Receding Horizon Control Scheme Research Articles

Research on Dynamic Takeout Delivery Vehicle Routing Problem under Time-Varying Subdivision Road Network

For the dynamic takeout delivery vehicle routing problem, which faces fluctuating order demand and time-varying speeds, this study presents a novel approach. We analyze the time distribution of takeout orders and apply a Receding Horizon Control (RHC) strategy to convert the dynamic challenge into a static one. The driving speed of delivery vehicles on different roads at different times is determined based on the subdivision criteria of the urban road network and a traffic congestion measurement method. We propose a dynamic takeout delivery vehicle routing optimization model and a time-varying subdivision road network is established to minimize the total delivery cost. We validated the model through simulation examples. The optimization results show that the total distribution cost is reduced after considering the time-varying subdivision road network, with the penalty cost decreasing by 39%. It is evident that considering the subdivision of the road network can enhance order delivery efficiency and optimize the overall dining experience. The sensitivity analysis of various parameters reveals that the delivery platform must appropriately determine the time domain and allocate the number of delivery personnel based on order scale to avoid escalating delivery costs. These findings provide theoretical guidance for vehicle routing planning in the context of delivery platforms.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles (AVs). The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer. To address this issue, this study proposes a safe motion planning and control (SMPAC) framework for AVs. For the control layer, a dynamic model including multi-dimensional uncertainties is established. A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set. A flexible tube with varying cross-sections is constructed to reduce the controller conservatism. For the planning layer, a concept of safety sets, representing the geometric boundaries of the ego vehicle and obstacles under uncertainties, is proposed. The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories. An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles. A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC. The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

Microgrids with Model Predictive Control: A Critical Review

Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations.

Effects of heat pump scheduling on low‐voltage grids using a receding horizon control strategy

AbstractHeat pumps (HPs) are a promising option for decarbonizing the residential heating sector. Their use leads to an increased demand for electricity, which can burden the grid in uncontrolled operation, while controlled HPs can provide additional flexibility to the system. In this paper, the integration of HPs through a receding horizon approach into a power market model is presented to study the behavior of HPs considering electricity market prices and network constraints of a low‐voltage grid. In a case study with centralized and self‐consumption optimized control strategies, the effects of HPs are analyzed. Furthermore, the authors investigate how the aggregation of several households into energy communities affects the system. The results show that for the considered grid, the additional power demand of HPs is usually uncritical and a strictly cost‐minimizing operation is often possible from a grid perspective. Typically, critical grid situations occur only at times of high PV feed‐in with low HP flexibility. The formation of energy communities compared to individual households differs primarily in the allocation of cost savings to individual consumers. The difference in the impact of the two control strategies on the grid is negligible in this case.

A Large Neighborhood Search Algorithm with Simulated Annealing and Time Decomposition Strategy for the Aircraft Runway Scheduling Problem

The runway system is more likely to be a bottleneck area for airport operations because it serves as a link between the air routes and airport ground traffic. As a key problem of air traffic flow management, the aircraft runway scheduling problem (ARSP) is of great significance to improve the utilization of runways and reduce aircraft delays. This paper proposes a large neighborhood search algorithm combined with simulated annealing and the receding horizon control strategy (RHC-SALNS) which is used to solve the ARSP. In the framework of simulated annealing, the large neighborhood search process is embedded, including the breaking, reorganization and local search processes. The large neighborhood search process could expand the range of the neighborhood building in the solution space. A receding horizon control strategy is used to divide the original problem into several subproblems to further improve the solving efficiency. The proposed RHC-SALNS algorithm solves the ARSP instances taken from the actual operation data of Wuhan Tianhe Airport. The key parameters of the algorithm were determined by parametric sensitivity analysis. Moreover, the proposed RHC-SALNS is compared with existing algorithms with excellent performance in solving large-scale ARSP, showing that the proposed model and algorithm are correct and efficient. The algorithm achieves better optimization results in solving large-scale problems.

Real-Time Economic Dispatch of CHP Systems with Battery Energy Storage for Behind-the-Meter Applications

The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system (BESS); however, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, this paper proposes a real-time Energy Management System (EMS) using a combination of Long Short-Term Memory (LSTM) neural networks, Mixed Integer Linear Programming (MILP), and Receding Horizon (RH) control strategy. The RH control strategy is suggested to reduce the impact of prediction errors and enable real-time implementation of the EMS exploiting actual generation and demand data on the day. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method.

