturbances. In this study, for engine room power systems in intelligent container ships, including cooling water subsystems, fuel oil subsystems, and main engine subsystems, a coordinated control scheme based on a correlation model and a distributed model predictive control approach was proposed to reduce continual fluctuations of control and output values through optimisation in a negotiated manner. The correlation model considers the interconnections among the subsystems, and the control approach achieves Nash equilibrium solutions. A comparative simulation was carried out between the proposed method and the decentralized model predictive control. The result shows that the proposed method works better than the decentralized one with less fluctuations. This study provides a theoretical basis for integrated designs of power control systems in intelligent container ships.

Economic Potential for Hybrid Electric Vehicles in Urban Signal-free Intersections with Decentralized MPC

The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, providing convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains. This paper proposes a decentralized coordination strategy for a group of connected and autonomous vehicles (CAVs) with a series hybrid electric (sHEV) powertrain at urban signal-free intersections. The problem is formulated as a convex form with suitable relaxation and approximation of the powertrain model and solved by decentralized model predictive control (DMPC), which enables rapid search and a unique solution in real-time. Numerical examples validate the effectiveness of the proposed methods concerning physical and safety constraints. By utilizing the petrol fuel and battery charging prices over the last year, the performance of the proposed approach is evaluated against the optimal results produced by two benchmark solutions, conventional vehicles (CVs) and battery electric vehicles (BEVs). The comparison results demonstrate that the traveling cost of sHEVs approaches and, even under some circumstances, reaches the same level as for BEVs, which indicates the importance of hybridization, particularly under the current rising electricity price situation.

Guidance, Navigation, and Control of AUVs for Permanent Underwater Optical Networks

Ocean exploration is in its infancy with more than 80% of the oceans still unexplored. Contrary to space exploration, electromagnetic waves have seldom been used underwater because of their heavy absorption by seawater. In this article, we present guidance, navigation, and control systems to steer autonomous underwater vehicles (AUVs) such that a permanent optical network can be set up between the deep sea and the surface. The challenge is to guarantee that there is consistently a path through the AUV network to relay the signal for an unlimited time despite the finite battery's lifetime of the AUVs. To achieve this, the guidance system combines a decentralized model predictive control (D-MPC) with graph theory to compute the trajectory of each agent relaying the optical signal as well as of redundant agents forming local leader–follower formations with each relay acting as leader of its own subfleet. The D-MPC solves a constrained optimization problem where the communication and collision avoidance constraints are formulated to enable two successive leaders and one of their followers to remain within optical range and thus allowing to immediately re-route the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad hoc</i> network if a leader fails to relay the signal. Its combination with graph theory allows to change the dynamic topology of the swarm, with additional agents diving from a charging station, such that the network can relay the optical signal for an unlimited time.

Decentralized Robust Control of a Coupled Wind Turbine and Diesel Engine Generator System

This paper presents the design of a robust decentralized controller for the frequency and active power control of a single stand-alone (off-grid) mechanically-coupled generator system including wind turbine and diesel engine generator subsystems. Two robust decentralized controllers are designed and optimized for the two subsystems by using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> -synthesis technique. The decentralized controllers address the mechanical coupling between the two subsystems without communicating with each other. In addition, both controllers are designed in such a way that they are robust against the concurrent variations of all the system parameters, which in turn improves the frequency and power control performances significantly. Simulation results indicate that the proposed decentralized controllers have a better performance as compared to the benchmark decentralized model predictive controllers and decentralized linear-quadratic Gaussian controllers. It is also demonstrated that the proposed control scheme is able to damp out mild and large disturbances originating from the load and wind power changes, and has an acceptable robust performance against the concurrent variations in all parameters of the system.

Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme

Aggregators are emerging players in the future power markets which aggregate the flexibility of small consumers. This paper proposes a hierarchical model-based scheme to activate the energy flexibility of multi-zone buildings through a direct aggregation mechanism. The novelty lies in considering the power market mechanism in which consumers try to remain committed to their bids without violating their desired comfort levels. In the proposed approach, a high-level control layer determines an hourly energy budget for the whole building according to price signals and reports it to an aggregator. A lower-level dispatch layer then distributes the pre-planned hourly energy budget among different zones. At this level, the emphasis is on keeping the energy consumption as close to the pre-planned budget as possible while satisfying the comfort requirements. In addition, this layer computes the available real-time up and down regulating power and reports them to the aggregator. For comparison, we develop both a centralized and a decentralized model predictive control (MPC) scheme for the high-level control layer. Furthermore, a decentralized MPC with variable prediction horizon is designed for the lower-level dispatch layer. The proposed method is applied to a detailed multi zone building model developed in a high-fidelity simulation environment. The results show that the proposed scheme can keep its commitment to the aggregator to a large extent (by more than 93%) while maintaining the desired comfort levels. In addition, it is seen that the centralized model reduces energy costs and exhibits between 0.5% and 2.5% better commitment to the pre-planned budget in comparison with the decentralized one at the cost of sacrificing comfort to some extent. Moreover, some preliminary results regarding available up and down regulating power for residential buildings are reported for the first time.

Multi-intersection traffic signal control: A decentralized MPC-based approach

Traffic signal control is considered as one of the most important urban traffic management tools, due to its effectiveness in reducing traffic congestion, resulting in smoother and more secure vehicle flows. This work proposes a decentralized Model Predictive Control (MPC) strategy for the minimization of the queue length in a multi-intersection road network. Specifically, we show that our efficient linear formulation enables real-time control of the intersections’ signals, while taking into account safety constraints and pedestrian requests. A novel hyper-parameter tuning algorithm for decentralized MPC (based on Bayesian Optimization) is also proposed. The method is finally tested on a microscopic traffic simulator faithfully reproducing the layout of a real multi-intersection framework in Monza, Italy, fed with real traffic profiles. Simulation results illustrate the effectiveness of the proposed control approach, which can be easily scaled up to larger networks by keeping comparable performance with the state-of-the-art centralized methods.

Operation and control of multiple electric vehicle load profiles in bipolar microgrid with photovoltaic and battery energy systems

Charging of electric vehicles is going to be a major electrical load in the near future, as more and more population shift to electric auto-motives from conventional internal combusted engine-powered vehicles. Integration of electric vehicle charging stations (EVCS) might even burden the existing grid to a point of collapse or grid failure. Establishing charging stations interfaced with bipolar DC microgrids along the roads and highways is the most realistic and feasible solution to avoid the overburdening of the existing power system. The bipolar DC microgrid is a far better microgrid structure than the unipolar microgrid structure in many aspects like reliability, flexibility, and controllability. It can provide multiple voltage level interfaces according to the load demands, which is very apt for different charging levels of electric vehicles (EVs). Operation of multiple sources and multiple loads connected to bipolar DC microgrid will affect DC voltage regulation, capacitance-voltage balancing, and overall stable operation of the grid. In order to mitigate these power quality problems arising in multi-node bipolar DC microgrids, a decentralized model predictive control is proposed in this paper. EV charging load profiles are modeled and developed by considering standard driving cycles, state of charge, and power demand of multiple vehicles to study the effect of unpredictable varying EV loads in the bipolar DC microgrid. EVCS thus modeled are connected to solar photovoltaic-battery energy storage fed bipolar DC microgrid with three-level/bipolar converters and analyzed under dynamic conditions for capacitance–voltage unbalance mitigation, voltage regulation, and the stability of operation with model predictive control. Simulation studies are carried out in MATLAB/Simulink to verify the effectiveness of the system.

Enhancing dynamic stability performance of hybrid ac/dc power grids using decentralised model predictive control

High voltage direct current (HVDC) networks are transforming ac power grids to hybrid ac/dc power grids; hence hybrid ac/dc power grids are facing new stability challenges. In this paper, a decentralised control scheme based on model predictive control (MPC) is proposed to enhance the stability of hybrid ac/dc power grids under disturbances. The proposed scheme controls the dc voltage, frequency, active and reactive power simultaneously with minimum deviations from steady-state values. A communication network is used to share the sampled grid data with the local MPC of each voltage source converter (VSC). Hence, the central controller can be avoided. Moreover, the active power-sharing between converters can be pre-designed. Thus, fast and accurate active power control can be realised. Finally, the proposed control scheme is validated using a five-terminal VSCHVDC test system with MATLAB. The VSCHVDC network with the proposed control scheme shows a smaller deviation and a faster response compared with the conventional droop control scheme and centralised MPC scheme under small disturbances. Under large power variations, the proposed control scheme assists to reach the steady-state rapidly with a minimum impact on the entire power grid. The proposed control scheme has also proved its robustness under communication delays and outages.

