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The ability to accurately predict others' behavior is central to the safety and efficiency of robotic systems in interactive settings, such as human–robot interaction and multi-robot teaming tasks. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as other agents' goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between the robot's trajectory plan and its prediction of other agents' intent. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive models with both continuous and categorical uncertainty. To ensure the safe operation of the interacting agents, we use a runtime safety filter (also referred to as a "shielding" scheme), which overrides the robot's dual control policy with a safety fallback strategy when a safety-critical event is imminent. We then augment the dual control framework with an improved variant of the recently proposed shielding-aware robust planning scheme, which proactively balances the nominal planning performance with the risk of high-cost emergency maneuvers triggered by low-probability agent behaviors. We demonstrate the efficacy of our approach with both simulated driving studies and hardware experiments using 1/10 scale autonomous vehicles. [ABSTRACT FROM AUTHOR]

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve energy discrepancies and expand the capacity for renewable energy utilization. Then, the SMPC-based framework embedding an auto-regressive model and scenario generation method are established to make real-time corrections to the day-ahead scheduling stage and offset the prediction errors of uncertainty sources economically. Furthermore, thermal inertia of the aggregated buildings with different envelopes and linepack in gas pipelines are both leveraged to enhance the flexibility and synergy of CIES. Finally, a case study is executed to verify the effectiveness and applicability of the proposed strategy. The simulation results unequivocally demonstrate that this strategy successfully coordinates and harnesses complementary advantages from various energy sources, fostering a balanced energy supply-demand equilibrium across multiple temporal and spatial scales. [ABSTRACT FROM AUTHOR]

This paper addresses the trajectory-tracking problem under uncertain road-surface conditions for autonomous vehicles. We propose a stochastic nonlinear model predictive controller (SNMPC) that learns a tyre–road friction model online using standard automotive-grade sensors. Learning the entire tyre–road friction model in real time requires driving in the nonlinear, potentially unstable regime of the vehicle dynamics, using a prediction model that may not have fully converged. To handle this, we formulate the tyre-friction model learning in a Bayesian framework and propose two estimators that learn different aspects of the tyre–road friction. The estimators output the estimate of the tyre-friction model as well as the uncertainty of the estimate, which expresses the confidence in the model for different driving regimes. The SNMPC exploits the uncertainty estimate in its prediction model to take proper action when the uncertainty is large. We validate the approach in an extensive Monte Carlo study using real vehicle parameters and in CarSim. The results when comparing to various MPC approaches indicate a substantial reduction in constraint violations, as well as a reduction in closed-loop cost. We also demonstrate the real-time feasibility in automotive-grade processors using a dSPACE MicroAutoBox-II rapid prototyping unit, showing a worst-case computation time of roughly 40 ms. [ABSTRACT FROM AUTHOR]

A novel stochastic model predictive control (SMPC) scheme is proposed for automotive scenes based on high-performance and practical motion state prediction method. The significant properties of the proposed scheme are that: 1) it can accurately predict disturbances within the prediction horizon, and 2) the prediction results can be considered into the optimizing process to obtain a more efficient and accurate controller. As a result, the proposed adaptive cruise control (ACC) system can ensure driving safety and improve tracking accuracy and comfort performance while satisfying different driving styles. In detail, a large amount of naturalistic driving data is collected based on a real vehicle test platform at first. Then an adaptive optimization Gaussian process regression (AOGPR) is developed and trained with real measurements to predict the motion states of the preceding vehicle. The prediction module is embedded in SMPC to bind the collision conditions, tighten the states and finally construct a novel controller, i.e., AOGPR-SMPC controller. A bidirectional LSTM (BiLSTM) network is trained and tested for online recognizing driving styles to satisfy personalized car-following needs. The simulation and field tests verify and evaluate the proposed controller. The results demonstrate that the ACC system could realize personalized car-following according to the driver's driving style, and the proposed controller can obtain better tracking accuracy and comfort performance compared with the GPR-SMPC controller and MPC controller. [ABSTRACT FROM AUTHOR]

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov–Barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi‐linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled‐data MPC framework to deal with states and inputs constraints, and to analyze stability of closed‐loop systems. Moreover, event‐triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example. [ABSTRACT FROM AUTHOR]

Model‐based methods in autonomous driving and advanced driving assistance gain importance in research and development due to their potential to contribute to higher road safety. Parameters of vehicle models, however, are hard to identify precisely or they can change quickly depending on the driving conditions. In this paper, we address the problem of safe trajectory planning under parametric model uncertainties motivated by automotive applications. We use the generalized polynomial chaos expansions for efficient nonlinear uncertainty propagation and distributionally robust inequalities for chance constraints approximation. Inspired by the tube‐based model predictive control, an ancillary feedback controller is used to control the deviations of stochastic modes from the nominal solution, and therefore, decrease the variance. Our approach allows reducing conservatism related to nonlinear uncertainty propagation while guaranteeing constraints satisfaction with a high probability. The performance is demonstrated on the example of a trajectory optimization problem for a simplified vehicle model with uncertain parameters. [ABSTRACT FROM AUTHOR]

