Receding Horizon Control Algorithm Research Articles

Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control

The integration of increasing share of renewable based distributed generation in distribution networks brings great challenges to voltage control. To address this issue, this paper presents a two-stage distributionally robust chance-constrained receding horizon control algorithm. In the proposed method, the distributionally robust chance-constrained reformulation of chance-constrained voltage control is derived, which is not only accurate, but also computationally efficient. Rather than perfect knowledge about the uncertainty associated with renewable generation, the proposed method only requires partial information of the underlying probability distribution. In addition, the mechanical voltage regulation devices and the DG inverters are controlled in two stages, considering their different characteristics in voltage control. By taking into account both the current and forecasted renewable generation, the proposed method utilizes receding horizon control to determine the control actions of voltage regulation devices. The effectiveness of the proposed method is demonstrated by case studies on unbalanced IEEE-123 bus system.

Optimal plug-and-control of unknown nonlinear systems

Modeling and control design are typically subsequent but independent activities. Optimal control is traditionally developed on the basis of explicit models. While this usually yields good results for linear systems, the same is not as true for nonlinear ones, for which explicit solutions can be found only for few cases. In practice, in most cases receding horizon controls based on linear approximations are used. In this paper, we propose a procedure which delivers in one step both a model and an optimal receding horizon control algorithm, without requiring a linearization. Our procedure relies essentially on a system identification by a suitable class of functions which offers universal approximation properties that can be directly incorporated in the control algorithm. Using directional forgetting we show that an adaptive extension can be realized. Measurements and simulations based on a standard automotive control problem are presented to confirm the validity of our proposal.

Impact of shared battery energy storage system on total system costs and power peak reduction in commercial buildings

The power system is experiencing an increasing share of renewable and intermittent energy production and increasing electrification. However, these changes are creating high power peaks, are straining the grid and call for expensive investments in expansions and improvements. This paper examines how the operational strategy of shared battery energy storage systems (s-BESS) can address these issues for commercial buildings with relatively high power peaks. Due to the uncertainty in long-term costs when subject to a measured peak (MP) grid tariff, the scheduling of the battery is optimised with a receding horizon control algorithm. The optimisation model is used on a Norwegian real-life case study to find the best possible configuration with an already existing battery. Although current Norwegian regulations challenge the possibility for shared metering and billing for a s-BESS configuration, the results show that the total system cost was reduced by 19.2% compared to no battery. The community peak was reduced by 17.8% compared to no battery and 6.22-17.5% compared to individual storage, which indicates that s-BESS is of value for the DSO as well.

Performance comparison of two market algorithms for providing support services todistribution systems

Several market algorithms, such as Fast Locational Marginal Pricing (FLMP) and Receding Horizon Control (RHC), have shown the ability to cope with grid congestion. The RHC algorithm can cope with fluctuations in power consumption and generation but is computationally intensive due to the larger search space within the time horizon. The FLMP algorithm is not complex but does not utilize a horizon with information on future consumption and generation. This paper demonstrates a performance comparison between the FLMP and Horizon Marginal Pricing (HMP) algorithm. HMP is an algorithm that combines the simplicity of the FLMP algorithm with the time horizon possibilities of the RHC algorithm. The HMP algorithm uses the predicted generation profile to adjust the bid curves, such that consumption shifts to moments with more renewable generation available. Simulations are carried out to compare the performance between the HMP and FLMP algorithms. The results show comparable performance between the FLMP and HMP, whilst the FLMP computes the simulation faster and requires less bandwidth.

Platooning Control for Heterogeneous Sailboats Based on Constant Time Headway

This paper addresses the problem of platooning control for a fleet of heterogeneous sailboats. The platooning maintains a constant time headway (CTH) between sailboats following a circular path, a complex problem for sailboats due to the influence of wind direction. First, the desired acceleration based on the CTH and the sailboat velocity needed to converge to the platooning is defined. Second, a control of sailboat orientation to manage the sailboat acceleration is proposed. The proposed platooning strategy adapts to the specific characteristics of sailboats, which are different from other motorized marine vehicles. Two tack strategies can be used for the method: the first is to regulate the sailboat velocity; the second is to go front of the wind while staying in a short corridor. The desired acceleration for fulfilling the platooning has been derived and validated. The simulation results demonstrate the effectiveness of the proposed approach in comparison with an optimal receding horizon control algorithm.

