Model-free Adaptive Predictive Control Research Articles

Data‐driven model‐free predictive control for microstage with coupling and hysteresis nonlinearities

SummaryThe precision motion control problem is investigated in this paper for microstages with cross‐axial coupling and hysteresis. Cross‐axis coupling generally results in stress‐stiffening effects, thereby causing time‐varying dynamics in the microstages. Additionally, when a microstage is driven by piezoelectric actuators (PEAs), the hysteresis effect of the actuator itself must also be considered. Modeling the microstages becomes complicated when both nonlinear characteristics, coupling and hysteresis, coexist. To address this challenge without the need for modeling, a novel data‐driven model‐free predictive control scheme called first‐order tensor‐vector product polynomial approximation based model‐free predictive control (TPPA ‐MFPC is proposed. TPPA ‐MFPC solely relies on the sampling input/output (I/O) data of the systems. The main concept behind TPPA ‐MFPC is to derive a linear approximation model using the I/O data collected during operation. This linear approximation model then serves as a nominal model in a predictive controller, enabling the control of the microstages. Finally, the effectiveness of the proposed TPPA ‐MFPC scheme and the performance improvement over existing model‐free schemes, for example, proportion integration differentiation control (PID), model‐free adaptive control (MFAC), model‐free adaptive predictive control (MFAPC), data‐dependent LMI (DDLMI), and data‐enabled predictive control (DeePC) are demonstrated in the simulation examples with a 2‐degree of freedom (DOF) multileaf spring‐based microstage driven by PEA.

Event-Triggered Model-Free Adaptive Predictive Control for Networked Control Systems Under Deception Attacks

Event-Triggered Model-Free Adaptive Predictive Control for Networked Control Systems Under Deception Attacks

Optimal Output Tracking for Switched Systems Under DoS Attacks: A Model-Free Adaptive Predictive Control Method

Optimal Output Tracking for Switched Systems Under DoS Attacks: A Model-Free Adaptive Predictive Control Method

Distributed Model-Free Adaptive Predictive Control for MIMO Multi-Agent Systems With Deception Attack

Distributed Model-Free Adaptive Predictive Control for MIMO Multi-Agent Systems With Deception Attack

Distributed model-free adaptive predictive control of traffic lights for multiple interconnected intersections

Distributed model-free adaptive predictive control of traffic lights for multiple interconnected intersections

Data-Driven Operation of Flexible Distribution Networks with Charging Loads

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs.

An Improved Method of Model-Free Adaptive Predictive Control: A Case of pH Neutralization in WWTP

pH neutralization reaction process plays a crucial role in Waste Water Treatment Process (WWTP). Traditional PID Proportion Integral Differential, (or even advanced PID control) algorithms have poor performance on WWTP due to the strong non-linearity, large time lag, and large inertia characteristics of pH neutralization. Therefore, finding a superior control method to maintain the pH value of wastewater within the normal range will greatly help to improve the efficiency and effectiveness of wastewater treatment. The chemical reaction mechanism of pH neutralization reaction process is first analyzed, and a mechanistic model of pH neutralization reaction process is developed based on the reaction of ions during acid-alkali neutralization and the electric balance equation. Then, combining the characteristics of generalized predictive control and Model-Free Adaptive Control (MFAC), a Model-Free Adaptive Predictive Control (MFAPC) method based on compact format dynamic linearization is introduced. An Improved Model Free Adaptive PI Predictive Control algorithm (IMFAPC) with proportional (P) and integral (I) algorithms is proposed to further improve the control performance. IMFAPC is proposed on the basis of MFAPC, combining the advantages of generalized predictive control, introducing a PI module consisting of error and error sum, and predicting the PI module, making it possible to produce more accurate constraints on the control inputs, avoiding increasing errors, and improving the control effect of delayed systems at the same time. pH neutralization process simulation experimental results show that compared with the ordinary Model-Free Adaptive Control (MFAC) and MFAPC, the IMFAPC control algorithms has the best performance in terms of accuracy, overshoot, and the robustness.

