Model-free Adaptive Control Research Articles

An Online Model-Following Projection Mechanism Using Reinforcement Learning

In this paper, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-horizon of model-following error measurements. In addition, the control strategy is designed by using a projection mechanism that employs Lagrange dynamics. It allows for real-time tuning of derived actor-critic structures to find the optimal model-following strategy and sustain optimized adaptation performance. Finally, the efficacy of the proposed framework is emphasized through a comparison with sliding mode and high-order model-free adaptive control approaches.

Finite-Time Model-Free Adaptive Control for Discrete-Time Nonlinear Systems

This paper addresses the fast trajectory tracking problem for discrete-time nonlinear systems, and whereby a finite-time model-free adaptive control (FMFAC) approach is innovatively devised. Here, the nonlinear systems’ dynamics are completely unknown and the widespread disturbances are considered. The nonparametric dynamic linearization technique and the adaptive extended disturbance observer are firstly deployed to achieve real-time linearization of the discrete-time nonlinear system, and an efficient finite-time performance index function is thereafter devised, and thereby contributing to a novel FMFAC control scheme. Theoretical analysis indicates the finite-time model-free tracking control within a stable region can be guaranteed, and the boundedness of the control input signal can be ensured rigorously, even though under the effect of various disturbances. The key points of the proposed FMFAC approach are model-free and possessing the fast regulation ability, and the corresponding effectiveness and superiority are validated via simulation studies.

Optimal model-free adaptive control based on reinforcement Q-Learning for solar thermal collector fields

Optimal model-free adaptive control based on reinforcement Q-Learning for solar thermal collector fields

Model‐free adaptive control for ultracapacitor based three‐phase interleaved bidirectional DC–DC converter

AbstractThe lack of an accurate mathematical model of non‐linear systems, such as bidirectional DC–DC converters, brings certain limits to design a formidable controller for such systems. This article proposes a model‐free adaptive control (MFAC) approach for an ultracapacitor based three‐phase interleaved bidirectional DC–DC converter (BDC) to ameliorate the controller design limitations due to the demand for system model information. This article designs a pseudo gradient (PG) estimation algorithm based on the input and output (I/O) data of a three‐phase interleaved BDC and designs a control input estimation algorithm through the PG estimation value, then, the design of a model‐free adaptive controller is completed. Moreover, the Black‐box technique is applied to the system, which does not require a precise mathematical model resulting in a lower computational burden, faster dynamics, and easier implementation. Furthermore, the feasibility and effectiveness of the proposed MFAC scheme are experimentally tested and verified on an energy storage system microgrid platform.

Adaptive energy management strategy of plug-in hybrid electric bus

In this study, the online application of a new energy management strategy (EMS) is proposed for a power-split plug-in hybrid electric bus (PHEB) to improve its system's fuel economy and reduce power battery degradation. The new EMS employs Pontryagin's minimum principle (PMP) and the power battery state of charge (SOC) feedback method to adjust the covariates online. A model-free adaptive control (MFAC) EMS was designed and constructed according to the compact form dynamic linearization model to further obtain accurate SOC feedback; the time–domain cumulative as well as covariate upper and lower limit constraints were used to obtain a real-time MFAC-Ⅲ strategy for online applications. The average reduction in battery capacity loss on all ten routes was 8.19 %, resulting from the correlation between the MFAC-III EMS and MFAC EMS; evaluations conducted on both strategies indicated that the MFAC-III outperforms the MFAC strategy in hardware-in-the-loop.

Robust adaptive path following control of autonomous underwater vehicle with uncertainties and communication bandwidth limitation

This article investigates the path-following control problem for autonomous underwater vehicles (AUVs) subject to external disturbances and unknown model parameters. An event-triggered model-free adaptive control (ETMFAC) method is designed with a practical experimental platform. Firstly, a line-of-sight (LOS) guidance law is employed to obtain the desired heading angle for the AUV. Secondly, an improved model-free adaptive control method is used, in which an adaptive law is introduced to compensate for the effects caused by external disturbances. Subsequently, an event-triggering mechanism that can reduce the communication and calculation burden of the control system is proposed. As for the state mutation caused by variable disturbances, an adaptive triggering threshold is designed based on the state vector of the AUV to maintain satisfactory following performance. The stability of the control system is proved by theoretical analysis. Finally, both numerical simulations and practical experiments are conducted to evaluate the efficacy and prove the superiority of the proposed method.

