Adaptive Model Predictive Control Research Articles

Adaptive Tube Model Predictive Control for Manipulating Micro- and Nanoparticles in Fluid Suspensions Under Global External Fields

Adaptive Tube Model Predictive Control for Manipulating Micro- and Nanoparticles in Fluid Suspensions Under Global External Fields

Adaptive model predictive control based on neural networks for Hover attitude tracking with input saturation and unknown disturbances

The anti-disturbance control of quadrotor attitude tracking under saturation constraints is a difficult problem. In this paper, a neural network-based model predictive controller for quadrotor systems with input saturation and external disturbances is developed. The unmodeled dynamics and external disturbances of the system are simplified to the disturbance superimposed on the nominal system, and the gradient descent neural networks are used to complete the estimation and compensation of the disturbance. The adaptive model predictive controller is designed based on the nominal system. The disturbance value estimated by the neural network adaptively adjusts the control constraints in the model predictive controller. The robustness and anti-disturbance of the designed controller are analyzed. The experiments show that, compared to the robust model predictive control, the algorithm proposed in this paper reduces the steady-state mean errors of the yaw, pitch, and roll attitude channels of the Hover system. Specifically, the algorithm results in a decrease of 2.622%, 2.292%, and 1.192% without external disturbances and 2.056%, 4.17%, and 0.956% with outside disturbances. Experimental results confirm the effectiveness of the proposed method.

Event-Triggered Communication in Cooperative, Adaptive Model Predictive Control of a Nuclear Power Plant’s Turbo–Generator Set

This paper discusses the issue of optimizing the communication between the components of a cooperating control system formed by a pair of MPC controllers of a nuclear power plant turbine set using online recursive least squares identification. It is proposed to use event-triggered communication, i.e., sending information only at selected time instants, as opposed to the standard approach where communication is triggered by time (time-triggered approach). The aim of this paper is to propose a change in the method of information exchange in the case of asynchronous communication between control system components and to prove its suitability for the selected application. Resignation from continuous communication in favor of sending information only at selected moments allows the load on the communication network to be reduced by approximately 90% while maintaining the quality of control.

Intelligent vehicle obstacle avoidance path-tracking control based on adaptive model predictive control

Abstract. In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model. According to the relationship between the predictive horizon and vehicle speed in the MPC algorithm, an adaptive path-tracking control strategy which can update the prediction horizon in real time is proposed to improve the accuracy of vehicle path tracking. To increase the vehicle stability, a sideslip angle and an acceleration control variable are added to the vehicle dynamics model as soft constraint conditions. The proposed method is simulated based on a CarSim and MATLAB/Simulink co-simulation platform. The simulation results show that the maximum lateral path deviation and the maximum centroid sideslip angle of the designed controller are 0.13 m and 0.4∘, respectively. Compared with the traditional MPC, the adaptive MPC maximum lateral path deviation and the maximum centroid sideslip angle are reduced by 0.51 m and 1.57∘, respectively, which proves the effectiveness of the proposed method.

Robust adaptive tube tracking model predictive control for piece‐wise constant reference signals

AbstractRobust tracking of piece‐wise constant reference signals for constrained systems with parametric plant uncertainty and additive disturbances is addressed in this paper. The parametric uncertainty is decreased online by set‐membership estimation and a nominal model is updated for improving set‐point tracking. The online estimated parametric uncertainty is used for an online‐determined terminal set which enlarges the set of reachable references close to the system constraints when compared to an offline worst‐case consideration. An artificial target state is introduced which can deviate from the nominal target state. This new target state is used to ensure recursive feasibility for unreachable references and changes in the reference signal. Moreover, a novel “recovery mode” is specified which is deployed in case the new nominal model yields an infeasible control problem. Control algorithms are developed for time‐invariant systems and systems with arbitrarily fast changing plants but known relative bounds. Constraint satisfaction and ‐stability are guaranteed for the proposed algorithms. Controlling the engine load of a self‐propelled work machine is used as a practical example.

