Receding Horizon Optimization Research Articles

Design and analysis of electric power-assisted steering in vehicles for mountain forests

This study investigates the problem of vehicle exploitation in the mountainous and wooded regions of the Russian Federation. Considering how critical rollover accidents are in the logging industry, it is vital to ensure safe and stable driving. This study aims to develop a model of electric power steering (EPS) control using a quasi-continuous sliding-mode controller in MATLAB/Simulink. Experiments were carried out under the following conditions: maximum slope angle, 30°; maximum steering angle, 30°; maximum driving speed, 7 m/s. The dynamic characteristics of the suspension and EPS systems were optimized using the receding horizon optimization technique. The theoretical and experimental results suggest that the model reaches its critical state of stability (−0.2) at a speed range of 0–7 m/s with a steering angle of 25°. After optimization, critical stability (0.3) occurs at the driving speed of 7 m/s when the steering angle is 30°. The paper discussed the possibility of installing the EPS control system into vehicles that operate in mountainous wooded areas.

Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

Multiobjective adaptive car-following control of an intelligent vehicle based on receding horizon optimization

NMPC in active subspaces: Dimensionality reduction with recursive feasibility guarantees

Dimensionality reduction of decision variables is a practical and classic method to reduce the computational burden in linear and Nonlinear Model Predictive Control (NMPC). Available results range from early move-blocking ideas to singular-value decomposition. For schemes more complex than move-blocking it is seemingly not straightforward to guarantee recursive feasibility of the receding-horizon optimization. Decomposing the space of decision variables related to the inputs into active and inactive complements, this paper proposes a general framework for effective feasibility-preserving dimensionality reduction in NMPC. We show how – independently of the actual choice of the subspaces – recursive feasibility can be established. Moreover, we propose the use of global sensitivity analysis to construct the active subspace in data-driven fashion based on user-defined criteria. Numerical examples illustrate the efficacy of the proposed scheme. Specifically, for a chemical reactor we obtain a significant reduction by factor 20−40 at a closed-loop performance decay of less than 0.05%.

Reliability optimization of flexible test system based on pyro-mechanical device products production driven

The quality evaluation of aerospace pyro-mechanical device products(PDPs) is very important to the safety of aerospace engineering. Aerospace PDPs are small-lot batch, multiple batches and one-off products which rely on the analysis of ignition test with limited samples. To accommodate multiple batches and small-lot batch of aerospace PDPs, the flexible test system(FTS) switches between different batches of products by the selection of instruments and channels. The conventional test mode is driven by the completion time of batch products, which usually leads to frequent switching of instruments and channels and affects the reliability of test system. Therefore, the FTS is configured according to the completion of production missions within a certain time period in the future. This paper considers the FTS of aerospace PDPs as a phased mission system, and designs the corresponding dynamic Bayesian network(DBN) model. A test of one batch is taken as a phase of phased mission system. In order to improve efficiency and optimize reliability of the aerospace PDPs FTS, the receding horizon optimization problem is constructed. Numerical experiments show that the FTS with phased mission model can optimize the test sequence during the test process, which improves efficiency and reliability of test system.

Data-driven finite control set model predictive speed control of an autonomous surface vehicle subject to fully unknown kinetics and propulsion dynamics

This article investigates the speed control problem of an autonomous surface vehicle (ASV) suffering from fully unknown kinetics and propulsion dynamics. A data-driven finite control set model predictive control (FCS-MPC) strategy is presented for tracking a desired surge velocity. In the learning loop, a data-driven neural predictor is present to learn the unknown input gains, model nonlinearities, and environmental disturbances simultaneously. Filtered data of vehicle states are recorded and used to update the neural network parameters. In the control loop, a pulsewidth-modulation-driven speed controller is developed based on the FCS-MPC technique. The optimal control actions are selected based on the receding horizon optimization with a prediction model. The learning error system of the data-driven neural predictor is proved to be input-to-state stable via Lyapunov analysis. A major feature of the proposed method is that optimal performance can be provided for speed tracking without using any a priori knowledge of model parameters or external disturbances. Simulation results substantiate the effectiveness of the proposed data-driven FCS-MPC strategy for an ASV to track a reference surge velocity.

