A distributed model predictive control using virtual field force for multi-ship collision avoidance under COLREGs

The model predictive control method can be effective for converter control in distributed power generation but requires a large amount of computation, leading to a considerable time delay in the actuation. If the delay is not reflected, the system performance could get worse. This paper presents a two-step (horizon) prediction algorithm of model predictive control technique for grid-tied inverters used in wind turbine systems. The control objectives such as active and reactive power flow and switching loss reduction are reflected in the objective function of the controller. The proposed model predictive control strategy is verified numerically by using MATLAB/Simulink.

This paper proposes a model predictive controller for two-stage inverter with less switching losses and capacitor current. In implementation, model predictive control (MPC) method is simple and flexible without assuming the traditional PWM block. As the capacitor current has great influence on the capacitor losses and lifetime, an adjustment before the switching signals are sent to the switches is added in the proposed MPC method to reduce the capacitor current RMS value. The decrease of capacitor current RMS value enables the reduction of capacitor losses and the increase of capacitor lifetime. Besides, the proposed method can achieve less switching losses of front-end dc-dc converter, compared with the conventional MPC method. This paper presents the detailed operational principle of proposed MPC method and compares the performance of conventional and proposed MPC methods using the same system parameters. Finally, the simulation and experimental results verified the validity and advantage of the proposed method.

This paper presents centralized control for a wind farm using model predictive control (MPC) method. In order to solve intra-day unit commitment (UC) problem, the UC problem is solved by using the MPC method with short-term wind power forecasting. We introduce a new dynamics model for the UC with some constraints to utilize the benefits of the MPC method. The objective function considering the operation and maintenance costs is formulated by adding a new variable in order to average the operating time of each wind turbine (WT) within the whole time. The proposed method could solve the UC problems on-line using the prediction time horizon that could be selected flexibly considering time horizon based on wind power forecasting errors. From the simulation study using 10 WTs, we observed that the proposed dynamics model of UC effectively provided the optimal solution to each scenario. Numerical study will validate that the proposed method can be applied to solving the UC problem of a large scale wind farm by aggregating WTs.

This paper proposes a time-lag model predictive control (MPC) method to solve the problem of communication delay in networked automatic generation control (AGC). The method uses the optimum weighting method to predict delay and the time-lag dynamic state equation of interconnected grid as the MPC prediction model. Then the deviation of predicted output is added to the objective function of rolling optimization in MPC method. The backup suboptimal control command from MPC controller can handle the loss of data packet in networked AGC. The correctness of the proposed method is verified by dynamic simulation.

This paper proposes a highly efficient single-phase three-level neutral point clamped (NPC) converter operated by a model predictive control (MPC) method with reduced commutations of switches. The proposed method only allows switching states with none or a single commutation at the next step as candidates for future switching states for the MPC method. Because the proposed method preselects switching states with reduced commutations when selecting an optimal state at a future step, the proposed method can reduce the number of switchings and the corresponding switching losses. Although the proposed method slightly increases the peak-to-peak variations of the two dc capacitor voltages, the developed method does not deteriorate the input current quality and input power factor despite the reduced number of switching numbers and losses. Thus, the proposed method can reduce the number of switching losses and lead to high efficiency, in comparison with the conventional MPC method.

