Fast Model Predictive Control Research Articles

Fast quadratic model predictive control based on sensitivity analysis and Wolfe method

AbstractThis paper proposes a new algorithm based on sensitivity analysis and the Wolfe method to solve a sequence of parametric quadratic programming (QP) problems such as those that arise in quadratic model predictive control (QMPC). The Wolfe method, based on Karush–Kuhn–Tucker conditions, has been used to convert parametric QP problems to parametric linear programming (LP) problems, and then the sensitivity analysis is applied to solve the sequence of the parametric LP problems. This strategy obtains sensitivity analysis‐based QMPC (SA‐QMPC) algorithm. It is proved that the computational complexity of SA‐QMPC is for a region of the initial conditions and for sufficiently small sampling time and all initial conditions, where and are the horizon time and dimension of the state vector, respectively. Numerical results indicate the potential and properties of the proposed algorithm.

Retraction Note: Alternating-direction-method-of-multipliers-based fast model predictive control for an aerial trees-pruning robot

Retraction Note: Alternating-direction-method-of-multipliers-based fast model predictive control for an aerial trees-pruning robot

Autonomous High-Speed Overtaking of Intelligent Chassis Using Fast Iterative Model Predictive Control

High-speed overtaking is one of the structured driving scenarios of autonomous vehicles (AVs), and safety and real-time control requirements must be fulfilled during this scenario. In this work, we develop an overtaking path-planning and tracking framework for AVs based on the improved artificial potential field (APF) and fast iterative model predictive control (MPC). The improved APF is used to plan a feasible, high-speed overtaking path by considering the constraints of vehicle dynamics and introducing the adjustment factor into the potential function to avoid local optimization and target unreachability problems. In addition, to reduce the computational burden and fulfill the real-time control requirements of trajectory tracking, a fast iterative MPC algorithm is proposed herein. This algorithm adds disturbances that possess the nonlinear characteristics of the real model by using a linearized model. In terms of vehicle configuration, we consider that the target vehicle has the four-wheel steer (4WS)-four-wheel drive (4WD) chassis configuration and the fast iterative MPC is also used to coordinate the conflict between 4WS and direct yaw moment control (DYC) to improve tracking accuracy and vehicle stability during overtaking. Simulink/Carsim co-simulation and hardware-in-the-loop (HIL) tests are conducted to verify the effectiveness of the proposed controller.

Workflow-Based Fast Model Predictive Cloud Control Method for Vehicle Kinematics Trajectory Tracking Problem

Workflow-Based Fast Model Predictive Cloud Control Method for Vehicle Kinematics Trajectory Tracking Problem

Fast Nonlinear Predictive Control Using Classical and Parallel Wiener Models: A Comparison for a Neutralization Reactor Process

The Wiener model, composed of a linear dynamical block and a nonlinear static one connected in series, is frequently used for prediction in Model Predictive Control (MPC) algorithms. The parallel structure is an extension of the classical Wiener model; it is expected to offer better modeling accuracy and increase the MPC control quality. This work discusses the benefits of using the parallel Wiener model in MPC. It has three objectives. Firstly, it describes a fast MPC algorithm in which parallel Wiener models are used for online prediction. In the presented approach, sophisticated trajectory linearization is performed online, which leads to computationally fast quadratic optimization. The second objective of this work is to study the influence of the model structure on modeling accuracy. The well-known neutralization benchmark process is considered. It is shown that the parallel Wiener models in the open-loop mode generate significantly fewer errors than the classical structure. This work’s third objective is to validate the efficiency of parallel Wiener models in closed-loop MPC. For the neutralization process, it is demonstrated that parallel models demonstrate better control quality using various indicators, but the difference between the classical and parallel models is not significant.

Fast Indirect Model Predictive Control for Variable Speed Drives

Fast Indirect Model Predictive Control for Variable Speed Drives

Fast adaptive regression-based model predictive control

Fast adaptive regression-based model predictive control

Eco-driving strategy for connected vehicles at signalized intersections considering human driver error

In recent years, eco-driving strategies based on connected vehicle (CV) technologies have been studied to assist human drivers to reduce fuel consumption and pollutant emissions. In this paper, a real-time eco-driving strategy for CVs that considers human driver error is proposed to improve both traffic and fuel efficiency at signalized intersections where CVs and human-driven vehicles (HDVs) coexist. Firstly, a human driver error estimation model is established using real-world driving data. Then, based on the signal phase and timing information, vehicle state information, and the estimated human driver errors, a constrained nonlinear optimal control problem (OCP) is proposed to calculate the optimal advisory speed of each CV. The trajectory of HDV is estimated by utilizing the Gipps’ car-following model. Fast stochastic model predictive control (SMPC) is employed to solve the proposed OCP effectively. At last, simulation studies and real-vehicle experiments are conducted in various scenarios to verify the performance of the proposed strategy. Simulation and experiment results indicate that compared with the baseline strategies, the proposed eco-driving strategy can significantly reduce travel time and fuel consumption while ensuring the real-time performance.