Turbo-fan engine acceleration control schedule optimization based on DNN-LPV model

The acceleration control schedule (ACS) is crucial in determining the acceleration performance of an aero-engine and is usually acquired by optimization means. To reduce the computational burden in optimizing ACS and improve the ACS superiority, a local receding-horizon optimization method with the structure of model predictive control is proposed. Moreover, a novel online data-driven linear parameter varying model based on a special structure deep neural network (DNN) is proposed to establish the predictive model to ensure prediction accuracy and modeling efficiency. Compared with common neural networks, an extra multiplying layer is inserted between the last hidden layer and the output layer which gives the output description of the DNN the ability to be transformed into the state space equations. Then the local optimization of the ACS can be constructed as a quadratic constraint problem with the state space predictive model. The optimizations are carried out at different isotherms within the flight envelope and an isotherm-based ACS is achieved which can be applied to the full-flight envelope by linear interpolation. Simulation results demonstrate that not only the proposed novel predictive model can achieve a high prediction accuracy, but also the ACS can effectively protect the engine from overlimit during acceleration at both design and off-design points. Thus, the effectiveness of the proposed method is validated.

Novel Unbiased Optimal Receding-Horizon Fixed-Lag Smoothers for Linear Discrete Time-Varying Systems

This paper proposes novel unbiased minimum-variance receding-horizon fixed-lag (UMVRHF) smoothers in batch and recursive forms for linear discrete time-varying state space models in order to improve the computational efficiency and the estimation performance of receding-horizon fixed-lag (RHF) smoothers. First, an UMVRHF smoother in batch form is proposed by combining independent receding-horizon local estimators for two separated sub-horizons. The local estimates and their error covariance matrices are obtained based on an optimal receding horizon filter and the smoother in terms of the unbiased minimum variance; they are then optimally combined using Millman’s theorem. Next, the recursive form of the proposed UMVRHF smoother is derived to improve its computational efficiency and extendibility. Additionally, we introduce a method for extending the proposed recursive smoothing algorithm to a posteriori state estimations and propose the Rauch–Tung–Striebel receding-horizon fixed-lag smoother in recursive form. Furthermore, a computational complexity reduction technique that periodically switches the two proposed recursive smoothing algorithms is proposed. The performance and effectiveness of the proposed smoothers are demonstrated by comparing their estimation results with those of previous algorithms for Kalman and receding-horizon fixed-lag smoothers via numerical experiments.

An MPC auto-tuning framework for tracking economic goals of an ESP-lifted oil well

This paper proposes a novel economic requirements-oriented online tuning strategy for MPC, the design of which encompasses the well-known operational particularities of oil production wells with ESP installations, e.g. operational envelope, maximization of oil production, minimization of power consumption, and profit maximization. The proposed tuning strategy is based on an online receding horizon optimization framework that can interact with RTO layers and different MPC techniques in a computation time appropriate for real-time implementations. The benefits of the proposed method are demonstrated by a case study that investigates different tuning requirements applied to provide an optimal ESP-lifted operation. It shows that using economic goals for MPC tuning problems is an effective way to achieve improved ESP-lifted oil operation.

Adaptive Fading-Memory Receding-Horizon Filters and Smoother for Linear Discrete Time-Varying Systems

In this paper, an adaptive fading-memory receding-horizon (AFMRH) filter is proposed by combining the receding-horizon structure and the adaptive fading-memory method. In the recent finite horizon, state error covariance is adapted with an adaptive fading factor; then the process noise covariance matrix adaption is realized by adjusting the properties of systems. An AFMRH fixed-lag smoother is also proposed by combining the proposed AFMRH filtering algorithm and a Rauch–Tung–Striebel smoothing algorithm to improve the estimation accuracy. Because the proposed AFMRH filter and smoother are reduced to the optimal receding-horizon (RH) filter and smoother when all measurements have the same weight, the proposed adaptive RH estimators could provide a more general solution than the optimal RH filter and smoother. To reduce the complexity and improve the estimation performance of the proposed RH estimators, an adaptive horizon adjustment method and a switching filtering algorithm based on an adaptive fading factor are also proposed. In particular, the proposed adaptive horizon adjustment method is designed to be computationally efficient, which makes it suitable for online and real-time applications. Through computer simulation, the performance and adaptiveness of the proposed approaches were verified by comparing them with existing fading-memory approaches.