Event-triggered model-free adaptive predictive control for multi-area power systems under deception attacks

This paper focuses on the event-triggered model-free adaptive predictive load frequency control (LFC) problem for multi-area power systems under false data injection (FDI) attacks. First, the power system model is assumed to be unknown, and the nonlinear power system is converted into an equivalent linear data model using the input and output data of the powerline system. The FDI attacks on the power system is also modeled, and a Bernoulli stochastic process is used to represent whether the data is transmitted successfully or not. Second, the equivalent linear data model to predicts upcoming events, and a new event-triggered model-free adaptive predictive control scheme is designed via the predictions. And a RBF neural network disturbance estimator is designed to estimate and compensate for the effects caused by disturbances, and then power system stability is analyzed. An event-triggered scheme is also developed in the design of the LFC in order to save communication and computational burden. The results show that the designed control algorithm is data-driven independent of the power system structure and does not require the measurement of any system state signals. Finally, the effectiveness and correctness of the scheme is verified by a three-area numerical example and simulation results.

Control of small pressurized water reactors under single-loop operation based on MFAPC theory

Small pressurized water reactors can supply power to ships, and single-loop operation is adopted to improve maneuverability under some abnormal conditions. Large key parameter fluctuations and the potential risk of human errors occur in conventional single-loop operation. Therefore, an automatic rapid power reduction method is proposed to improve the control performance of single-loop operation. An automatic rapid power-reduction method based on conventional control systems was tested. Poor power control performance and large fluctuations in steam generator water level were observed. Reactor power and steam generator water level control systems improved by the model-free adaptive predictive control (MFAPC) theory were developed, and the parameters of the MFAPC controller were optimized by the differential evolution method. The simulation results under single-loop operation show that the improved control systems reduce the overshoot of the reactor power and effectively suppress the water level fluctuation of the steam generator.

Date-Driven Tracking Control via Fuzzy-State Observer for AUV under Uncertain Disturbance and Time-Delay

This paper focuses on developing a data-driven trajectory tracking control approach for autonomous underwater vehicles (AUV) under uncertain external disturbance and time-delay. A novel model-free adaptive predictive control (MFAPC) approach based on a fuzzy state observer (FSO) was designed to achieve high precision. Concretely, the mathematical model of AUV motion was analyzed, and simplified via model decoupling, thus providing the model basis with an explicit physical explanation for the controller. Second, the MFAPC scheme for a multiple-inputs and multiple-outputs (MIMO) discrete time system was derived, that estimates system external disturbance. The controller can online estimate and predictive time-varying parameter pseudo-Jacobian matrix (PJM) to establish equivalent state space data-model for AUV motion system. Third, the Takagi–Sugeno (T–S) fuzzy model based state observer was designed to combine with the MFAPC scheme for the first time, which was used to online decline the state error generated by system uncertain time-delay. In addition, the stability of the proposed control scheme was analyzed. Finally, two trajectory tracking scenarios were designed to verify the effectiveness and robustness of the proposed FMFAPC scheme, and the simulations are implemented using the realistic parameters of T-SEA AUV.

Direct Thrust Force Control of Primary Permanent Magnet Linear Motor Based on Improved Extended State Observer and Model-Free Adaptive Predictive Control

A model-free adaptive predictive control algorithm based on an improved extended state observer (IESO) is proposed to solve the problem that the primary permanent magnet linear motor is susceptible to time-varying parameters and unknown disturbances. Firstly, a model-free adaptive control algorithm based on compact format is designed to achieve high control precision of the system and reduce thrust fluctuation, only through the input/output data of the system. Because the traditional model-free adaptive control is too sensitive to the internal parameters of the controller, a combination of model-free adaptive control and predictive control is further developed. By predicting the data for a future time in advance, the sensitivity to the internal parameters of the controller is reduced and the control performance is further improved. Since the load change and other nonlinear disturbances in practical applications have a great impact on the control effect of the system, an improved extended state observer is further used to compensate for the impact of nonlinear disturbances on the control system. In addition, the stability of the closed-loop system is analyzed. Comparable simulation results clearly demonstrate the good tracking performance and strong robustness of the proposed control.