Deadline-Aware MapReduce Job Scheduling with Dynamic Resource Availability

As MapReduce is becoming ubiquitous in large-scale data analysis, many recent studies have shown that the performance of MapReduce could be improved by different job scheduling approaches, e.g., Fair Scheduler and Capacity Scheduler. However, most exiting MapReduce job schedulers focus on the scenario that MapReduce cluster is stable and pay little attention to the MapReduce cluster with dynamic resource availability. In fact, MapReduce cluster resources may fluctuate as there is a growing number of Hadoop clusters deployed on hybrid systems, e.g., infrastructure powered by mix of traditional and renewable energy, and cloud platforms hosting heterogeneous workloads. Thus, there is a growing need for providing predictable services to users who have strict requirements on job completion times in such dynamic environments. In this paper, we propose, RDS , a Resource and Deadline-aware Hadoop job Scheduler that takes future resource availability into consideration when minimizing job deadline misses. We formulate the job scheduling problem as an online optimization problem and solve it using an efficient receding horizon control algorithm. To aid the control, we design a self-learning model to estimate job completion times. We further extend the design of RDS scheduler to support flexible performance goals in various dynamic clusters. In particular, we use flexible deadline time bounds instead of the single fixed job completion deadline. We have implemented RDS in the open-source Hadoop implementation and performed evaluations with various benchmark workloads. Experimental results show that RDS substantially reduces the penalty of deadline misses by at least 36 and 10 percent compared with Fair Scheduler and Earliest Deadline First (EDF) scheduler, respectively. In a Hadoop cluster running partially on renewable energy, the experimental result shows the green power based resource prediction approach can further reduce the penalty of deadline misses by 16 percent compared to Auto-Regressive Integrated Moving Average (ARIMA) prediction approach.

Particle swarm optimization-based receding horizon formation control of multi-agent surface vehicles

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

Preventing environmental disasters from grounding accidents: A case study of tugboat positioning along the Norwegian coast

An important task of operators in Norwegian vessel traffic services (VTS) centres is to cleverly position tugboats before potential vessel distress calls. Here, we formulate a non-linear binary-integer program, integrated in a receding horizon control algorithm that minimises the expected cost of grounding accidents by positioning tugboats optimally under uncertainty about vessel incidents and environmental conditions. Linearisations of the model lead to easy-to-compute bounds on the optimal value. Numerical experiments with real-world data demonstrate significant reduction in the expected cost, suggesting that the model can be used as a decision-support tool at VTS centres.

A receding horizon stabilization approach to constrained nonholonomic systems in power form

The control of nonholonomic systems with practical requirements is still a challenging problem but finds many industrial applications. This paper studies the receding horizon control (RHC)-based stabilization problem of a class of constrained nonholonomic systems in power form. A non-quadratic cost function is constructed by using the homogeneous norm of the nonholonomic system in power form. With this novel cost function, two kinds of RHC algorithms are designed, of which one ensures the convergence of the closed-loop system states, and the other ensures the ρ-exponential stability. The feasibility of the designed algorithms and the closed-loop convergence are analyzed and ensured theoretically under mild conditions. The comparison and application results are provided, showing that (1) the proposed RHC algorithms are effective and the theoretical results are valid, and (2) the proposed algorithms can stabilize the nonholonomic systems with a much faster convergence rate than the conventional time-varying stabilizable controllers.

Spacecraft relative guidance via spatio-temporal resolution in atmospheric density forecasting

Spacecraft equipped with the capability to vary their ballistic coefficient can use differential drag as the control force to perform propellant-less relative maneuvers. Because atmospheric drag is proportional to atmospheric density, uncertainty in atmospheric density makes the generation and tracking of drag-based guidances difficult. Spatio-temporal resolution, or the mapping of density information to altitude and time, is shown in this work to improve atmospheric density estimation from forecasted density for spacecraft in LEO. This is achieved by propagating simulated orbits for two spacecraft using forecasted density. Additionally, a receding-horizon control algorithm is introduced, with the goal of improving the tracking of guidances. Using a simulated perfect forecast of the atmospheric density for propagation of the orbits, relative guidance trajectories are generated and tracked, establishing the benefit of adding spatio-temporal resolution. Next, imperfect density forecasting is added, indicating that the benefit of spatio-temporal resolution is retained in the presence of imperfect forecasting. Finally, a receding-horizon control algorithm is used with imperfect forecasting, demonstrating that receding-horizon control improves the tracking of guidances compared to single-horizon control.

Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed [formula omitted]-gain stability

This paper investigates the distributed receding horizon control (RHC) problem of a vehicle platoon with nonlinear dynamics and subject to system constraints, where each vehicle can communicate with its immediate predecessor and follower. A novel optimization problem and detailed distributed RHC algorithm are designed in order to keep a platoon formation, and further to ensure neighbor γ-gain stability (which is a new notion proposed in this paper and generalizes the string stability). The sufficient conditions on ensuring closed-loop stability and neighbor γ-gain stability are established, respectively. Finally, simulation studies are provided to verify the theoretical results. It is shown that it is possible to achieve certain control performance (i.e., γ-gain stability) and keep a formation simultaneously for the nonlinear vehicle platoon using distributed RHC.