Two-Stage Energy Management Strategies of Sustainable Wind-PV-Hydrogen-Storage Microgrid Based on Receding Horizon Optimization

Hydrogen and renewable electricity-based microgrid is considered to be a promising way to reduce carbon emissions, promote the consumption of renewable energies and improve the sustainability of the energy system. In view of the fact that the existing day-ahead optimal operation model ignores the uncertainties and fluctuations of renewable energies and loads, a two-stage energy management model is proposed for the sustainable wind-PV-hydrogen-storage microgrid based on receding horizon optimization to eliminate the adverse effects of their uncertainties and fluctuations. In the first stage, the day-ahead optimization is performed based on the predicted outpower of WT and PV, the predicted demands of power and hydrogen loads. In the second stage, the intra-day optimization is performed based on the actual data to trace the day-ahead operation schemes. Since the intra-day optimization can update the operation scheme based on the latest data of renewable energies and loads, the proposed two-stage management model is effective in eliminating the uncertain factors and maintaining the stability of the whole system. Simulations show that the proposed two-stage energy management model is robust and effective in coordinating the operation of the wind-PV-hydrogen-storage microgrid and eliminating the uncertainties and fluctuations of WT, PV and loads. In addition, the battery storage can reduce the operation cost, alleviate the fluctuations of the exchanged power with the power grid and improve the performance of the energy management model.

Adaptive dual model predictive control for linear systems with parametric uncertainties

Adaptive dual model predictive control (DMPC) for linear systems with constant parametric uncertainties is investigated in this paper. In particular, chance constraints on output and input are considered. The online set-membership identification and recursive least squares are utilized for shrinking the uncertain parameter set and getting the estimation point of the parameters. The dual effect represented by the parameters' prediction error is considered in the receding horizon optimization problem for easing the impact of the uncertainties and improving the performance simultaneously. Chance constraints on output are tackled by converting into convex alternatives. The output tracking problem is transformed into a quadratically constrained quadratic-programming (QCQP) problem, which is computationally tractable. A numerical example is provided to illustrate the effectiveness of the proposed method.

Cooperative trajectory optimization of UAVs in approaching stage using feedback guidance methods

This paper mainly studies the problem of using UAVs to provide accurate remote target indication for hypersonic projectiles. Based on the optimal trajectory trends and feedback guidance methods, a new cooperative control algorithm is proposed to optimize trajectories of multi-UAVs for target tracking in approaching stage. Based on UAV kinematics and sensor performance models, optimal trajectory trends of UAVs are analyzed theoretically. Then, feedback guidance methods are proposed under the optimal observation trends of UAVs in the approaching target stage, producing trajectories with far less computational complexity and performance very close to the best-known trajectories. Next, the sufficient condition for the UAV to form the optimal observation configuration by the feedback guidance method is presented, which guarantees that the proposed method can optimize the observation trajectory of the UAV in approaching stage. Finally, the feedback guidance method is numerically simulated. Simulation results demonstrate that the estimation performance of the feedback guidance method is superior to the Lyapunov guidance vector field (LGVF) method and verify the effectiveness of the proposed method. Additionally, compared with the receding horizon optimization (RHO) method, the proposed method has the same optimization ability as the RHO method and better real-time performance.