Design model-free adaptive PID controller based on lazy learning algorithm

Abstract The nonlinear system is difficult to achieve the desired effect by using traditional proportional integral derivative (PID) or linear controller. First, this study presents an improved lazy learning algorithm based on k-vector nearest neighbors, which not only considers the matching of input and output data, but also considers the consistency of the model. Based on the optimization index of an additional penalty function, the optimal solution of the lazy learning is obtained by the iterative least-square method. Second, based on the improved lazy learning, an adaptive PID control algorithm is proposed. Finally, the control effect under the condition of complete data and incomplete data is compared by simulation experiment.

Research on motion control of the semi-submersible platform under the mooring line failure

The mooring line failure can cause significant drift, continuous mooring line failures, riser collision and production shutdown, but mooring line repairs and replacements are time consuming, and there is a lack of associated short-term emergency measures. To solve the above problems, a short-term recovery strategy is proposed in this paper to suppress low-frequency motions. The strategy contains a control process consisting of five controllers and a thrust distribution optimization algorithm and a prediction process consisting of the LSTM and Dense neural network. The simulation results show that all five controllers can suppress the low-frequency motion after mooring line failure, and the control efficiency of the controller can be improved and the control force and power consumption can be reduced with the help of the predictive correction algorithm. The combination of the predictor-corrector algorithm and the model-free adaptive controller provides the best comprehensive performance. The direction of the control force and the average propulsion angle of the thrusters are independent of the wave direction and are related to the direction of the failed mooring line.

Distributed Event-Triggered Iterative Learning Control for Multiple High-Speed Trains With Switching Topologies: A Data-Driven Approach

This paper studies the distributed data-driven event-triggered model free adaptive iterative learning control (ETMFAILC) of multiple high-speed trains (MHSTs) under iteration-varying topologies, which breaks away from the dependence on the train dynamics. Firstly, the nonlinear MHSTs with unknown dynamics are converted into a linear model. Then, combining the proposed event-based triggering condition and the linear model, the ETMFAILC scheme under the fixed topology is designed. Next, theoretical analysis proves the bounded input bounded output (BIBO) stability of MHSTs. Finally, the study is extended to the switching topologies and the validity of the ETMFAILC is verified by a numerical example.

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays.

To solve the time-delay problem and actuator saturation problem of nonlinear plants in industrial processes, an improved compact-form antisaturation model-free adaptive control (ICF-AS-MFAC) method is proposed in this work. The ICF-AS-MFAC scheme is based on the concept of the pseudo partial derivative (PPD) and adopts equivalent dynamic linearization technology. Then, a tracking differentiator is used to predict the future output of a time-delay system to effectively control the system. Additionally, the concept of the saturation parameter is proposed, and the ICF-AS-MFAC controller is designed to ensure that the control system will not exhibit actuator saturation. The proposed algorithm is more flexible, has faster output responses for time-delay systems, and solves the problem of actuator saturation. The convergence and stability of the proposed method are rigorously proven mathematically. The effectiveness of the proposed method is verified by numerical simulations, and the applicability of the proposed method is verified by a series of experimental results based on double tanks.

Model-free adaptive control of average coolant temperature in a novel marine small lead–bismuth-cooled reactor

Marine lead–bismuth-cooled nuclear reactors are sensitive to the marine environment, facing motion-induced changes like heeling and rolling. These changes induce heavy oscillatory processes, and general controllers cannot totally meet the power output requirements from reactors. To address these challenges, this paper proposes a model-free adaptive control (MFAC) method. Initially, an initial model is established based on mechanism analysis, incorporating neutron kinetics, fuel thermal dynamics, and core thermal dynamics of the reactor while considering a dynamic marine environment. Subsequently, a model-free adaptive controller with online identification and adaptive capability is designed, employing a projection algorithm to continuously identify initial model parameters to match the harsh marine condition and correct the control law based on the input and output data. Finally, through simulation experiments, the effectiveness of the MFAC controller is demonstrated, showcasing superior adaptive capabilities and robustness, enabling rapid and precise tracking of the desired setpoint for average coolant temperature, even under fluctuating marine conditions.