With the high proportion of distributed energy resource (DER) access in the distributed network, the tie-line power should be controlled and smoothed to minimize power flow fluctuations due to the uncertainty of DER. In this paper, a stochastic model predictive control (SMPC) method is proposed for tie-line power smoothing using a novel data-driven linear power flow (LPF) model that enhances efficiency by updating parameters online instead of retraining. The scenario method is then employed to simplify the objective function and chance constraints. The stability of the proposed model is demonstrated theoretically, and the performance analysis indicates positive results. In the one-day case study, the mean relative error is only 1.1%, with upper and lower quartiles of 1.4% and 0.2%, respectively, which demonstrates the superiority of the proposed method.

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. • Implementation of an SMPC strategy based on a scenario-based approach. • Reduction of operation and maintenance costs and integration of uncertainties. • Minimization of the overall operational costs and maximization the profits. • Reduction of complexity and computational time. • Implementation of a unique control architecture to manage a microgrid in different modes. [ABSTRACT FROM AUTHOR]

Summary Data‐driven predictive control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, little has been done on data‐driven stochastic control. In this paper, we propose a data‐driven stochastic predictive control scheme for LTI systems subject to possibly unbounded additive process disturbances. Based on a stochastic extension of the fundamental lemma and leveraging polynomial chaos expansions, we construct a data‐driven surrogate optimal control problem (OCP). Moreover, combined with an online selection strategy of the initial condition of the OCP, we provide sufficient conditions for recursive feasibility and for stability of the proposed data‐driven predictive control scheme. Finally, two numerical examples illustrate the efficacy and closed‐loop properties of the proposed scheme for process disturbances governed by different distributions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stochastic model predictive control problem (SMPC) for a class of networked control systems with exogenous disturbances and restrictions on communication frequency. A self-triggered SMPC algorithm with an improved triggering condition is designed which integrates the co-design of both the current control inputs and the maximum sampling interval, with the aim of reducing the amounts of communication transmission while guaranteeing the specific performance. To give consideration to both probabilistic guarantee and computability, chance constraints on both states and inputs are transformed into deterministic ones by leveraging Cantelli's inequality and linearisation techniques, so as to be solvable for the optimisation problem. Meanwhile, the nonconvex terms in dynamics of covariance propagation are averted through introducing the covariance upper bound control approach. The formulated online optimisation problem is convex to all decision variables. The theoretical analysis on recursive feasibility and closed-loop stability is addressed for the proposed self-triggered SMPC scheme. Finally, the efficacy and achievable performance of the proposed algorithm are showcased through numerical simulations. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide a stochastic framework to optimally avoid collision between a maneuverable spacecraft and a space object or debris. The satellite collision can be caused through a cyber-attack on a satellite by colliding it with a considered strategic satellite. Consequently, it is highly imperative that critical operational space assets be provided with autonomous collision avoidance systems. The collision avoidance methodology proposed in this paper will reduce the collision probability to an acceptable level and protect the satellite against indirect kinetic cyber-attacks initiated by designing optimal collision avoidance maneuvers using a stochastic model predictive control strategy. The collision probability is estimated using the available historical Two-Line Elements of determined objects, and the model predictive control scheme guarantees the safety of the space close approaches. The proposed and developed collision-avoidance countermeasure methodology is numerically simulated for the collision case study between the Iridium-33 and the Cosmos-2251 satellites. The results demonstrate and illustrate the effectiveness, capabilities, and advantages of our proposed methodology in avoiding probable collisions due to indirect kinetic cyber-attacks. [ABSTRACT FROM AUTHOR]

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In the face of multiple failures caused by extreme disasters, the power and communication sides of the distribution network are interdependent in the fault recovery process. To improve the post-disaster recovery efficiency of the distribution network, this paper proposes a coordinated optimization strategy for distribution network reconfiguration and repair, which integrates the power and communication aspects. First, the recovery process is divided into islanding–reconfiguration and dynamic emergency repair. The coupling relationship between power and communication is considered; that is, power failure may cause communication nodes to lose power, and communication failure may affect the effective operation of remote control devices. Based on this, the fault recovery process is optimized with the objective of maximizing load transfer and direct recovery while introducing a stochastic model predictive control method to handle the uncertainty of distributed power generation by rolling optimization of typical scenarios. Finally, the effectiveness of the proposed strategy is verified using an improved IEEE33-node distribution network system. The simulation results show that the proposed method can recover power to the maximum extent and reduce loss while ensuring the safe and stable operation of the distribution system. [ABSTRACT FROM AUTHOR]