A predictive controller for real-time energy management of plug-in hybrid electric vehicles

Battery aging can degrade the energy efficiency of plug-in hybrid electric vehicles (PHEVs) significantly. This paper presents a novel intelligent real-time energy management strategy (EMS) for PHEVs, integrating battery life and fuel consumption optimization. The two-objective offline optimization problem is solved by the dynamic programming (DP) approach to obtain the globally optimal solutions. For the real-time implementation, a model predictive control (MPC) scheme is combined with an adaptive neuro-fuzzy inference system (ANFIS) model. DP carries out a receding-horizon optimization using future traffic information. Level-set functions are exploited within the DP algorithm to reduce numerical errors and decrease the computational effort of the baseline DP approach. Contrary to the real-time EMSs with a pre-determined state-of-charge (SOC) reference, an ANFIS model provides the SOC reference online. The proposed method is evaluated in simulation over multiple real-time driving cycles and compared with the DP results and two other real-time approaches. The effect of prediction horizon length is also studied. The simulation results demonstrate that the developed method can optimize battery life and fuel consumption. The results indicate 93%–97% matching to those of optimal controller, that is much better compared to the two other tested approaches.

Receding horizon control and coordination of multi‐agent systems using polynomial expansion

AbstractThe present paper proposes a flexible and efficient methodology for constructing norm‐bounded optimal receding horizon control laws for a group of agents, each one of which is described as a repeated integrator of an arbitrary order and with a common input delay. The goal of each agent is to track the given target, while simultaneously avoiding other agents in the group. Polynomial expansion, together with appropriate subspace projection, is utilized in order to derive receding control law in the closed form. Properties of this control law have been investigated, and its link to a well‐known control strategy based on avoidance functions, which ensures collision avoidance in the case of first‐order integrator agents, has been derived.

Hybrid automaton-based disturbance-aware predictive control with receding horizon optimization for three-phase full-bridge inverters

A data-driven disturbance-aware predictive control policy is proposed for DC–AC power inverters based on receding horizon optimization approach. First a discrete event-driven hybrid automaton model has been constructed for the nonlinear inverter system dynamics. A control problem of infinite discrete state-space transition sequence optimization is formulated. A receding-horizon-optimization-based hybrid controller is designed to solve the discrete optimization problem piece-wisely on-line. Accordingly, a disturbance-aware adaptive control is proposed, the external disturbance is sampled and estimated by an on-line Recursive Least Square (RLS) algorithm. It is elaborated that the conventional PWM control solution is a subset of solutions of the proposed control strategy and the code-transition between them is provided. By adding extra PWM constraints to the proposed control strategy, an Optimal PWM Control Mode (OPCM) is introduced as example. The essence of OPCM is still a data-driven optimal solution based on MPC following pre-designated PWM constraints which is a modified sub-mode of ODCM and greatly reduces computational requirement. The proposed controller can freely operate under the original Optimal Discrete Control Mode (ODCM) and the OPCM. Numerical simulation results have verified that the proposed discrete control strategy has realized disturbance-aware adaptive control of DC–AC inversion against load-shift, and ODCM has better control performance than OPCM. In addition, the proposed modeling and control frame has potential to support other forms of control modes.

Explicit stochastic model predictive control for anti-pitching a high-speed multihull

Heave and pitch motion control of a high-speed multihull is important for restraining its large heave and pitch motions and improving its seaworthiness. However, stochastic ocean wave disturbances and strict input constraints on the T-foil and the flap highly increase difficulties in heave and pitch motion control. This paper proposes a explicit stochastic model predictive control (ESMPC) approach for anti-pitching the multihull without the violation of the control input constraints. A quadratic cost function is designed for the stochastic receding-horizon optimization of MPC under control input constraints, by considering the stochastic control model for the multihull as the predictive model. Then, the stochastic optimization is converted into deterministic optimization based on a Kalman-filter-based estimator and state mean prediction, and the ESMPC law is obtained by a combination of offline design and online search, which reduces the complexity of online optimization and enhances real-time control performance. The mean-square boundedness of stochastic system state is theoretically analyzed using the Lyapunov theory. The control effectiveness is illustrated by simulations and experiments, from which the pitch motion and the heave motion are reduced by 30% and 40%, respectively.