Model-Free Adaptive Sliding Mode Control Method for Unmanned Surface Vehicle Course Control

A novel data-driven-based adaptive sliding-mode control scheme is proposed for unmanned surface vehicle course control in the presence of disturbances. The proposed method utilizes the model-free adaptive control (MFAC) theory. On account of the unknown dynamics of the USV course system, the control scheme is only established by online input and output information of the system. Based on a model-free adaptive control scheme, the system disturbance estimation technique is applied to compensate for the disturbances in the established compact form dynamic linearization data model. The controller is designed and combined with the sliding mode method, and a second-order switching surface with a fast terminal sliding function is employed to achieve finite-time convergence. Furthermore, an analysis of the stability of the control system is provided. Finally, MATLAB simulations are implemented to verify the validity and robustness of the proposed control scheme by comparing it with PID and typical model-free adaptive sliding mode control.

Model-free adaptive trajectory tracking control of robotic manipulators with practical prescribed-time performance

Model-free adaptive trajectory tracking control of robotic manipulators with practical prescribed-time performance

A robust inventory management in dynamic supply chains using an adaptive model-free control

As a complex and nonlinear systems characterized by inherent delays, supply chains call for more robust and efficient strategies for their dynamic management. Given the inherent modeling challenges that often fail to capture their dynamic behavior, this paper examines the application of model-free control, as introduced by Fliess and Join, to address supply chain management (SCM) issues. The proposed control framework integrates the principles of model-free control, further enriched by recent advancements in time series analysis for delay compensation and customer demand forecasting. The primary objective, in addition to ensuring effective supply chain control, is to mitigate the intriguing bullwhip effect, where the system model is assumed unknown, and the delay is constant yet unknown. To substantiate this approach’s effectiveness, we present several convincing computer simulations using real-world examples and compare the results with the internal model control strategy.

Model-free adaptive backstepping control for a class of uncertain nonlinear systems

Most nonlinear dynamic systems are characterized by uncertainties in models and parameters. Deterministic models cannot account for these uncertainties; therefore, model-based control using such models cannot provide the required performance. It is crucial to establish a practical concept of model-free control as a powerful alternative to model-based control. This paper develops a model-free adaptive backstepping control (MFABC) based on type 2 fuzzy Petri nets for a class of uncertain nonlinear systems. To provide valuable robustness to the MFABC structure, we have exploited the universal approximation property of type 2 fuzzy Petri nets to approximate the different nonlinear functions of the uncertain nonlinear system. The parameter adaptive laws are designed by the Lyapunov function; the stability and error convergence can be guaranteed. The simulation tests show that the proposed MFABC can provide good performance and high accuracy compared with the backstepping control. Moreover, the stability of this control scheme is affirmed.

Model-Free Adaptive Positioning Control of the Bidirectional Stick-Slip Piezoelectric Actuator with Coupled Asymmetric Flexure-Hinge Mechanisms.

A model-free adaptive positioning control strategy for piezoelectric stick-slip actuators (PSSAs) with uncertain disturbance is proposed. The designed controller consists of a data-driven self-learning feedforward controller and a model-free adaptive feedback controller with a radial basis function neural network (RBFNN)-based observer. Unlike the traditional model-based control methods, the model-free adaptive control (MFAC) strategy avoids the complicated modeling process. First, the nonlinear system of the PSSA is dynamically linearized into a data model. Then, the model-free adaptive feedback controller based on a data model is designed to avoid the complicated modeling process and enhance the robustness of the control system. Simultaneously, the data-driven self-learning feedforward controller is improved to realize the high-precision control performance. Additionally, the convergence of the tracking error and the boundedness of the control output signal are proved. Finally, the experimentally obtained results illustrate the advantages and effectiveness of the developed control methodology on the bidirectional stick-slip piezoelectric actuator with coupled asymmetric flexure-hinge mechanisms. The positioning error through the proposed controller reaches 30 nm under the low-frequency condition and 200 nm under the high-frequency condition when the target position is set to 100 μm. In addition, the target position can be accurately tracked in less than 0.5 s in the presence of a 100 Hz frequency.