Modelling and control for eliminating flux oscillations in generation IV high temperature gas cooled reactor

The core power distribution in the advanced high temperature prismatic reactor (AHTPR 625) belonging to the high temperature gas cooled reactor family is controlled using the constrained receding-horizon control algorithm. The outputs from each region of the reactor core are used as the inputs of the system. The reactor model is based on the two-point-coupled xenon oscillation model. It utilises the nonlinear xenon and iodine balance equations along with the one-group, one-dimensional neutron diffusion equation including feedback with a power coefficient. Axially, the reactor core is divided into two regions. Each region has one input and one output. These regions are coupled with each other. The proposed control method demonstrates very fast response to input flux change in the form of a step and ramp change without any residual flux oscillations between the regions of the reactor core. The constrained receding-horizon control problem involves solving an optimisation problem for finite future samples and obtaining the control with few samples. Among the few constrained control samples only the first one is applied and the process to solve the optimisation problem is repeated again.

An obstacle avoidance model predictive control scheme for mobile robots subject to nonholonomic constraints: A sum-of-squares approach

The paper addresses the obstacle avoidance motion planning problem for ground vehicles operating in uncertain environments. By resorting to set-theoretic ideas and sum of squares (SOS) decomposition techniques, a receding horizon control algorithm is proposed for robots modeled by polynomial systems subject to input, state and nonholonomic constraints. Sequences of inner ellipsoidal approximations of the exact one-step controllable sets are computed for all admissible obstacle scenarios and then on-line exploited to determine the more adequate control action to be applied in a receding horizon fashion. The results here proposed are a significant generalization of existing algorithms which are tailored only for linear time invariant plant descriptions. The resulting framework guarantees uniformly ultimate boundedness and constraints fulfilment regardless of any obstacle scenario occurrence.

Robust Parameter Dependent Receding Horizon H∞ Control of Flexible Air‐Breathing Hypersonic Vehicles with Input Constraints

AbstractThis paper describes the design of a robust parameter dependent receding horizon H∞ controller (PD‐RHHC) for a flexible air‐breathing hypersonic vehicle (ABHSV) model. Because of the complexity and uncertainty of the model of the vehicle dynamics, few control strategies take constraints into account in the computation of the control law. The objective of the current research is to develop a controller design method that can accommodate input saturation. In this paper, starting from the analytical nonlinear dynamic model with aerothermoelastic mode dynamics, a linear parameter varying (LPV) model is obtained by an improved functional substitution method. Tensor product (TP) model transformation technology is then applied to obtain the polytopic representations of the LPV model. Based on the gain‐scheduling method, a novel parameter dependent receding horizon control algorithm by convex optimization solving linear matrix inequalities (LMIs) is presented, which provides stable tracking of the velocity and altitude reference trajectories. The proposed control strategy not only addresses the issue of robustness stability with respect to parametric model uncertainty and aerothermoelastic mode, but also explicitly deals with input constraints and guarantees that the control inputs are within their limits. Finally, the numerical simulation results prove the availability of the proposed method.

The obstacle avoidance motion planning problem for autonomous vehicles: A low-demanding receding horizon control scheme

The paper addresses the obstacle avoidance motion planning problem for ground vehicles operating in uncertain environments. By resorting to set-theoretic ideas, a receding horizon control algorithm is proposed for robots modelled by linear time-invariant (LTI) systems subject to input and state constraints and disturbance effects. Sequences of inner ellipsoidal approximations of the exact one-step controllable sets are pre-computed for all the possible obstacle scenarios and then on-line exploited to determine the more adequate control action to be applied to the robot in a receding horizon fashion. The resulting framework guarantees Uniformly Ultimate Boundedness and constraints fulfilment regardless of any obstacle scenario occurrence.

Stabilizing Dynamic Controllers for Hybrid Systems: A Hybrid Control Lyapunov Function Approach

This paper proposes a dynamic controller structure and a systematic design procedure for stabilizing discrete-time hybrid systems. The proposed approach is based on the concept of control Lyapunov functions (CLFs), which, when available, can be used to design a stabilizing state-feedback control law. In general, the construction of a CLF for hybrid dynamical systems involving both continuous and discrete states is extremely complicated, especially in the presence of non-trivial discrete dynamics. Therefore, we introduce the novel concept of a hybrid control Lyapunov function, which allows the compositional design of a discrete and a continuous part of the CLF, and we formally prove that the existence of a hybrid CLF guarantees the existence of a classical CLF. A constructive procedure is provided to synthesize a hybrid CLF, by expanding the dynamics of the hybrid system with a specific controller dynamics. We show that this synthesis procedure leads to a dynamic controller that can be implemented by a receding horizon control strategy, and that the associated optimization problem is numerically tractable for a fairly general class of hybrid systems, useful in real world applications. Compared to classical hybrid receding horizon control algorithms, the proposed approach typically requires a shorter prediction horizon to guarantee asymptotic stability of the closed-loop system, which yields a reduction of the computational burden, as illustrated through two examples.