Stochastic Model Predictive Control (SMPC) is a promising solution for controlling multivariable systems in the presence of uncertainty. However, a core challenge lies in obtaining a probabilistic system model. Recently, multi-step system identification has been proposed as a solution. Multi-step models simultaneously predict a finite sequence of future states, which traditionally involves recursive evaluation of a state-space model. Particularly in the stochastic context, the recursive evaluation of identified state-space models has several drawbacks, making multi-step models an appealing choice. As a main novelty of this work, we propose a probabilistic multi-step identification method for a linear system with noisy state measurements and unknown process and measurement noise covariances. We show that, in expectation, evaluating the identified multi-step model is equivalent to estimating the initial state distribution and subsequently propagating this distribution using the known system dynamics. Therefore, using only recorded data of an unknown linear system, our proposed method yields a probabilistic multi-step model, including the state estimation task, that can be directly used for SMPC. As an additional novelty, our proposed SMPC formulation considers parametric uncertainties of the identified multi-step model. We demonstrate our method in two simulation studies, showcasing its effectiveness even for a nonlinear system with output feedback.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article studies a data‐driven predictive control for a class of control‐affine systems which is subject to uncertainty. With the accessibility to finite sample measurements of the uncertain variables, we aim to find controls which are feasible and provide superior performance guarantees with high probability. This results into the formulation of a stochastic optimization problem (P), which is intractable due to the unknown distribution of the uncertainty variables. By developing a distributionally robust optimization framework, we present an equivalent and yet tractable reformulation of (P). Further, we propose an efficient algorithm that provides online suboptimal data‐driven solutions and guarantees performance with high probability. To illustrate the effectiveness of the proposed approach, we consider a highway speed‐limit control problem. We then develop a set of data‐driven speed controls that allow us to prevent traffic congestion with high probability. Finally, we employ the resulting control method on a traffic simulator to illustrate the effectiveness of this approach numerically. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the release of the '2050 Net Zero Emissions from Shipping' by the International Maritime Organization (IMO) and China 'Carbon Neutrality' policy, it is imperative to develop renewable energy ships. River-sea going renewable energy ships have high economic benefits and low emissions. They can sail in both rivers and seas without other ships and barges. To realize the economical and low-carbon operation of river-sea going renewable energy ships, a variable time-scale power scheduling strategy is proposed. A new model of all-electric propulsion power is firstly proposed. The model focuses on the multi-variables coupling variations of a ship propulsion process, such as resistance, motion state, navigation speed, deadweight and so on. A voyage-power hierarchical framework which is based on a stochastic model predictive control (SMPC) method is developed to optimize the scheduling scheme. A novel variable time-scale control mechanism in view of uncertainty fluctuation quantization is proposed. Results show that the energy consumption of the proposed all-electric propulsion power computational model is reduced by 52.6%, compared with the traditional computational model. The operation cost and carbon emission which are determined by the proposed method is reduced by 21.6% and 35.9%, respectively. [Display omitted] • A new multi-variable coupling model of AEP power is proposed to improve the calculation accuracy of a river-sea going ship. • A voyage-power hierarchical scheduling framework is proposed by an improved stochastic model predictive control (SMPC) method. • A novel variable time-scale control mechanism in view of uncertainty fluctuation quantization is proposed. • Results show that the proposed AEP power model can improve the accuracy and economy of a ship power scheduling. [ABSTRACT FROM AUTHOR]

Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distributed stochastic model predictive control (DSMPC) design for a human-leading heavy-duty truck platoon. The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon. The driver behavior of the human-driven leader truck is learned by a stochastic inverse reinforcement learning (SIRL) approach. The proposed stochastic driver behavior model aims to learn a distribution of cost function, which represents the richness and uniqueness of human driver behaviors, with a given set of driver-specific demonstrations. The distributed formation control includes a serial DSMPC with guaranteed recursive feasibility, closed-loop chance constraint satisfaction, and string stability. Simulation studies are conducted to investigate the efficacy of the proposed design under several realistic traffic scenarios. Compared to the baseline platoon control strategy (deterministic distributed model predictive control), the proposed DSMPC achieves superior controller performance in constraint violations and spacing errors. [ABSTRACT FROM AUTHOR]

In the face of multiple failures caused by extreme disasters, the power and communication sides of the distribution network are interdependent in the fault recovery process. To improve the post-disaster recovery efficiency of the distribution network, this paper proposes a coordinated optimization strategy for distribution network reconfiguration and repair, which integrates the power and communication aspects. First, the recovery process is divided into islanding–reconfiguration and dynamic emergency repair. The coupling relationship between power and communication is considered; that is, power failure may cause communication nodes to lose power, and communication failure may affect the effective operation of remote control devices. Based on this, the fault recovery process is optimized with the objective of maximizing load transfer and direct recovery while introducing a stochastic model predictive control method to handle the uncertainty of distributed power generation by rolling optimization of typical scenarios. Finally, the effectiveness of the proposed strategy is verified using an improved IEEE33-node distribution network system. The simulation results show that the proposed method can recover power to the maximum extent and reduce loss while ensuring the safe and stable operation of the distribution system.