Adaptive model predictive control with ensembled multi-time scale deep-learning models for smart control of natural ventilation

Model predictive control (MPC) offers promising solutionsfor the smart control of natural ventilation. However, challenges arise in constructing precise models for such nonlinear systems, preserving accuracy over a long-prediction horizon while capturing short-term dynamics, and handling disturbances uncertainties. This study proposes a novel Ensembled Multi-time scale deep-learning-based Adaptive Model Predictive Control (EMA-MPC) system. It integrates an ensembled Long-Short-Term Memory (ensembled-LSTM) model, comprising two LSTM models for fast and slow time-scale dynamics, respectively, and continuously evolving to changing conditions through online model adaptation. A bound control module is incorporated as an additional safety mechanism ensuring the environmental control within the desired threshold during unforeseen scenarios. A multi-objective optimization problem is formulated to maintain indoor air temperature and CO2 concentration within the predefined comfort range while optimizing energy efficiency by controlling automated windows in a naturally ventilated room in winter. The EMA-MPC system demonstrates superior performance in balancing indoor air quality, temperature regulation, and energy efficiency. The ensembled-LSTM model significantly reduces the mean absolute error by 78.6% and 88.9% for CO2 and indoor air temperature predictions respectively, against a single LSTM model. The proposed EMA-MPC system achieves an 86% reduction in unmet CO2 hours compared to rule-based control and reduces occupied hours with temperature below 19 °C by 44% and 93% compared to enhanced MPC and basic MPC, respectively, while maintaining similar heating demand as other controllers. In conclusion, the proposed EMA-MPC system reduces modeling efforts and provides an effective approach towards reliable use of ML models in smart building control.

Coordination Control of Active Steering and Direct Yaw Control for the Articulated Steering Vehicle

Lateral stability is vital to vehicle handling stability and traffic safety. It is also a crucial factor for the path-tracking ability of the vehicle in the intelligent transportation system (ITS). Most of the research focuses on vehicles with an Ackerman steering system. The ASV (articulated steering vehicle) has a lower steering radius. Thus, it is widely used in some special vehicles, such as mining and construction vehicles. The ASV has weaker lateral stability than the vehicle with an Ackerman steering system. To improve the stability of an ASV, the nonlinear lateral dynamic model is established and validated by field test. With the lateral dynamic model, the steering characteristic of the ASV is analyzed. Based on the stability criteria analysis, the vehicle sideslip angle and angular velocity phase portrait are chosen as the stability indicator. An integrated AASS (active articulated steering system) DYC (direct yaw control) controller based on the adaptive MPC (model predictive control) method is designed according to the track on the phase plane. The double lane-change and 0.7 Hz sine with dwell maneuver are initiated based on the integrated vehicle dynamic model. The results suggest that the provided controller has a better stability performance than the current antiwindup PID control algorithm. It lays a good foundation for the vehicle safety and path tracking of ASV in the ITS.

Integrated control method for path tracking and lateral stability of distributed drive electric vehicles with extended Kalman filter–based tire cornering stiffness estimation

Aiming at the lack of adaptability of vehicle parameters under extreme conditions, this paper proposes an integrated control method for path tracking and lateral stability of distributed drive electric vehicles based on tire cornering stiffness adaptive model predictive control (MPC) scheme. The control method integrates active front steering and direct yaw control to improve the path tracking and lateral stability performance of distributed drive electric vehicles. Firstly, considering the influence of vertical load transfer, the tire cornering stiffness is estimated based on the extended Kalman filter (EKF) algorithm. Then, using this online updated tire cornering stiffness value, an adaptive MPC controller for path tracking and lateral motion stability of distributed drive electric vehicles is constructed. Meanwhile, a fuzzy sliding mode control (Fuzzy-SMC)–based longitudinal velocity controller is established to ensure the accuracy of velocity tracking. Also, according to the distributed driving characteristics of the controlled system, a tire torque distributor based on weighted pseudo-inverse (WPI) is designed with the minimum tire load rate as the optimization objective, where the road adhesion condition and the maximum output torque of the motor are considered as constraints. The simulation results show that the proposed integrated control method based on tire cornering stiffness adaptive model predictive control is robust and effective. Compared with constant cornering stiffness model predictive control–based control method, it can improve the vehicle path tracking accuracy and lateral motion stability under extreme conditions.

A control strategy of heating system based on adaptive model predictive control

A control strategy of heating system based on adaptive model predictive control

Nonparameteric Event-Triggered Learning With Applications to Adaptive Model Predictive Control

In this paper, an event-triggered online learning problem for Lipschitz continuous systems with nonlinear model mismatch is considered, with the aim of building a data-efficient non-parametric estimation approach for learning-based control. The system considered is composed of known linear dynamics and unknown nonlinearity, and the main focus of this work includes the design and analysis of event-triggered learning mechanisms, and the application of the learning method to adaptive model predictive control (MPC). First, a sample grid-based event-triggering mechanism and a prediction uncertainty-based event-triggering mechanisms are designed on the basis of the lazily adapted constant kinky inference framework. Then, the properties of the designed event-triggered learning methods are analyzed, and it is proved that the proposed approach provides error-bounded predictions with limited computational complexity. Third, a tube-based adaptive MPC design approach is developed utilizing the proposed event-triggered learning approach, and the closed-loop stability of the adaptive MPC is analyzed and proved based on the properties of the event-triggered learning algorithms. Implementation issues are discussed, and the effectiveness of the results is illustrated by numerical examples and comparative simulations.