Nonsmooth funnel function-based model-free adaptive sliding-mode control for discrete-time nonlinear systems

This paper proposes a nonsmooth funnel function (NFF)-based model-free adaptive sliding-mode control strategy for the discrete-time nonlinear systems with unknown dynamics and asymmetric constraints. The dynamic linearization model of the nonlinear systems with unknown dynamics is obtained online only using the input/output data, which is combining with the designed NFF-based discrete-time sliding-mode surface to guarantee asymmetric constraints of the tracking error. In the proposed NFF, the coordinate transformation for asymmetric constraints simplifies the controller design, and the nonsmooth parameter can improve the transient and steady-state performances of the tracking error. The control consumption is further reduced by a proposed time-varying weighting factor-based cost function in the controller design. Furthermore, parameter selections of performance boundaries and the sliding-mode surface are given, and the performance margin (the minimum distance from the tracking error to the preset boundaries) is proved theoretically. Simulation and experimental results verify the effectiveness of the proposed control strategy.

Model-Free Adaptive Resilient Control for Nonlinear CPSs With Aperiodic Jamming Attacks.

The problem of the model-free adaptive resilient control (MFARC) for nonlinear cyber-physical systems (CPSs) suffered from aperiodic jamming attacks is investigated in this article. First, the MFARC framework subject to aperiodic jamming attacks is established, and an intermediate variable method is introduced to avoid using the unavailable time-varying parameter and further eliminate an extra assumption on the sign limit of it. Then, a MFARC scheme is devised to track the desired output, where the problem of the tracking control can be transformed into solving a feasibility problem, and the controller parameters can be obtained with the aid of the linear matrix inequality technique. What is more, a novel attack compensation mechanism is developed in the MFARC scheme to mitigate the impact of aperiodic jamming attacks. In the last, an example is provided to verify the effectiveness of the devised MFARC scheme.

Event-Triggered Cooperative Model-Free Adaptive Iterative Learning Control for Multiple Subway Trains With Actuator Faults.

This article investigates the issue of speed tracking and dynamic adjustment of headway for the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the repeatable nonlinear subway train system is transformed into an iteration-related full-form dynamic linearization (IFFDL) data model. Then, the event-triggered cooperative model-free adaptive iterative learning control (ET-CMFAILC) scheme based on the IFFDL data model for MSTs is designed. The control scheme includes the following four parts: 1) the cooperative control algorithm is derived by the cost function to realize cooperation of MSTs; 2) the radial basis function neural network (RBFNN) algorithm along the iteration axis is constructed to compensate the effects of iteration-time-varying actuator faults; 3) the projection algorithm is employed to estimate unknown complex nonlinear terms; and 4) the asynchronous event-triggered mechanism operated along the time domain and iteration domain is applied to lessen the communication and computational burden. Theoretical analysis and simulation results show that the effectiveness of the proposed ET-CMFAILC scheme, which can ensure that the speed tracking errors of MSTs are bounded and the distances of adjacent subway trains are stabilized in the safe range.

Improved Model-Free Adaptive Control for MIMO Nonlinear Systems With Event-Triggered Transmission Scheme and Quantization.

In this article, an improved model-free adaptive control (iMFAC) is proposed for discrete-time multi-input multioutput (MIMO) nonlinear systems with an event-triggered transmission scheme and quantization (ETQ). First, an event-triggered scheme is designed, and the structure of the uniform quantizer with an encoding-decoding mechanism is given. With the concept of partial form dynamic linearization based on event-triggered and quantization (PFDL-ETQ), a linearized data model of the MIMO nonlinear system is constructed. Then, an improved model-free adaptive controller with the ETQ process is designed. By this design, the update of the pseudo partitioned Jacobean matrix (PPJM) estimates and control inputs occurs only when the trigger conditions are met, which reduces the network transmission burden and saves the computing resources. Theoretical analysis shows that the proposed iMFAC with the ETQ process can achieve a bounded convergence of tracking error. Finally, a numerical simulation and a biaxial gantry motor contour tracking control system simulation are given to illustrate the feasibility of the proposed iMFAC method with the ETQ process.