Game-Theoretic Trajectory Planning of Mobile Robots in Unstructured Intersection Scenarios

The motion of autonomous mobile robot platforms in a shared operation area can lead to intersection scenarios that cannot be resolved by individual and uncoordinated motion planning. A coordinated solution approach is needed to solve these kinds of scenarios for which game theory provides a suitable framework. Therefore, this paper presents a game-theoretic trajectory planner based on a nonlinear receding horizon control scheme. To handle complex collision avoidance in unstructured environments with no reference paths, the proposed method extends an existing sensitivity enhanced iterated best response algorithm. Our reformulation of the sensitivity cost term within the optimal control problem of each vehicle enables a cooperative and time-efficient collision avoidance behavior. Another benefit of our proposed approach is that no central coordination unit is required, and only minimal communication between the vehicles is necessary. We simulatively compare our decentralized approach in different intersection scenarios involving 2 to 4 mobile robots to a centralized multi-robot trajectory planner. The simulation results show that our algorithm resolves the intersection scenarios in most cases with only a minimum average duration extension until each vehicle reaches its goal state compared to the centralized planner.

A Set-Theoretic Control Approach to the Trajectory Tracking Problem for Input–Output Linearized Wheeled Mobile Robots

This paper proposes a set-theoretic receding horizon control scheme to address the trajectory tracking problem for input-constrained differential-drive robots. The proposed solution is derived starting from an input-output linearized description of the robot kinematics and a worst-case characterization of the orientation-dependent input constraint acting on the feedback linearized model. In particular, offline, given a worst-case characterization of the constraint set, we analytically design the smallest robust control invariant region for the tracking error. Moreover, such a region is recursively enlarged by computing a family of robust one-step controllable sets whose union characterizes the controller’s domain of attraction. Online, such sets and the knowledge of the current robot’s orientation are leveraged to define a non-conservative control law ensuring bounded tracking error. The effectiveness of the proposed strategy is experimentally validated using a Khepera IV robot, and its performance is contrasted with four alternative trajectory tracking algorithms.

Receding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach.

Optimal sizing of residential battery energy storage systems for long-term operational planning

Appropriate battery storage capacity plays an important role in the performance and cost of residential energy systems. However, the load demand and renewable energy generation vary seasonally. To address the long-term operational planning problem of battery energy storage, two battery sizing methods are developed based on the consensus alternating direction method of multipliers (C-ADMM). The residential system layout and convex battery model considering cycling aging are first established. Then, for the one-year horizon planning, C-ADMM decomposes the original large-scale problem into 365 sub-problems. The global optimal battery size and operation strategy are obtained by coordinating these sub-problems in parallel until convergence. In addition, to obtain the optimal battery size based on receding horizon control (RHC) strategies in a year, a new bi-level optimization method is developed, which uses the particle swarm optimization (PSO) algorithm in the outer loop and C-ADMM in the inner loop. Thus the inner loop can be accelerated through parallel computing. The results show that the long-term storage planning problem of residential systems can be solved quickly through C-ADMM due to the parallel computing capability. Furthermore, the optimal battery size calculated under the RHC strategy in a year can contribute to more economic benefits than the global battery size when applied online.

Gaussian Process Approximate Dynamic Programming for Energy-Optimal Supervisory Control of Parallel Hybrid Electric Vehicles