A recurrent neural network (RNN)-based approach is proposed in this paper to handle joint chance-constrained stochastic optimal control problems (SOCP) and stochastic model predictive control (SMPC) implementations. In the proposed approach, the joint chance constraint (JCC) in a SOCP is first reformulated as a quantile-based inequality. Then, the sample average approximation (SAA) method is used to build the RNN-based surrogate model for the quantile function. Afterwards, the RNN-based model is embedded into the probabilistic constraint of the SOCP. Subsequently, the SOCP involving the RNN-based model can be solved using a deterministic nonlinear optimization solver. Moreover, while applying the proposed approach to the SMPC, the SOCP involving the RNN-based model is solved repeatedly at different sampling instants, based on different initial system states. The proposed approach is applied to a numerical illustrating example and a chemical process case study to demonstrate its capability of handling the SOCP and the SMPC implementation. • Quantile reformulation of joint chance constraints and sample based empirical approximation. • Approximation of quantile function through recurrent neural network based surrogate model. • Stochastic optimal control problem is deterministically solvable though embedded RNN model. • Easy extension and implementation of the proposed approach to stochastic model predictive control problem. [ABSTRACT FROM AUTHOR]

For obstacle avoidance against randomly moving traffic participants, stochastic model predictive control is promising. In crowded environments, however, feasible trajectories satisfying chance constraints do not necessarily exist; crowding induces a relaxation of constraints that causes deterioration of safety. To address this issue, we developed a velocity control method that decelerates the ego vehicle to a speed that satisfies the chance constraints on the prediction horizon. We conducted numerical simulations of obstacle avoidance and experiments of moving through a crowd comprising vehicles and pedestrians to evaluate the performance. The results indicate that the designed controller can generate a trajectory that mitigates the relaxation of constraints and adapts to various traffic participants. [ABSTRACT FROM AUTHOR]

This research proposes a TSPcut controller that enables connected and automated buses to cut through traffic to make TSP green light. The proposed controller overcomes the shortcomings of conventional TSP strategies and is able to: 1) overtake slowing moving vehicles in order to catch TSP green time; 2) decide the best time to pass the intersection; 3) considering the stochasticity of surrounding traffic; and 4) functional under partially connected and automated environment. It takes full advantage of connected vehicle technology by taking in real-time vehicle and infrastructure information as optimization input. The problem is formulated as an SMPC problem and is solved by a high-efficient dynamic programming algorithm. The nonlinear bicycle model is adopted as the system dynamics to realize CAV bus’s lane-changing and overtaking function. The stochasticity of surrounding traffic is considered as a probability distribution which is transformed into a linear chance constraint. Simulation evaluation is conduct to compare the TSPcut against NTSP, CTSP and BocTSP. Sensitive analysis is conducted for congestion levels. The evaluation results demonstrate that the TSPcut improves the bus delay reduction by 17.9%–49.1%, and the benefits are 3.5% to 16.1% greater than that of other TSP systems. The range is caused by different congestion levels. In addition. Further tests are conducted to analyze how CAV bus’s arrival time and the speed of background traffic influence the performance of the TSPcut. [ABSTRACT FROM AUTHOR]

This study has been used to solve the control problems of inventory systems in a probabilistic environment. This has involved the use of an inventory system or warehouse with one type of product purchased from a supplier with some time needed for transportation and some of the products considered to be demanded by buyers or consumers. Meanwhile, some have been considered to be defected before entering the warehouse due to several reasons such as damages during delivery or bad quality. This has led to the use of the demand value and defect product rate as the two probabilistic parameters, and they have been treated as random variables with some probability density functions. Moreover, the inventory level behavior represented by the number of products stored in the warehouse has been modeled as a linear dynamical system containing the two aforementioned probabilistic parameters. The manager of the warehouse has been also considered the one deciding the number of items to be purchased from the supplier at every period in order to ensure the expected demand is satisfied and the inventory level is close to a "safe" point. Furthermore, a robust model predictive control, which is a model-based control method, has been later applied to this inventory control problem. The numerical experiment results have showed that the optimal decisions have been achieved and the inventory level has been sufficiently closed to the "safe" level. Therefore, the approach proposed in this study is recommended to be adopted by managers of inventory systems. [ABSTRACT FROM AUTHOR]

Stochastic model predictive control (SMPC) is a popular approach to control uncertain systems by incorporating the probabilistic distribution of the uncertainty into the controller design. However, the performance of the SMPC deteriorates rapidly when the system becomes nonlinear. In this article, an efficient SMPC is derived after decomposing the discrete nonlinear systems into a linear stochastic model and a bounded model mismatch. The proposed controller includes a linearized‐model‐based SMPC and an RMPC for the model mismatch. The multi‐step control strategy is incorporated to reduce the conservativeness by introducing more degrees of freedom. The feasibility of the recursiveness of the proposed controller is thoroughly discussed. The control performance is illustrated by examples involving both a numerical nonlinear stochastic system and a wind‐blade pitch system. [ABSTRACT FROM AUTHOR]