Distributed Group Coordination of Multiagent Systems in Cloud Computing Systems Using a Model-Free Adaptive Predictive Control Strategy.

This article studies the group coordinated control problem for distributed nonlinear multiagent systems (MASs) with unknown dynamics. Cloud computing systems are employed to divide agents into groups and establish networked distributed multigroup-agent systems (ND-MGASs). To achieve the coordination of all agents and actively compensate for communication network delays, a novel networked model-free adaptive predictive control (NMFAPC) strategy combining networked predictive control theory with model-free adaptive control method is proposed. In the NMFAPC strategy, each nonlinear agent is described as a time-varying data model, which only relies on the system measurement data for adaptive learning. To analyze the system performance, a simultaneous analysis method for stability and consensus of ND-MGASs is presented. Finally, the effectiveness and practicability of the proposed NMFAPC strategy are verified by numerical simulations and experimental examples. The achievement also provides a solution for the coordination of large-scale nonlinear MASs.

Distributed model-free adaptive predictive control for heterogeneous nonlinear multi-agent systems

This paper investigates the data-driven consensus tracking problem for heterogeneous multi-agent systems. All of the heterogeneous dynamics, disturbances and measurement noises are considered for the output tracking consensus of nonlinear multi-agent network under a data-driven scheme. A dynamic linearisation method is introduced to deal with the nonlinear nonaffine structures of the agents and a heterogeneous linear data model for the agent is obtained due to the heterogeneous dynamics of the agent itself. On this basis, a distributed model-free adaptive predictive control (DMFAPC) algorithm is constructed by the use of consensus errors and the robustness analysis is conducted in the presence of disturbances. Further, the results are modified by using measured outputs to replace the system outputs, and thus a measured output-based DMFAPC is presented to deal with the omnipresent measurement noises in practical applications. The two proposed DMFAPC methods are data driven since no mechanistic model information is required wherein. And thus, no unmodelled dynamics affects the consensus performance. Instead, they can improve the control performance by utilising additional predictive information. The two proposed DMFAPC methods are verified using numerical simulations.

Improved model-free adaptive predictive control method for direct data-driven control of a wastewater treatment process with high performance

In this paper, an improved model-free adaptive predictive control (IMFAPC) method with controller parameters sensitivity discrimination and optimization is proposed, to solve the problem that using conventional methods is difficult to effectively control the nitrate concentration and dissolved oxygen concentration of wastewater treatment process (WWTP). First, aiming at the nonstationary dynamic characteristics of WWTP and the limitation of control accuracy of conventional MFAPC method in the nonlinear dynamic system control, a novel improved MFAPC method is proposed considering the effect of the WWTP output tracking error variation on the control of nitrate concentration and dissolved oxygen concentration. Meanwhile, in order to obtain the optimum control performance, the sensitivity discrimination and optimization for the multi-parameter set of the proposed IMFAPC method are performed by combining the Monte Carlo experiment based multi-parameter sensitivity analysis and the hybrid intelligent optimization. Then, the stability of the proposed IMFAPC scheme is analyzed and proved by the BIBO method. Finally, various data experiments are carried out to verify the proposed method. The experimental results demonstrate that the proposed method can control the nitrate nitrogen and dissolved oxygen concentration rapidly and accurately under different operating conditions.

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method.