This chapter presents the results of experiments comparing different computationally efficient model predictive control (MPC) methods applied to a laboratory device, demonstrating the active vibration control (AVC) of lightly damped mechanical structures. Because of the combination of long prediction horizons, short sampling times and large actuator-disturbance asymmetry, the implementation of the predictive control strategy on lightly damped vibrating structures is highly demanding. The vibration damping effect and online timing properties of model predictive control algorithms such as infinite horizon cost dual-mode quadratic programming based MPC (QPMPC), pre-computed explicit multi-parametric programming based MPC (MPMPC), minimum-time MPMPC and the very efficient but suboptimal Newton–Raphson based MPC (NRMPC); all with guaranteed stability and constraint feasibility are analyzed in different disturbance and loading scenarios. All MPC methods along with the baseline linear quadratic (LQ) controller decrease vibration settling to equilibrium by an order of magnitude time. The damping effect of all investigated MPC strategies is comparable with a slight decrease in performance for the suboptimal minimum-time MPMPC and NRMPC controllers. Due to the excessive online computational needs of QPMPC, it is a very unlikely candidate for lightly damped vibrating systems given currently available hardware. The online timing analysis presented here demonstrates that MPMPC provides significantly shorter online execution times, however its suboptimal minimum-time version does not bring a convincing improvement. NRMPC can provide online execution times on par with linear quadratic controllers; however, its suboptimality becomes excessive with increasing prediction model orders.

Designing a stabilized distributed model predictive control (DMPC) with constraints is an open and important problem for a class of large-scale distributed systems, which are composed by both weakly and strongly coupled subsystems. This paper proposes a design of DMPC network to stabilize this class of large-scale systems. A coupling degree-based clustering method is first designed to classify subsystems into some middle-scale subsystems (M-subsystem) off-line according to the adjacent matrix, so that these M-subsystems are weakly coupled with each other. Then, each M-subsystem is controlled by a virtual model predictive control (MPC), which is realized by several individual controllers with running iterative cooperative DMPC algorithm, since the solution of cooperative DMPC is able to converge to a fixed point without coupling constraints. Each MPC communicates with the corresponding interacted M-subsystems’ MPCs once in a control period for exchanging future state evolution estimation. All the subsystem-based MPCs are composed of the proposed peer-to-peer DMPC network. In addition, an additional consistency and stabilization constraints are added to guarantee the recursive feasibility and stability of the overall system. The convergence of the iterative DMPC algorithm for each M-subsystem and the stabilization analysis of the overall system are provided. The simulation results show the efficiency of the proposed method. Note to Practitioners —The proposed method is designed for the systems, which are composed of both strong and weak coupling subsystems and are controlled in a distributed structure. The aim of this method is to design a distributed model predictive control (MPC) network for the operational optimization of entire system. The designed control structure and the distributed MPC algorithm with the proposed method could guarantee the recursive feasibility and asymptotic stability of the entire system. To apply this method, the first step is to obtain the models of each subsystem. Then, design the distributed MPC network structure according to the proposed cluster algorithm, which determines the set of subsystems that each virtue MPC controls. Next step is to configure the network information exchanging among these subsystems and construct the optimization problem of each subsystem. Finally, use the existing quadratically constrained quadratic program solvers to calculate the optimal control laws and apply these optimal control laws to the corresponding subsystems. This method can be used in large-scale chemical processes, air conditioner systems for multizone buildings, distributed energy generation systems, and so on.

An economic dispatch (ED) problem of committed turbines is solved minimizing each wind turbine's output power variation by model predictive control (MPC) method. The proposed method could solve the ED problem on-line using prediction time horizon that could be selected flexibly considering time horizon based on wind forecasting errors. Dispatched wind turbines are classified into three groups considering variations of forecasted power generation: maximum power point tracking, balance control, and delta control. From the simulation study using 10 wind turbines we observed that the proposed method effectively provided the optimal solution to the scenario.

This article proposes a dynamic characteristic improvement of phase-shift full-bridge center-tapped (PSFB-CT) converter using a model predictive control (MPC) method. The PSFB-CT converter is widely used for low-voltage dc–dc converters in electric vehicles. In general, its output voltage is a control object which is usually controlled using a proportional–integral control-based output voltage controller and current controller. However, it has a disadvantage such as undesirable dynamic characteristic of the PSFB-CT converter and the necessity of gain tuning. Especially, if the gain of the PI control is increased to improve the dynamic characteristic of the PSFB-CT converter, an overshoot of the output voltage can occur. In this article, the advanced voltage control using the MPC method is proposed to improve the dynamic characteristic of the PSFB-CT converter without overshoot of the output voltage. As a result, the proposed method has advantages with not only fast dynamic characteristic but also robustness from a sudden change of load impedance. The effectiveness of the proposed MPC method was proved by the simulation and experimental results.