A Non-Cooperative Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Interconnected Systems

This paper deals with a new non-cooperative distributed controller for linear large-scale systems based on designing multiple local Model Predictive Control (MPC) algorithms using Laguerre functions to enhance the global performance of the overall closed-loop system. In this distributed control scheme, that does not require a coordinator, local MPC algorithms might transmit and receive information from other sub-controllers by means of the communication network to perform their control decisions independently on each other. Thanks to the exchanged information, the sub-controllers have in this way the ability to work together in a collaborative manner towards achieving a good overall system performance. To decrease drastically the computational load in the small-size optimization problem with a short prediction horizon, discrete-time Laguerre functions are used to tightly approximate the optimal control sequence. For evaluating the proposed distribution control framework, a simulation example is proposed to show the effectiveness of the proposed scheme and its applicability for large-scale interconnected systems. The obtained simulation results are provided to demonstrate clearly that the proposed Non-Cooperative Distributed MPC (NC-DMPC) outperforms Decentralized MPC (De-MPC) and achieves performance comparable to centralized MPC with a reduced computing time. The system performance of the proposed distributed model predictive control is given.

Hierarchical clustering of constrained dynamic systems using robust positively invariant sets

A systematic Agglomerative Hierarchical Clustering (AHC) approach based on minimal Robust Positively Invariant (mRPI) sets is presented for decomposing linear constrained dynamic systems. Specifically, a novel distance metric is proposed where the distance between neighboring subsystems is computed based on the volume of the mRPI sets. Through this distance metric, the proposed AHC approach simultaneously accounts for subsystem dynamics, the constraints on states and inputs, and disturbances created by the dynamic coupling between subsystems. A combination of simple and complex numerical examples is used to demonstrate the key features of the approach including the sensitivity of the proposed AHC to system structure, parameters, and constraints. Finally, numerical results show the benefit of using the proposed AHC when determining potential system decompositions for robust decentralized Model Predictive Control (MPC).

A two-level power management strategy in a DC-coupled hybrid microgrid powered by fuel cell and energy storage systems with model predictive controlled interface converter

This paper intends to present a DC-coupled hybrid microgrid, including proton exchange membrane fuel cell (PEMFC), battery bank, and supercapacitor, which is suitable for applications such as distributed power generation and the power system of vessels. In the proposed configuration, the PEMFC serves as the main energy source, and supercapacitor and battery bank are as energy storage systems. A comprehensive model of the 6 kW PEMFC, including the dynamic model along with the electrical model, is presented. Three DC-DC converters, consisting of an inter-leaved boost with voltage multiplier converter (IBVM) connected to the fuel cell and two bi-directional converters connected to the storage devices are employed. Additionally, an AC-DC converter is utilized to ensure stable AC voltage supply, proper power flow, and DC-link voltage control. Moreover, this paper provides a two-level power management strategy (PMS) to control and optimally distribute power between the components of the hybrid microgrid. The proposed strategy is divided into device-level and system-level controls. At the device-level control, a decentral-ized model predictive control strategy (MPC) without using any proportional-integral-derivative (PID) controllers is proposed to control the different modules of the hybrid microgrid. Also, at the system-level control, a rule-based strategy is developed to optimally distribute power between power sources and ensure stable operation under different operation modes. The simulation results in Matlab/Simulink environment are given to verify the effectiveness of the PEMFC model, the chosen converters, the proposed control methods, and the proposed hybrid microgrid.