Stochastic configuration network based cascade generalized predictive control of main steam temperature in power plants

The main steam temperature (MST) in power plants suffers from nonlinearity and large time delay, which cause large overshoot and long settling time under widely used cascade proportion integration differentiation (PID) controller. In order to cope with the negative effects, we propose a stochastic configuration network (SCN) based cascade generalized predictive control (GPC) scheme to improve the performance of the MST. A three-layer SCN is employed to model the MST process. The SCN is constructed by two phases, i.e., initial phase and real-time phase. The initial phase determines the structure and primary parameters of the learner model using the stochastic configuration algorithm. The real-time phase employs weighted recursive least squares (WRLS) for building the real-time MST process model for GPC design. Taking into account some constraints of the MST process, Karush–Kuhn–Tucker (KKT) conditions are applied for solving the constrained receding-horizon optimization problem. The derived explicit solutions of GPC avoid the implicit form which usually has to be solved iteratively. Comparative simulations demonstrate the superiority of the proposed SCN based cascade GPC (SCN-CGPC). Finally, the proposed SCN-CGPC is implemented via a standalone external MST control system in a power plant. The effectiveness and practicability are validated with the real-world application.

Predictive Cost Adaptive Control: A Numerical Investigation of Persistency, Consistency, and Exigency

Among the multitude of modern control methods, model predictive control (MPC) is one of the most successful <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> – <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[4]</xref> . As noted in “Summary,” this success is largely due to the ability of MPC to respect constraints on controls and enforce constraints on outputs, both of which are difficult to handle with linear control methods, such as linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG), and nonlinear control methods, such as feedback linearization and sliding mode control. Although MPC is computationally intensive, it is more broadly applicable than Hamilton–Jacobi–Bellman-based control and more suitable for feedback control than the minimum principle. In many cases, the constrained optimization problem for receding-horizon optimization is convex, which facilitates computational efficiency <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[5]</xref> .

Model predictive control for switched systems with a novel mixed time/event-triggering mechanism

This paper investigates observer-based model predictive control (MPC) for switched systems with a mixed time/event-triggering mechanism. The problem of predictive control that can achieve receding horizon optimization is considered and solved by minimizing an upper bound of the quadratic cost function. Since the system state may not be fully measured in practice, state observers are employed to estimate. A mixed mechanism including adaptive event-triggering and time-triggering is proposed, which can be switched determined by a threshold describing system performance to better balance system resource utilization and performance requirements. Then, a closed-loop switched system subject to networked-time-delay is modeled. Piecewise Lyapunov function technique and average dwell time approach are utilized to ensure asymptotical stability. Afterwards, MPC controller construction problem is turned into a LMIs feasibility problem. A new solving method of sufficient conditions for co-design of the state observers, feedback controllers and mixed triggering mechanism is derived. Lastly, simulation examples illustrate the correctness and advantages of research content.

Multi-time scale coordinated optimization of an energy hub in the integrated energy system with multi-type energy storage systems

To comprehensively consider the impact of uncertainties of variable energy resources (VERs) and load demands on the scheduling plans, the paper proposes a multi-time scale coordinated optimization (MTSCO) method of the energy hub (EH) with multi-energy flows and multi-type energy storage systems (MESSs). The MTSCO involves with global optimization of day-ahead economic dispatch (DAED), local intra-day receding-horizon optimization (IDRHO) and real-time adjustment (RTA). DAED formulates the 24-period output of controllable devices integrated in EH through minimizing its operation cost of a single day. Following the formulated DAED plan, IDRHO minimizes the sum of energy purchase cost and MESSs’ penalty cost within every 15 min. Further, RTA provides real-time feedback to IDRHO and minimizes the total adjustment amount of all controllable devices every 5 min. The simulation results indicate that the proposed multi-time scale dispatching and MESSs enable to promote the EH economy, acquire better dispatching solution aiming at uncertainties of VERs and varying load demands and alleviate the grid’s compensation for fluctuation.