Today’s increasing in demand for renewable energy resources has attracted a lot of attention towards Microgrids (MGs). Due to their reliability, computation power and performance, a distributed network of MGs seems to be a promising approach to integrate a large number of distributed energy resources, energy storage systems and loads. However, the local privacy, the consensus process and the stochastic behaviors of renewable energy resources are some of the challenges which should be addressed before a wide deployment of such networks. In this paper, we introduce a distributed MGs integrated with buildings, focusing on their ability to provide peak load limiting. The algorithm is formalized as a two-stage stochastic problem, where the first stage variables determine the next time-step buildings’ temperatures setpoints in each microgrid, while the second-stage variables define the power exchange decisions for the purpose of limiting peak load in the network of microgrids. Leveraging by alternating direction method of multipliers (ADMM), the proposed framework can work in a real-time environment as a supervisory controller to coordinate MGs. [ABSTRACT FROM AUTHOR]

This paper presents a chance constrained stochastic model predictive control (SMPC) approach for building climate control under combined parametric and additive uncertainties. The proposed SMPCap approach enables the quantification, and manipulation, of both the mean and covariance of the stochastic system states and inputs. Its enhanced uncertainty anticipation is shown to induce improved thermal comfort in closed-loop simulations compared to the conventional deterministic MPC (DMPC) and the state-of-the-art SMPCa only accounting for additive uncertainties, at the cost of a maximum relative increase in energy use of 21.6% and 4.2%, respectively. By incorporating the SMPCap strategy in an integrated optimal control and design (IOCD) approach, its additional added value for obtaining a more appropriate, yet robust, heat supply system sizing is illustrated. Via simulations, size reductions up to 33.3% are shown to be achievable for a terraced single-family dwelling without increasing thermal discomfort compared to an IOCD approach incorporating DMPC. [ABSTRACT FROM AUTHOR]

The energy management strategy of hybrid electric vehicles is of significant importance to improve the fuel economy. In this regard, two energy management strategies are designed for power-split hybrid electric city bus (HECB), which are based on the linear time-varying stochastic model predictive control (LTV-SMPC) and stochastic model predictive control based on Pontriagin’s minimum principle (PMP-SMPC). In the present study, the Markov chain and long short-term memory (LSTM) forecast demand torque and velocity respectively are applied to establish a combination forecast model. Then several processes, including linear approximation, processing simplified control model, the proposed nonlinear vehicle model is converted into a linear time-varying model. Meanwhile, the energy management problem in a linear quadratic programming problem is solved. Considering linearization error and limitations of the quadratic optimization, Pontriagin’s minimum principle (PMP) is applied to optimize the nonlinear model predictive control. Based on the reference theory, the range of coordinated variables is derived, and the optimal coordination variable is searched by dichotomy to realize the rolling optimization of the model predictive control (MPC). Finally, the effectiveness of the proposed energy management strategy is verified by simulating several case studies. Obtained results show that compared with the rule-based (RB) control strategy, the fuel economy of LTV-SMPC and PMP-SMPC increases by 8.79% and 14.42%, respectively. Meanwhile, it is concluded that the two strategies have real-time computing potential.

The charging management of plug‐in electric vehicles (PEVs) in San Francisco considering the effect of drag force on the vehicles, the real driving routes of vehicles, the social aspects of drivers' behaviour, the type of PEVs and the PEV penetration level is presented in this study. In this study, the drivers' responsiveness probability, to provide vehicle‐to‐grid service at the parking lot, is modelled with respect to the value of the incentive, drivers' social class and the real driving routes in San Francisco. Herein, the Monte Carlo Markov Chain is applied to estimate the hourly probability distribution function of the state of charge (SOC) of the PEV fleet in the day. The main data set applied in this study includes the real longitude and latitude of driving routes of vehicles in San Francisco, recorded in every four‐minute interval of the day. In this study, a stochastic model predictive control is applied in the optimisation problem to address the variability and uncertainty issues of PEVs' SOC and renewables' power. Herein, quantum‐inspired simulated annealing algorithm is applied as the optimisation technique. It is demonstrated that the type of PEVs, the PEV penetration level and even the social class of drivers can affect the problem results.