Data-driven identification and fast model predictive control of the ORC waste heat recovery system by using Koopman operator

Organic Rankine Cycle (ORC) has received its wide application in low-grade waste heat recovery (WHR) technology for its significant performance and easy access to its components. Given the highly fluctuating nature of the waste heat source, Model Predictive Control (MPC) is usually utilized to realize the reasonable adjustment of ORC based WHR systems and has performed satisfactorily. In order to apply MPC, a relatively precise model should be built up for the ORC system which ensures acceptable control performance. However, popular modeling methods based on the mechanism of the ORC establish the high-dimensional nonlinear model and suffer from computational costs when utilizing complicated nonlinear MPC. Even if linear MPC is employed for the purpose of reducing the calculation amount, the model obtained by linearization near the operating points usually makes it valid locally, thus bringing about suboptimal or unstable control performance. To address this problem, the Koopman operator is introduced for the data-driven identification and MPC of the ORC system. Koopman identification constructs a linear model in the lifted space in which the ORC system possesses nearly global linear evolution and enables the prediction of the nonlinear ORC dynamics from measurement data. In view of the possible online calculation burden increment caused by the rise of variable dimension through lifting action, the fast Koopman MPC (FKMPC) algorithm is thus proposed based on the invariance of the Hessian matrix of the optimization problem to shorten the computing time. Simulations on setpoint tracking and disturbance rejection are performed to verify the established model accuracy and the control effectiveness of the proposed strategy in comparison with other approaches.

Optimal Active Vibration Control of Tensegrity Structures Using Fast Model Predictive Control Strategy

Active vibration control of tensegrity structures is often challenging due to the geometrical nonlinearity, assemblage uncertainties of connections, and actuator saturation of controllers. To tackle these technical difficulties, a fast model predictive control (FMPC) strategy is herein implemented to effectively mitigate the structural vibration. Specifically, based on the explicit expression form of the Newmark- β method, the computation of the matrix exponential is avoided and replaced by one online and two offline transient analyses at each sampling instant on the structure, and the optimal control input is attainted from the second-order dynamic equation without forming an expanded state-space equation. Meanwhile, the artificial fish swarm algorithm (AFSA) is embedded to automatically derive optimal arrangement of actuators with the selection of a reasonable objective function. Two illustrative examples, including two standard and clustered tensegrity beams and a clustered tensegrity tower, have been fully investigated. The outcomes from illustrative examples prove the effectiveness and feasibility of the proposed method in optimal active vibration control of tensegrity structures, implying a promising prospect of the investigated approach in analyzing and solving relevant engineering problems.

Alternating-direction-method-of-multipliers-based fast model predictive control for an aerial trees-pruning robot

Alternating-direction-method-of-multipliers-based fast model predictive control for an aerial trees-pruning robot

Fast Model Predictive Control System for Wave Energy Converters With Wave Tank Tests

At present, the mainstream solution for the control of wave energy converters (WECs), the model predictive control (MPC)-like method, faces a gap between simulation-based research and practical application. Two of the major difficulties are the online computation burden and the requirement of real-time wave information. In this study, a fast solving strategy is proposed at the level of quadratic programming (QP), where a tailored warm-start algorithm is designed and combined with an early stop technique. Simulations show that only one iteration of interior-point method (IPM) suffices to reach over 95% efficiency of the exact MPC, which significantly speeds up the computation. The fast strategy is then deployed on the real-time controller of a prototype WEC platform. During this implementation, the instantaneous wave force is estimated by a Kalman filter entirely based on the basic feedback signals: position, velocity, and the generator current, while future wave forces are predicted by an autoregressive model. The wave tank test confirms that the proposed fast MPC is capable of being executed at a high frequency, achieving stable operations within constraints, and reaching satisfactory energy efficiency under real wave-body interactions.

A Fast Model Predictive Control Framework for Multi-Float and Multi-Mode-Motion Wave Energy Converters

Recently, multi-float and multi-mode-motion wave energy converters (M-WECs) have been developed to improve energy conversion capability. Although model predictive control (MPC) can be very effective to solve the constrained energy maximization control problem of point absorber WECs, the increased complexity of the M-WEC hydrodynamics can bring significant challenges due to computational demand. This brief proposes a novel computational-efficient fast MPC (FMPC) design method for the M-WECs requiring complex linear hydrodynamic models. The controller design objective is to maximize the energy conversion with some available wave forecasting information and to satisfy state and control input constraints to ensure safe operation. The main advantage of the proposed FMPC is the reduced computational burden with a negligible impact on performance. A demonstrative numerical simulation based on a 1:50 laboratory-scale M-WEC design, M4, for which linear hydrodynamics has been verified experimentally, is presented to verify the efficacy of the proposed control method in terms of both computational load and energy output.