We propose an energy-efficient supervisory control method for the power management of parallel hybrid electric vehicles (HEVs) to improve the fuel economy and reduce exhaust gas emissions. Plug-in HEVs ((P)HEVs) have multiple power sources (e.g., an engine and motor) that should be cooperatively operated to meet the required instantaneous traction power for the desired vehicle speed while satisfying their physical limits. Because the efficiencies of the engine and motor vary with different operating speeds and torques, the main issue of energy-efficient power management is to allocate the power demand among the power sources by achieving maximum power conversion efficiencies and satisfy the operating limits. For an efficient power allocation, an optimal control problem is formulated, and a global solution is found through deterministic dynamic programming (DP). Owing to the curse of dimensionality and uncertainties in real driving, DP solutions are not directly applicable in real time. To resolve the limitations of DP, we employ a non-parametric Bayesian function approximation technique using a Gaussian process (GP). The offline DP solutions obtained from a set of real vehicle driving test data were used to learn a state-dependent probabilistic value function through Gaussian process regression. For online implementations, a receding horizon control scheme was applied for the feedback control of the power management. In comparison with the existing charge sustaining strategy and charge depleting and charge sustaining mixed controllers, we recorded fuel efficiency improvements of over 4.8% and 7.3%, respectively, in a mixed urban-suburban route.

Agent-Based Model Predictive Controller (AMPC) for Vehicular Stability With Experimental Results

Model predictive control (MPC) in automotive active safety applications has gained great success in recent years. Detailed vehicle models along with a receding horizon control scheme seek the optimal control distribution among actuators for enhanced performances while satisfying system constraints. To promote the re-usability and scalability of MPC-based vehicular active safety systems, an agent-based MPC (AMPC) is proposed in our previous study for a modularized control architecture. In this paper, implementation of the control scheme is demonstrated on an all-wheel-drive test platform. Both centralized and agent-based MPCs for vehicular stability are compared for their control performances as well as computational costs on embedded hardware. It is shown from experimental results that agent-based MPC is more flexible and computationally efficient in handling vehicle active safety challenges while gives no compromise to control performances compared to their holistically formulated counterparts.

A nonlinear model predictive controller design for residual current compensation inverters in rapid earth fault current limiters to mitigate powerline bushfires

This paper presents a nonlinear model predictive control scheme for residual current compensation inverters in rapid earth fault current limiters to inject appropriate current to the neutral so that the fault current is compensated in distribution networks. The proposed nonlinear model predictive controller is designed to limit the fault current within a level that powerline bushfires do not ignite due to single line-to-ground faults on power distribution systems in bushfire prone areas. The nonlinear model predictive controller is designed for a T-type residual current compensation inverter in rapid earth fault current limiters based on its dynamical model where the control objective is to inject current for compensating the fault current. The control law for the residual current compensation inverter is obtained by solving an optimal control problem while using the concept of the receding horizon control scheme. The performance of the controller is evaluated through standard software-based computer and processor-in-loop simulations where results are benchmarked against backstepping and sliding mode controllers in terms of compensating both faulty phase voltage and fault current.

Constrained stochastic predictive control of linear systems with uncertain communication

Abstract This paper proposes a scheme of model predictive control for single-loop networked control system (NCS) with probabilistically modeled communication channel and disturbances. Uncertainties of the communication network are projected onto a tailored probability for the satisfaction of state and input constraints. The proposed receding horizon control scheme uses a probabilistic terminal state and set to establish a balance between control performance and state probability distribution, while satisfying the given constraints. In addition to describing the control approach, its properties are discussed, and it is illustrated by an example.

Efficient Motion Planning Based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces

Quadruped robots have superior terrain adaptability and flexible movement capabilities than traditional robots. In this article, we innovatively apply it in person-following tasks, and propose an efficient motion planning scheme for quadruped robots to generate a flexible and effective trajectory in confined spaces. The method builds a real-time local costmap via onboard sensors, which involves both static and dynamic obstacles. And we exploit a simplified kinodynamic model and formulate the friction pyramids formed by ground reaction forces' inequality constraints to ensure the executable of the optimized trajectory. In addition, we obtain the optimal following trajectory in the costmap completely based on the robot's rectangular footprint description, which ensures that it can walk through the narrow spaces avoiding collision. Finally, a receding horizon control strategy is employed to improve the robustness of motion in complex environments. The proposed motion planning framework is integrated on the quadruped robot JueYing and tested in simulation as well as real scenarios. It shows that the execution success rates in various scenes are all over 90%.