Recurrent Neural Network-Based Robust Adaptive Model Predictive Speed Control for PMSM With Parameter Mismatch

Due to the fast dynamic response and ability to handle the constraints, model predictive control (MPC) is becoming an exciting and widely applied approach for permanent magnet synchronous motor (PMSM). However, the control performance of conventional MPC is significantly affected by the model parameter mismatches. In addition, the high computational volume is also a limiting factor of MPC. This paper <inline-formula><tex-math notation="LaTeX">$^{\prime }$</tex-math></inline-formula> s main objective is to solve these problems by proposing an advanced control structure. First, the parameter mismatches are estimated online by a discrete-time mechanical parameter observer (DTMPO), then a robust adaptive model predictive speed control (RA-MPSC) is designed to suppress the influence of parameter mismatches. Secondly, a recurrent neural network (RNN) based algorithm is introduced to compute the RA-MPSC control law by solving an optimization problem in real-time. Lastly, the simulation and experimental results are presented to validate the performance of the proposed method.

Data assimilation and control system for adaptive model predictive control

Model-based control of complex systems is a challenging task, particularly when the system model involves many uncertain elements. To achieve model predictive control of complex systems, we require a method that sequentially reduces uncertainties in the system model using observations and estimates control inputs under the model uncertainties. In this work, we propose an extended data assimilation framework, named data assimilation and control system (DACS), to integrate data assimilation and optimal control-input estimation. The DACS framework comprises a prediction step and three filtering steps and provides adaptive model predictive control algorithms. Since the DACS framework does not require additional prediction steps, the framework can even be applied to a large system in which iterative model prediction is prohibitive due to computational burden. Through numerical experiments in controlling virtual (numerically created) fusion plasma, we demonstrate the effectiveness of DACS and reveal the characteristics of the control performance related to the choice of hyper parameters and the discrepancies between the system model and the real system.

Model predictive control for compliant feed drives with offset-free tracking behavior

Industrial machine tool feed drives are predominantly controlled by cascade control due to their low tuning complexity and inherent robustness. However, the cascaded structure requires the inner cascades to have higher dynamics than the outer cascades, which limits the achievable dynamic accuracy. Direct control approaches, which substitute the position and velocity cascade, offer the potential to utilize the unused potential. A promising approach is model predictive control (MPC), which optimizes the manipulated variable with a plant model along a prediction horizon. However, model uncertainties between the nominal model and the real plant lead to tracking errors. Therefore, this paper presents, a linear MPC (LMPC) and an adaptive MPC (AMPC) with an additional integral action to robustly compensate for model mismatches. Both controllers use a compliant model, are real-time capable with a sample rate of {{2},textrm{kHz}} and consider state and input space constraints. The AMPC accounts for position-varying stiffness and friction. The controllers are experimentally compared with classical P-PI cascaded control on a ball screw drive. They show a tracking error reduction of {{37}{,%}} (LMPC) and {{44}{,%}} (AMPC) during a high speed motion profile and an increase in bandwidth of {{180}{,%}} (LMPC) and {{184}{,%}} (AMPC), resulting in significantly improved dynamic accuracy.

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Adaptive model predictive control for grid‐forming converters to achieve smooth transition from islanded to grid‐connected mode

AbstractOne of the main capabilities of grid‐forming converters is the ability to operate in both islanded and grid‐connected modes. The grid‐forming converters work autonomously and behave as an AC voltage source when they are isolated from the bulk power system. In a grid‐connected mode, it is necessary for them to offer a set of grid‐forming capabilities for enhancing system inertia and strength. A grid‐forming converter may experience significant fluctuations in voltage, current, and frequency when the transition occurs. This paper investigates an improved adaptive model predictive control (AMPC) method for grid‐forming converters to achieve a smooth transition from islanded to grid‐connected mode and balance the contradiction between frequency stability and response speed. The AMPC method uses the angular frequency and phase angle as the performance indicators and introduces the angular frequency variations to adaptively adjust its weight coefficient in the objective function. The implementations and effects of the proposed AMPC method on the grid‐forming converter are theoretically evaluated. Compared to the conventional control, the proposed control scheme can not only suppress voltage, current, and frequency fluctuations, but also achieve a smooth transition. Additionally, the frequency control strategy in the islanded mode is improved for better independent operation. Finally, experiment tests on a controller‐level hardware‐in‐the‐loop simulation platform are carried out to validate the proposed control strategy for achieving a smooth transition.