Fast Distributed Model Predictive Control Method for Active Suspension Systems

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

Model Predictive Control of Time-Varying Aberrations for Sensorless Adaptive Optics

We propose a model predictive control (MPC) method to compensate for the time-varying phase aberrations in a sensorless adaptive optics (AO) system. The approximate model for the point spread function (PSF) of the imaging system with phase diversity is mathematically derived, and its validity is verified. The proposed MPC, which considers the operating limits of the deformable mirror (DM), computes the optimal applied voltage of each actuator over a prediction horizon, and the receding horizon scheme is applied as feedback control. Numerical case studies with the time-varying phase aberrations generated by an atmospheric turbulence simulator are presented to demonstrate the correction performance of the MPC strategy. The proposed method caused a 50% improvement in the reduction in residual aberration, making it competitive with a saturation linear quadratic regulator (LQR). The computational feasibility of the proposed method is validated using fast MPC, which approximates the primal barrier method.

The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting

Abstract. Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) that can deliver this information in fast and computational efficient manner, is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. anomaly correlation coefficient (ACC), continuous ranked probability (skill) score (CRPS and CRPSS), and Brier skill score (BSS), in comparison to a climatological reference hindcast. Aggregating the results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions to previous large-scale forecasting systems. With our study, we show that a hybrid framework is able to bring location-specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time, our hybrid approach is flexible and fast, and as such, a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance, as part of a fast model-predictive control framework.

A new fast nonlinear model predictive control of parallel manipulators: Design and experiments

High-speed parallel manipulators are characterized by fast sampling rates and challenges owing to the presence of constraints, high nonlinearities, uncertainties, and fast dynamics. The development of a reliable nonlinear model predictive control (NMPC) approach for this type of robotic systems is challenging because of the complex dynamics that involves high computational burden. To address this control problem, this paper proposes a new fast NMPC strategy applied to high-speed parallel robots. Based on (i) a parameterization technique, (ii) a fast gradient solver, (iii) a proportional integral derivative (PID) control term, (iv) a nonlinear dynamic model, and (v) an artificial neural network (ANN) model, two fast NMPC frameworks were developed to reduce the computational cost of the classical NMPC and address online implementation. In this study, we focus on improving the performance of the standard predictive control strategy outside of the range of its classical applicability by developing a fast NMPC controller for high-speed parallel kinematic manipulators (PKMs). Numerical simulations and real-time experimental results were presented and discussed to validate the relevance of the proposed controllers. Experiments were conducted on a four-degree-of-freedom (4-DOF) PKM called VELOCE developed in our laboratory. The results show that the proposed solution outperforms the original scheme in terms of real-time tracking performance.

Fast warm-start of F-MPC strategy for automotive cruise control with mode switching

A fast iterative algorithm of nonlinear model predictive control is proposed to solve the warm-start problem of the fast model predictive control (F-MPC) method for real-time application with mode switching. Because an incorrect initial guess of F-MPC creates problems such as excessive iterations or even solution failures, the proposed iterative algorithm aims to provide the initial value of F-MPC quickly and efficiently. The idea is to decompose the original nonlinear system into a main linear part and a nonlinear part, which is regarded as measurable disturbance. Then the explicit optimal solution of the “disturbed” system is derived, the obtained series of control inputs are applied to the system, the system state and consequently the “disturbance” are updated, and finally the process is repeated until convergence. The calculated iterative result can be used as the initial solution for F-MPC especially for maneuver mode switching. Automotive cruise control is used as an example for validation, and it is shown that the control strategy has superior adaptability for mode switching, guaranteeing the given safety constraints as well as significantly reducing the computational load.

Fast and Efficient MPC Approaches for Multilevel Drives Considering Cost Function Terms Dependency

—Model predictive control (MPC) for multilevel converter has a multi-term cost function that requires high computation burden. In this paper, the deviation terms considered are classified into two categories according to their dependency on the voltage vector or the switching states of the multilevel converter. Subsequently, two approaches to reduce the computation burden are proposed. Mathematical models for the proposed MPC approaches are discussed and analyzed. An experimental platform for a fivelevel T-type multilevel induction drive is used to validate the proposed study. The experimental results show that the proposed approaches have substantially reduced the MPC computation burden compared to the conventional MPC techniques.

Common-Mode Voltage Mitigation of Dual T-Type Inverter Drives Using Fast MPC Approach

This article proposes an intensive study for common-mode voltage (CMV) elimination/reduction in a five-level dual T-type multilevel converter (MLC) drive. An advanced and fast model predictive control (MPC) approach is developed for CMV reduction (CMVR) and CMV elimination based on the preselection of the switching states (SS). In addition, the proposed approach considers the torque control and the capacitor balancing of the proposed drive as essential aspects of MLC drives. A detailed study of the impact of the converter SS on the CMV is presented. The main features of the proposed MPC approach are reducing the complexity of the cost-function, the tuning burden of the weighting factors, and the computation time by more than 80% compared to conventional MPC techniques. A hardware implementation based on Silicon Carbide T-type MLC is built. The experimental results demonstrate the effectiveness of the proposed MPC approach to mitigate/eliminate the CMV along with achieving the torque command and balancing the dc-link capacitors. Unlike conventional CMVR techniques, the proposed approach reduces the CMV by 75% of its normal values without jeopardizing the harmonic contents, drive efficiency, and flux as well as torque ripples.