A decentralized emergency control scheme for power System transient stability enhancement based on model predictive control

This paper proposes an emergency control scheme based on decentralized model predictive control (MPC) to prevent transient instabilities occurring in power systems. After an onset of transient instability due to a contingency, the control scheme acts on the intercept valves of the steam turbines and the excitation systems of the generators that are available for stabilizing the system. Using the proposed method, the system’s operational constraints as well as the turbine actuator dynamics are properly taken into account, and an objective function towards stabilization is minimized at every sampling time to generate an optimal control law. The method encompasses a decentralized control strategy allowing a straightforward implementation that does not require a communication system, nor does it suffer from its inherent delays, where each participating generator unit is equipped with an MPC controller that relies on only local measurements. Following a critical contingency, the controllers force the generators to remain in synchronism and bring the system back to a stable operating condition. The performance and efficacy of the proposed method have been evaluated and demonstrated by simulations performed on two test systems, the WSCC 9-bus and the IEEE 68-bus systems. It has been shown that the controllers, which are designed independently from the contingencies and operating conditions, are effective in restoring the system’s stability and are robust even when the system is subjected to structural changes due to integration of power electronics interfaced non-synchronous generator units.

Multi-Objective Decentralized Model Predictive Control for Inverter Air Conditioner Control of Indoor Temperature and Frequency Stabilization in Microgrid

Microgrid (MG) is a novel concept for a future distribution power system that enables renewable energy sources (RES). The intermittent RES, such as wind turbines and photovoltaic generators, can be connected to the MG via a power electronics inverter. However, the inverter interfaced RESs reduce the total inertia and damping properties of the traditional MG. Consequently, the system exhibits steeper frequency nadir and the rate of change of frequency (RoCoF), which may degrade the dynamic performance and cause the severe frequency fluctuation of the system. Smart loads such as inverter air conditioners (IACs) tend to be used for ancillary services in power systems. The power consumption of IACs can be regulated to suppress frequency fluctuation. Nevertheless, these IACs, regulating power, can cause the deviation of indoor temperature from the temperature setting. The variation in indoor temperature should be controlled to fulfill residential comfort. This paper proposes a multi-objective decentralized model predictive control (DMPC) for controlling the power consumption of IACs to reduce MG frequency fluctuation and control the variation in indoor temperature. Simulation results on the studied microgrid with the high penetration of wind and photovoltaic generator demonstrate that the proposed DMPC is able to regulate frequency deviation and control indoor temperature deviation as a user preference. In addition, the DMPC has a superior performance effect to the proportional-integral (PI) controller in terms of reducing frequency deviation, satisfying indoor temperature preferences, and being robust to the varying numbers of IACs.

Robust decentralized model predictive load-frequency control design for time-delay renewable power systems

Abstract A robust decentralized model predictive control (DMPC) design is proposed for frequency stability of hybrid renewable power systems considering high renewables energy penetration and nonlinearity effects. The Egyptian power system (EPS) considered as a test system comprises both traditional power stations (i.e., steam, gas, combined cycle, and hydraulic power plants) and renewable energy sources (RESs). Where the considered RESs contain both the wind power generated from Zafarana and Gabel El-Zeit wind farms and the solar power generated from Benban solar park, which is considered one of the world’s largest photovoltaic (PV) plants. To obtain an accurate insight into a real modern power system, this research takes into account the effects of the important nonlinearity such as generation rate constraints (GRCs), governor deadband (GDB), and communication time delay (CTD). The designed control is set based on the DMPC for each subsystem independently to ensure the frequency stability of the whole system as each subsystem has different characteristics and operating constraints than the others. Moreover, the decentralized control scheme has become imperative for large power systems due to the high cost of transmitting data over long distances and the probability of error occurrence with the centralized control scheme. To verify the effectiveness and robustness of the proposed DMPC for the EPS, it is compared with the centralized MPC (CMPC) scheme in different operating conditions. The simulation results, which are conducted using MATLAB/SIMULINK® software, emphasized that the proposed DMPC scheme can effectively handle several load disturbances, high uncertainty in the system parameters, and random communication delays. Hence, it can regulate the grid frequency and ensure the robust performance of the studied renewable power system with high RESs penetration and maximum communication delays in the system.