Kalman Filter-Based Data-Driven Robust Model-Free Adaptive Predictive Control of a Complicated Industrial Process

The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking. First, a compact-form dynamic linearization-based extended MFAPC method for multivariable molten iron quality control is proposed by generalizing the existing single-variable MFAPC method to multivariable systems. Based on it, a Kalman filter-based robust MFAPC is further proposed considering the problems of data loss and measurement noise in quality detection. Specifically, the robust mechanism in the robust MFAPC combines a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss. After that, a Kalman filter is constructed based on a prediction model to filter the measurement noise. The stability of the proposed control method is analyzed, and various data experiments using actual industrial data are performed to verify the effectiveness of the proposed methods. <i>Note to Practitioners</i>&#x2014;The extremely complicated dynamics of blast furnace ironmaking process make the model-based controllers difficult to realize in practice. In this article, a novel robust model-free adaptive predictive control method is proposed for direct data-driven control of multivariate molten iron quality in the ironmaking process. This method directly uses the process input and output data to design the multivariable quality controller online by the compact-form dynamic linearization technology and the internal multilayer prediction mechanism, thus avoids the drawback of model-based controllers in troublesome process modeling. Moreover, the proposed method can effectively avoid the influence of data loss and measurement noise on the controller performance with the designed Kalman filter-based robust mechanism. The superiority and practicability of the proposed method are verified using various experiments against actual industrial data.

Constrained Model Free Adaptive Predictive Perimeter Control and Route Guidance for Multi-Region Urban Traffic Systems

Perimeter control (PC) and route guidance (RG) have become two powerful traffic control methods to address the urban traffic congestion, especially for multi-region urban traffic systems (MRUTS). Accurate traffic system model is necessary in majority of the existing PC and RG methods. If the traffic model is inaccurate or unavailable, the aforementioned PC and RG strategies may not work well or cannot be applied. In this paper, a novel data driven scheme called constrained model free adaptive predictive control (cMFAPC) is provided for PC and RG of MRUTS. Two outstanding advantages of this method are that it can only use the input and output (I/O) data of the controlled MRUTS to design the PC and RG strategies, instead of utilizing the traffic dynamics model, and the merits of model free adaptive control (MFAC) method and model predictive control (MPC) approach are combined in the proposed cMFAPC strategy. The effectiveness of cMFAPC strategy and its superiority over other commonly used PC and RG methods are verified via simulation.

Wind power compound model-free adaptive predictive control based on full wind speed

Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuation of wind power, this paper presents a model-free adaptive predictive controller (MFAPC) for variable pitch system with speed disturbance suppression. First, an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed, whose rolling time is 1s, and the predicted value is served as a feedforward to obtain the early compensation variation of pitch angle. Second, a function of multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed, and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative (PPD). Further, the compound MFAPC framework is synthetically obtained, whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions. Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions, and a detailed comparison is finally presented. The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed, but also meet the stability of wind power and the security of grid-connected at all operating conditions.

Perimeter Control for Two-region Urban Traffic System Based on Model Free Adaptive Predictive Control with Constraints

Abstract Recent studies have shown that macroscopic urban traffic control, especially perimeter control, plays an important role in the field of urban traffic control. In this paper, a novel data driven control method named model free adaptive predictive control with constraints (C-MFAPC) is utilized for perimeter control for two-region urban traffic system. In this strategy, the advantages of model free adaptive control (MFAC) and model predictive control (MPC) are combined. That is, the controller can be designed by merely using the I/O data of the system, while the system output sequence can be predicted without the system model. Moreover, the constraints of perimeter control input and the urban traffic system’s output are both considered. In this framework, macroscopic fundamental diagram (MFD) is utilized to determine the desired vehicle accumulations in each region and generate the output data of the urban traffic system. The effectiveness of the proposed method is tested via simulation, and the result shows that it works better than MPC strategy, which is commonly used for perimeter control.

Data-Driven Model-Free Adaptive Predictive Control for a Class of MIMO Nonlinear Discrete-Time Systems With Stability Analysis

In this study, a model-free adaptive predictive control (MFAPC) method is proposed for a class of unknown nonlinear non-affine multiple-input and multiple-output (MIMO) systems based on a novel dynamic linearization technique and a new time-varying Pseudo-Jacobian matrix (PJM) parameter. The advantages of the proposed method are that it does not need the model information in the control system design, and it can avoid a short-sighted control decision and shows better control performance by integrating the idea of predictive control. The applicability and effectiveness of the proposed control scheme have been verified through rigorous mathematical analysis and extensive simulations.