Effect of Haptic Assistance on Learning Vehicle Reverse Parking Skills

Compared to conventional visual- and auditory-based assisted driving technologies, haptic modality promises to be more effective and less disturbing assistance to the driver. However, in most previous studies, haptic assistance systems were evaluated from safety and stability viewpoints. Moreover, the effect of haptic assistance on human driving behavior has not been sufficiently discussed. In this paper, we introduce an assisted driving method based on haptic assistance for driver training in reverse parking, which is considered as an uncertain factor in conventional assisted driving systems. The proposed system assists the driver by applying a torque on the steering wheel to guide proper and well-timed steering. To design the appropriate assistance method, we conducted a measurement experiment to determine the qualitative reverse parking driver characteristics. Based on the determined characteristics, we propose a haptic assistance calculation method that utilizes the receding horizon control algorithm. For a simulation environment to assess the proposed assistance method, we also developed a scaled car simulator comprising a 1/10 scaled robot car and an omnidirectional camera. We used the scaled car simulator to conduct comparative experiments on subjects, and observed that the driving skills of the assisted subjects were significantly better than those of the control subjects.

A distributed obstacle avoidance MPC strategy for leader-follower formations

In this paper we address the obstacle avoidance motion planning problem for leader-follower vehicles configurations operating in static environments. By resorting to set-theoretic ideas, a receding horizon control algorithm is proposed for robots modelled by linear time-invariant (LTI) systems subject to input and state constraints. Terminal robust positively invariant regions and sequences of precomputed inner approximations of the one-step controllable sets are on-line exploited to compute the commands to be applied in a receding horizon fashion. Moreover, we prove that the design of both terminal sets and one-step ahead controllable regions is achieved in a distributed sense. An illustrative example is used to show the effectiveness of the proposed control strategy.

Optimal guidance based on receding horizon control for low-thrust transfer to libration point orbits

This paper addresses the design and computation of a guidance law for a transfer mission from an orbit near the Earth to a halo orbit around the libration point L2 in the Sun–Earth system. The guidance law, which is designed based on receding horizon control and compensates for launch velocity errors that are introduced by inaccuracies of the launch vehicle, is solved using the generating function method. During the design of the closed-loop guidance law, the entire transfer mission, which is considered a nonlinear optimal control problem, is evaluated to obtain a nominal reference trajectory. Using the launch velocity errors and the uncertainty of the model, a spacecraft controlled by the proposed guidance law tracks the reference trajectory. Furthermore, the original Riccati differential equation in the receding horizon control algorithm is replaced by an equivalent convenient form of the Riccati differential equation that is based on the generating function. The high-efficiency solution of the equivalent equation avoids the online direct integration of the original Riccati differential equation, which significantly increases the computational efficiency for the receding horizon control problem. Numerical simulations using a nonlinear bicircular four-body model demonstrate the capabilities of the proposed receding horizon guidance law for the transfer mission. In addition, the generating function method improves the computational efficiency by at least one order of magnitude over the backward sweep method in solving the receding horizon control problem.

Distributed Receding Horizon Control of Vehicle Platoons: Stability and String Stability

This paper considers the problem of distributed control of a platoon of vehicles with nonlinear dynamics. We present distributed receding horizon control algorithms and derive sufficient conditions that guarantee asymptotic stability, leader-follower string stability, and predecessor-follower string stability, following a step speed change in the platoon. Vehicles compute their own control in parallel, and receive communicated position and velocity error trajectories from their immediate predecessor. Leader-follower string stability requires additional communication from the lead car at each update, in the form of a position error trajectory. Predecessor-follower string stability, as we define it, implies leader-follower string stability. Predecessor-follower string stability requires stricter constraints in the local optimal control problems than the leader-follower formulation, but communication from the lead car is required only once at initialization. Provided an initially feasible solution can be found, subsequent feasibility of the algorithms are guaranteed at every update. The theory is generalized for nonlinear decoupled dynamics, and is thus applicable to fleets of planes, robots, or boats, in addition to cars. A simple seven-car simulation examines parametric tradeoffs that affect stability and string stability. Analysis on platoon formation, heterogeneity and size (length) is also considered, resulting in intuitive tradeoffs between lead car and following car control flexibility.