Renewable energy outputs such as wind turbines and photovoltaics, as well as data center workloads are both random and uncertain. In order to enhance the stability and economy of the data center in actual operation effectively, a multi-time scale optimal dispatch method for the data center microgrid based on stochastic model predictive control is proposed in this paper. In the day-ahead scheduling stage, the characteristic of the data center that batch workloads are allowed to be served delayed is considered. And the scenario analysis method is applied to describe the uncertainty of loads and renewable energy outputs, based on which an economic optimization scheduling model is established to minimize the system operating cost. The intra-day scheduling utilizes the rolling optimization and feedback correction of model predictive control to correct the deviation of loads and renewable energy outputs and adjust the day-ahead dispatch plan in real time, which ensures the effectiveness of day-ahead plan and the stability of system operating. Through the simulation results of a typical data center microgrid, the effectiveness of the proposed method is verified.

The rapid development of technology and economy has led to the development of chemical processes, large-scale manufacturing equipment, and transportation networks, with their increasing complexity. These large systems are usually composed of many interacting and coupling subsystems. Moreover, the propagation and perturbation of uncertainty make the control design of such systems to be a thorny problem. In this study, for a complex system composed of multiple subsystems suffering from multiplicative uncertainty, not only the individual constraints of each subsystem but also the coupling constraints among them are considered. All the constraints with the probabilistic form are used to characterize the stochastic natures of uncertainty. This paper first establishes a centralized model predictive control scheme by integrating overall system dynamics and chance constraints as a whole. To deal with the chance constraint, based on the concept of multi-step probabilistic invariant set, a condition formulated by a series of linear matrix inequality is designed to guarantee the chance constraint. Stochastic stability can also be guaranteed by the virtue of nonnegative supermartingale property. In this way, instead of solving a non-convex and intractable chance-constrained optimization problem at each moment, a semidefinite programming problem is established so as to be realized online in a rolling manner. Furthermore, to reduce the computational burdens and amount of communication under the centralized framework, a distributed stochastic model predictive control based on a sequential update scheme is designed, where only one subsystem is required to update its plan by executing optimization problem at each time instant. The closed-loop stability in stochastic sense and recursive feasibility are ensured. A numerical example is employed to illustrate the efficacy and validity of the presented algorithm in this study. • A distributed SMPC algorithm is developed for a class of stochastic systems. • Both individual and coupled probabilistic constraints are taken into account. • The intractable CCOP is converted into a tractable problem for online computation. • Computational complexity is reduced using the proposed distributed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, some data-based control design options that can be used to accommodate for the presence of uncertainties in continuous-state engineering systems are recalled and discussed. Focus is made on reinforcement learning, stochastic model predictive control and certification via randomized optimization. Some thoughts are also shared regarding the positioning of the control community in a data and AI-dominated period for which some suggestions and risks are highlighted. [ABSTRACT FROM AUTHOR]

We developed a load frequency control system with stochastic model predictive control (SMPC) for power systems where the market penetration of wind power generation is high. The controller adjusts the electricity price for heat pump water heaters while at the same time controlling thermal power plants and batteries in order to maintain the frequency in the designated range. We propose an approach for solving SMPC problems on Hammerstein models including affine disturbance feedback parametrization. Simulation results show that SMPC with affine disturbance feedback parametrization outperforms both SMPC without parametrization and deterministic model predictive control in terms of the stage-cost and constraint violation.

With the increasing demand for battery electric buses, the dual-motor coupling powertrain (DMCP) shows great advantages, but it makes the energy optimization problem more complex. To solve the hybrid system optimization problem, a stochastic model predictive control (SMPC) method is proposed to exploit the potential performance of DMCP, where the most critical issue is to improve the prediction accuracy and handle the uncertainties. After analyzing the typical velocity profiles, statistical properties are used to develop a novel Signed Markov Chain Monte Carlo (SMCMC) method that can enhance the accuracy of velocity prediction by more than 50%, compared to conventional Markov Chain methods. Next, considering the uncertainties present in various driving scenarios, the development of driving cycle recognition model based on fuzzy logic control (FLC) is introduced; this method permits to identify the current category of driving cycle rapidly. Then, dynamic programming (DP) is adopted to solve the rolling optimization problems in each finite horizon online, including necessary constraints of dynamic response. Finally, simulation results demonstrate that the proposed energy management strategy can address various daily driving cycles well, and can improve the energy performance by 6% under a generalized combination of driving conditions compared to preliminary rule-based control.

In this paper, the air-fuel ratio regulation problem of compressed natural gas (CNG) engines is considered by employing stochastic model predictive control (MPC) technology. A stochastic model predictive regulator based on a discrete-time dynamic model of CNG engines is proposed, taking into account the residual gas, and the closed-loop system is deduced to be stochastically stable. A numerical simulation is performed to demonstrate the effectiveness of the proposed control scheme under two working conditions. The simulation results show that the performance of the proposed stochastic model predictive regulator is better than that of the open-loop controller.