RNN for Receding Horizon Control of Redundant Robot Manipulators

Redundant manipulators have been studied and applied in many fields. The trajectory tracking of redundant manipulators is an important topic to explore for applications. This article aims to develop a planning scheme for achieving the trajectory tracking of redundant manipulators, from the receding horizon control (RHC) perspective. For the nonlinear model of manipulators, the linearization operation is conducted to obtain predictive outputs through the forward kinematic equation. Subsequently, an RHC scheme, which minimizes tracking error, velocity norm, and acceleration norm, and directly considers joint limits at three levels as well as the terminal equality constraint, is constructed and further simplified as a convex quadratic programming problem. Furthermore, a recurrent neural network (RNN) model is designed for the constructed RHC scheme, with the help of the technique of converting inequality constraints into equality constraints. The proposed RHC scheme solved by the RNN model is compared with other existing planning schemes and solvers through computer simulations and experiments, without and with the sudden external interference. Simulation and experiment results show that the proposed RHC scheme solved by the RNN model is able to make the redundant manipulator track the given trajectory excellently, and is superior to other existing schemes and solvers in terms of high efficiency, quick-response capacity, and strong robustness.

Reconfiguration control of satellite formation using online quasi-linearization iteration and symplectic discretization

This paper focuses on the reconfiguration control problem of formation flying spacecraft via low-thrust continuous propulsion. A nonlinear receding horizon control (NRHC) scheme for performance optimization and constraint enforcement is presented. The nonlinear optimal control (NOC) problem underlying the receding horizon control scheme is transcribed into a sequence of linear optimal control (LOC) problems by exploiting an online quasi-linearization strategy. Hence, only a LOC must be solved in each sampling interval, thereby significantly reducing the computational cost. It differs from conventional receding horizon control schemes that require solving a NOC problem in each sampling interval. Moreover, the numerical solution of the LOC problem is obtained using a structure-preserving symplectic algorithm that is capable of maintaining a high degree of accuracy while simultaneously improving computation efficiency. Finally, a numerical case study is presented to illustrate the effectiveness and superiority of the proposed control approach.

Motion Optimization for Musculoskeletal Dynamics: A Flatness-Based Polynomial Approach

A new approach for trajectory optimization of musculoskeletal dynamic models is introduced. The model combines rigid-body and muscle dynamics described with a Hill-type model driven by neural control inputs. The objective is to find input and state trajectories that are optimal with respect to a minimum-effort objective and meet constraints associated with musculoskeletal models. The measure of effort is given by the integral of pairwise average forces of the agonist-antagonist muscles. The concepts of flat parameterization of nonlinear systems and sum-of-squares optimization are combined to yield a method that eliminates the numerous set of dynamic constraints present in collocation methods. With terminal equilibrium, optimization reduces to a feasible linear program, and a recursive feasibility proof is given for more general polynomial optimization cases. The methods of the article can be used as a basis for fast, and efficient solvers for hierarchical, and receding-horizon control schemes. Two simulation examples are included to illustrate the proposed methods.

Perceptive Model Predictive Control for Continuous Mobile Manipulation

A mobile robot needs to be aware of its environment to interact with it safely. We propose a receding horizon control scheme for mobile manipulators that tracks task space reference trajectories. It uses visual information to avoid obstacles and haptic sensing to control interaction forces. Additional constraints for mechanical stability and joint limits are met. The proposed method is faster than state of the art sampling based planners, available as opensource and can be implemented on a broad class of robots. We validate the method both in simulation and through extensive hardware experiments with a multitude of mobile manipulation platforms. The resulting software package is released with this letter.

Hybrid modelling and predictive control of utility-scale variable-speed variable-pitch wind turbines

Wind energy has proven to be the highest reliable source of renewables due to the maturity of the technology. Wind turbine (WT) systems are complex systems with undergoing development process that requires innovative methods of design and control. In this paper, WTs are studied through hybrid systems framework. Hybrid models of WT are extracted with representable dynamics from nonlinear complex design code. The WT model is formulated into a mixed-logical dynamical model (MLD) and a piecewise affine (PWA) model. Then, a receding horizon control strategy is applied to WT hybrid model resulting in a hybrid model predictive control (HMPC) with mixed-integer programming (MIP) problem. The performance of the proposed controller is compared against the baseline controller within a simulation environment for the National Renewable Energy Laboratory (NREL) 5MW benchmark WT as a case study. The analysis and investigation of HMPC highlight its capability as a potential tool for exploiting the control objectives of WT systems.