Following the understanding of the cold atmospheric plasma jet control, the optimization of plasma parameters for biomedical applications has become an important area of research in the field of plasma-based cancer treatment. A real-time feedback signal is usually required by a control algorithm, such as a self-adaptive plasma jet, which is designed to automatically self-optimize its parameters to adapt to a variety of biomedical applications and situations. In this paper, we introduce the potential of replacing the cell viability or cell stress assay with electrochemical impedance spectroscopy (EIS) to provide a real-time feedback signal for a model predictive control (MPC) method aided by machine learning. The EIS frequency is in the kHz to GHz regime. Therefore, the MPC method is not only designed for minimizing the cancer cell viability, but also considered to optimize cell membrane behaviors and other chemical species dialing. Since these signals are in the range of GHz, we introduce alternatives for the impedance analyzer to measure the impedance spectrum, including a Fabry–Pérot resonator and one of its scanning-array variations.

Online path planning for multiple fixed-wing UAVs in 3D aerial space is a complex optimization problem with high dimension and multiple constraints, which requires efficient solving methods. In this paper, distributed model predictive control (DMPC) method is adopted to accomplish online path planning for multiple UAVs. In order to reduce the computation time of DMPC, an improved grey wolf optimizer (GWO) is designed where pheromone factors are incorporated into GWO to strengthen the exploration ability of the original GWO. In addition, pheromones are optimally selected based on the hierarchy of wolves to guide subsequent iterations so that the convergence speed of the optimizer can be increased. Simulation examples show the superiorities of the proposed method by comparing with various existing optimizers.

To cope with the volatility and randomness of wind power, photovoltaic (PV) power, and load demands in the islanded microgrid, and also to ensure the safety and economic operation of the islanded microgrid system. A collaborative Distributed model predictive control (Di-MPC) based voltage optimization control strategy is proposed, which considers the strong coupling characteristic of active and reactive power due to the impedance ratio of islanded microgrid, and the requirements of real-time and robustness in the optimization as well. By coordinating the controllable devices in the source-grid-load side of the islanded microgrid, the proposed strategy aims to make full use of the voltage regulation capability of each controllable device. Firstly, by considering the different operating characteristics of the controllable devices, a multi-time scale distributed voltage optimal control model is established. It divides the optimal control process into long-time scale and short-time scale and optimizes for respective objective functions and control variables in different time scales. Secondly, a collaborative Di-MPC-based voltage optimal control strategy is proposed. With the proposed collaboration mechanism, the power output increments of the distributed generators (DGs) are solved in the short-time scale, and the errors in the long-time scale control are also fixed. Finally, the simulation results show that compared with a traditional optimal control method and a centralized model predictive control (CMPC) method, the proposed voltage optimization control strategy can effectively reduce the voltage deviation and fluctuation at each node while ensuring the economic operation of the islanded microgrid system.

This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function is constructed to adjust the relative positions and velocities of UAVs, ensuring the desired formation. Additionally, to address the obstacle avoidance problem in the formation, the obstacle avoidance function is designed to provide safe formation control in the obstacle environment. To evaluate the cooperative capability of UAVs, we design evaluation metrics from multiple dimensions to reflect the swarm’s cooperative capability. Finally, the simulation results show the effectiveness of the formation control method with obstacle avoidance and the applicability of the swarm’s cooperative capability evaluation metrics.

Distributed Model Predictive Control with Event-Based Optimization

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed‐loop performance. In this case, it is desirable to design the pre‐existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre‐existing control system and the new control system are designed via Lyapunov‐based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov‐based controller that stabilizes the nominal closed‐loop system using only the pre‐existing control loops, two separate Lyapunov‐based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov‐based controller, improves the closed‐loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009