An unmanned surface vehicles (USV) set point tracking problem is investigated in this paper. The stochastic model predictive control (SMPC) scheme is utilized to design the controller in order to reject the environment disturbances and meet the physical constraints. The design problem is formulated as a chance-constrained stochastic optimization problem, which is non-convex. Thus, the problem is computationally prohibitive. For this, the convex conditional value at risk (CVaR) approximation is introduced to convert the chance constraints into deterministic convex constraints. The converted constraints are then further transformed into the second order cone (SOC) constraints. Therefore, the proposed method is computationally tractable and hence can be implemented online. A numerical example is provided to illustrate the effectiveness of the proposed method.

Vehicle-following operation is a typical scenario in the future intelligent transportation environment. Keeping a safe distance is the most important goal in the vehicle-following scenario. For a plug-in hybrid electric bus (PHEB) running in a specific urban route, the challenge will become how to realize the optimal power split in hybrid powertrain under the premise of maintaining driving safety. Considering the above issues, this paper proposes a stochastic model predictive control (SMPC) strategy for PHEBs during vehicle-following scenario. Firstly, Markov chain-based stochastic driving model is built using real-world bus driving condition data, which is applied to predict future demand torque over a finite receding horizon. And then, a sequential quadratic programming (SQP) algorithm is adopted to solve the rolling optimization problem. Meanwhile, brake specific fuel consumption and electric motor efficiency are fitted offline to match the SMPC strategy. Furthermore, a piecewise function is given to adjust the adaptive factor that balancing fuel economy and vehicle-following in the pre-set cost function. Finally, to verify the control performance of the proposed strategy, a nonlinear model predictive control strategy with dynamic programming optimization (DP-MPC) and a rule-based (RB) strategy are employed for comparison study. Results indicate that the proposed strategy is effectiveness to the given driving condition with excellent fuel economy and vehicle-following performance. Under the driving condition of Chongqing 303 bus line in China and China typical, the fuel consumption is reduced by 20.58% and 37.89% compared with RB strategy, respectively. It is closer to the fuel consumption reduction of 16.77% and 13.11 optimized by DP-MPC. Driving safety during vehicle-following also be demonstrated in the driving condition of Chongqing 303 bus line and China typical.

In the paper, a typical coal trade process is described and modelled, where one logistics enterprise with blending equipments lies in the core and two types of common contracts are elucidated to define constraints. A mixed-integer model is built and featured by addressing contract violation, blending operation, real-time price information and arbitrarily distributed stochastic demands. To deal with the stochastic demands, probabilistic constraints are formed. Accordingly, stochastic model predictive control strategy with both receding horizon and decreasing horizon formulations is developed to handle the probabilistic constraints and exploit the value of newest price information. By solving a series of mixed-integer linear programmes, optimal coal trade decisions for the logistics enterprise can be obtained, including procurement decision, selling decision and operational decision of the blending equipments. Thorough simulation experiments are carried out and compared with three different strategies, which interpret the effectiveness of the proposed strategy. [ABSTRACT FROM PUBLISHER]

This paper presents an uncontrolled intersection-passing algorithm with an integrated approach of stochastic model-predictive control and prediction uncertainty estimation for autonomous vehicles. The proposed algorithm is designed to utilize information from sensors mounted on the autonomous vehicle and high-definition intersection maps. The proposed algorithm is composed of two modules, namely target state prediction and a motion planner. The target state prediction module has predicted the future behavior of intersection-approaching vehicles based on human driving data. The recursive covariance estimator has been utilized to estimate the prediction uncertainty for each approaching vehicle. The desired driving mode has been determined based on the uncontrolled intersection theory. The estimated prediction uncertainty has been used to define the probability distribution of the stochastic model-predictive controller to cope with time-varying uncertainty characteristics of the perception algorithm. The constrained stochastic model-predictive controller based on safety indexes has determined the desired longitudinal acceleration. The proposed robust intersection-passing algorithm has been evaluated via computer simulation based on Monte Carlo simulation with a sensor model. The simulation results showed that the proposed algorithm guarantees the minimum safety constraints and improves the ride comfort at uncontrolled intersections by estimating the uncertainty of sensors and prediction. [ABSTRACT FROM AUTHOR]

For Model Predictive Control in safety‐critical systems it is not only important to bound the probability of constraint violation but to reduce this constraint violation probability as much as possible. Therefore, an approach is necessary that minimizes the constraint violation probability while ensuring that the Model Predictive Control optimization problem remains feasible even under changing uncertainty. We propose a novel two‐step Model Predictive Control scheme that yields a solution with minimal constraint violation probability for a norm constraint in an environment with uncertainty. After minimal constraint violation is guaranteed, the solution is then also optimized with respect to other control objectives. Recursive feasibility and convergence of the method are proved. A simulation demonstrates the effectiveness of the proposed method for a collision avoidance example. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of overloading in power distribution networks, which stems from the transmission systems being incapable of delivering power from source to consumers during peak hours. This causes frequent power-outages (or blackouts), requiring the consumers to rely on alternative energy sources, e.g., Uninterrupted Power Supply (UPS) systems with batteries to meet their essential needs. This paper proposes a demand response (DR) framework to eliminate the problem of network overloading. The flexibility in the consumption of batteries and air conditioners (ACs) is exploited in the proposed framework. The operation of ACs is manipulated while maintaining occupant comfort, and the power flow from mains and batteries is scheduled based on an ensemble of demand forecast avoiding network overloading and consequent power-outages. The problem is modeled in an optimal control setting and solved using a stochastic model predictive control strategy, and a computationally effective method is also proposed to efficiently solve the underlying optimization problems. Towards the end, simulation results show the efficacy of the proposed framework to avoid overloading. [ABSTRACT FROM AUTHOR]