A Data Mining/ANFIS and Adaptive Control for Detection and Mitigation of Attacks on DC MGs

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

A Novel Integral Reinforcement Learning-Based H∞ Control Strategy for Proton Exchange Membrane Fuel Cell in DC Microgrids

In this paper, a novel integral reinforcement learning (IRL) based H∞ control algorithm is proposed for proton exchange membrane fuel cells (PEMFCs) to enhance its performance in DC microgrids. The proposed control algorithm produces superior dynamic and steady-state responses without requiring the system model information. The stability of the proposed control algorithm is rigorously proven. Furthermore, its superiority over the PI control, model predictive control (MPC), fuzzy PI control and adaptive MPC is analyzed. Hardware-in-the-loop experimental results are given to verify the effectiveness of the proposed control algorithm.

Vehicle Platooning for Merge Coordination in a Connected Driving Environment: A Hybrid ACC-DMPC Approach

This study proposes a vehicle platooning algorithm to minimize the disruption from a lane-changing maneuver. Towards achieving this objective, while most studies emphasize the interaction between the lane-changing vehicle and vehicles directly or indirectly impacted by the lane-changing maneuver (i.e., the followers of the lane-changing vehicle in the target lane after the lane-changing maneuver), this study will focus on the leaders of the lane-changing vehicle in the target lane. Assuming the connectivity between vehicles, we propose a novel approach to implement both Adaptive Cruise Control (ACC) and Model Predictive Control (MPC) for platoon control. The proposed hybrid algorithm will create a forward-moving shockwave, resulting in an additional gap for the lane-changing vehicle. Simulation results indicate the ability of the proposed approach to generate an additional gap for the lane-changing vehicle and consequently, minimizing and potentially eliminating the disruption from the lane-changing maneuver.

Trajectory Planning for an Articulated Tracked Vehicle and Tracking the Trajectory via an Adaptive Model Predictive Control

This paper focuses on the trajectory planning and trajectory tracking control of articulated tracked vehicles (ATVs). It utilizes the path planning method based on the Hybrid A-star and the minimum snap smoothing method to obtain the feasible kinematic trajectory. To overcome the highly non-linearity of ATVs, we proposed a linear-parameter-varying (LPV) kinematic tracking-error model. Then, the kinematic controller was formulated as the adaptive model predictive controller (AMPC). The simulation of the path planning algorithm showed that the proposed planning strategy could provide a feasible trajectory for the ATVs passing through the obstacles. Moreover, we compared the AMPC controller with the developed controller in four scenarios. The comparison showed that the AMPC controller achieved satisfactory tracking errors regarding the lateral position and orientation angle errors. The maximum lateral distance error by the AMPC controller has been reduced by 72.4% compared to the standard-MPC controller. The maximum orientation angle error has been reduced by 55.53%. The simulation results confirmed that the proposed trajectory planning and tracking control system could effectively perform the automated driving behaviors for ATVs.

Adaptive diagnosis and non-fragile predictive control for HFV engine with non-Gaussian uncertain output

This study presents an adaptive fault estimation and model predictive fault-tolerant control (FTC) for the hypersonic vehicle engine with non-Gaussian uncertain output jet plume. The non-Gaussian probability density function (PDF) describing the plume velocity in a stochastic system is approximated by Type II fuzzy radial basis functions. In the fault observer, the novel prey adaptive estimation method is designed for the valve faults with different amplitudes. Ultimately, this fuzzy-prey fusion adaptive observer shields perturbation and accurately estimates complex faults including incipient and intermittent faults. A predictive tolerant controller compensates for all the faults effects and non-fragile compensation factors of FTC eliminate the perturbation interference, thence the output PDF matches with the expected value. Finally, the more refined internal structure of plume output distribution replaces the local and rough valve position variables, helping this study realizes the hypersonic vehicle engine integrated refine control. Simulation result verifies the effectiveness of the proposed approaches and the superiority compared with the existing method.