Increasing concerns related to fossil fuels have led to the introducing the concept of emission-free ships (EF-Ships) in marine industry. One of the well-known combinations of green energy resources in EF-Ships is the hybridization of fuel cells (FCs) with energy storage systems (ESSs) and cold-ironing (CI). Due to the high investment cost of FCs and ESSs, the aging factors of these resources should be considered in the energy management of EF-Ships. This article proposes a nonlinear model for optimal energy management of EF-Ships with hybrid FC/ESS/CI as energy resources considering the aging factors of the FCs and ESSs. Total operation costs and aging factors of FCs and ESSs are chosen as problem objectives. Moreover, a stochastic model predictive control method is adapted to the model to consider the uncertainties during the optimization horizon. The proposed model is applied to an actual case test system and the results are discussed. [ABSTRACT FROM AUTHOR]

This paper develops a stochastic model predictive control (SMPC) based framework for the real-time operation of residential-scale DC-coupled PV-storage systems. The proposed framework combines bivariate Markov chains to build the uncertainty model of PV generation and residential load, a Bayesian approach based recursive learning of the Markov model, and a scenario-based formulation for the SMPC problem. This approach operates in real-time, thus minimizing the impact of the mismatch between the forecasted data and the actual observation on the system performance by updating the control decisions with the realization of the stochastic parameters at each time step. Load and PV generation are jointly modeled, and the interdependence between them is accounted for through bivariate Markov chains. The use of recursive online learning guarantees that the uncertainty model is continuously updated to enhance its prediction capabilities for scenario generation. The numerical simulations using real-world data demonstrate the enhanced performance of the proposed approach over the conventional approaches, on a par with model predictive control with complete knowledge of the uncertainties. [ABSTRACT FROM AUTHOR]

Cross-regional energy sharing is a promising way to alleviate the imbalance in energy distribution. However, there is a risk that the uncertain fluctuations of intermittent renewable energy sources (RES) could be transmitted across regional transmissions, resulting in large-scale system economic inefficiency and security concerns. To this end, a unified spatial-temporal cooperative framework for the integrated energy system, which considers the coordination between intra-regional multi-energy coupling and cross-regional multi-agent-system energy sharing, is proposed in this paper. First, in the temporal dimension, a multi-time-scale stochastic decision-making model is established based on stochastic model predictive control (SMPC). Thus, slow-response units are arranged to gain economic goals in day-ahead stage and the closed-loop rolling adjustment is implemented on fast-response units to achieve accuracy supply-demand balance in real-time stage. Second, in the spatial dimension, a fully distributed dispatch approach is adopted to address the information sharing barriers, which only requires neighboring information and considers consistency of boundary control variables on border lines. Finally, to improve the computational efficiency, the established complex time sequential model is decomposed into a set of time-discrete parallel sub-problems, using the optimal condition decomposition (OCD) technique. Simulation results on a cross-regional test system demonstrate that proposed framework can increase the wind power consumption capacity and reduce operation cost through cross-regional cooperation, while the parallel decomposition can save 67.25 % of the computation time relative to the serial computation. • A novel unified spatial-temporal cooperative dispatching framework is proposed for the cross-regional IES. • A multi-time-scale rolling stochastic decision-making model is established to handle RES uncertainties. • The ADMM is implemented to achieve cross-regional rapid interactions iteration with only neighboring information. • The OCD is applied to decompose the complex spatial-temporal problem. [ABSTRACT FROM AUTHOR]

This paper presents a multi‐time hierarchical stochastic predictive control (MHSPC) scheme for an island microgrid, in which electric vehicles (EVs) can be used as mobile energy storages to improve power balance and realise load‐frequency control (LFC) with micro‐turbines (MTs). At the upper layer, a stochastic model predictive control is proposed to handle the EVs uncertainties on a long time scale, while optimising controllable power adjustment of MTs, the charge/discharge energy of the battery electric storage (BES) unit and guaranteeing EVs to be fully charged at the expected plug‐out time. At the lower layer, the coordination between EVs and MTs for LFC is achieved by a standard MPC framework on a short time scale. In this way, the power balance is met, and the frequency fluctuation is inhibited. Finally, simulation results are presented to illustrate the satisfactory operation of the island microgrid.