Model-free Adaptive Control Scheme Research Articles

Disturbance Observer Dynamic Linearization-Based Model-Free Adaptive Control for Discrete-Time Nonlinear Systems.

In this article, a disturbance observer dynamic linearization (DL)-based model-free adaptive control (MFAC) scheme is proposed for discrete-time nonlinear systems with disturbances and uncertainties. The partial-form-dynamic-linearization-based disturbance observer (PDO) is constructed by applying the DL method to an unknown ideal disturbance observer. An adaptive updating algorithm of the observer gain is derived by minimizing a estimation criterion function. Then, the PDO-based MFAC scheme is formed and its bounded stability is rigorously analyzed using the contraction mapping principle. The proposed scheme is a purely data-driven control method, that is, both the PDO and control system are designed by using only the input/output data of underlying system. A numerical simulation and a vehicle turning experiment are given to verify the effectiveness of the proposed scheme.

Event-Triggered Model-Free Adaptive Control for Wheeled Mobile Robot With Time Delay and External Disturbance Based on Discrete-Time Extended State Observer

Abstract In this paper, an improved model-free adaptive control strategy is proposed for the trajectory tracking problem of the wheeled mobile robot (WMR) with time-delay and bounded disturbance. Firstly, the original nonlinear time delay system is transformed into a data model by applying the full-form dynamic linearization method (FFDL). Secondly, the discrete-time extended state observer (DESO) is applied to estimate the unknown residual nonlinear time-varying term. A full-form dynamic linearization model-free adaptive control scheme based on discrete-time extended state observer (DESO-based FFDL MFAC) is proposed. In addition, a full-form dynamic linearization event-triggered model-free adaptive control based on discrete-time extended state observer (DESO-based FFDL ET-MFAC) is established by designing an event-triggering condition to assure Lyapunov stability. The control input signal is updated only if the system indicator meets the provided event-triggering condition; otherwise, the control input remains unchanged which can address limited communication bandwidth effectively. Finally, the effectiveness of the proposed method is verified by simulation.

PID-Like Model Free Adaptive Control With Discrete Extended State Observer and Its Application on an Unmanned Helicopter

Aiming at the problem that how to design a controller without using model information for an unmanned helicopter (UH), a novel data-driven method based on model free adaptive control (MFAC) is proposed in the paper. A new form of dynamic linearization (DL) equation composed of pseudo-partial derivative (PPD) and external disturbance is built in order to reduce the influence of disturbance on PPD. Since there are internal and external unknowns included in the DL equation, a nominal DL equation without external disturbance item is introduced to make the estimation of the two items decoupled. The estimation algorithm of PPD is designed based on the nominal DL equation, and a discrete extended state observer (DESO) is applied specifically into the estimation of external disturbance based on the original DL equation. Besides, with the nominal DL equation, a modified MFAC scheme called PID-like MFAC which considers the historical I/O data is given to make a better performance and a more convenient application compared with the typical MFAC scheme. Further, the stability of the control scheme is proved. Finally, comparative simulations are carried out to verify the effectiveness of the proposed control scheme. An experiment based on UH also verifies its practical performance and the theoretical findings.

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.

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.

Practical issues in data-driven model-free adaptive control for an omnidirectional mobile manipulator

This article focuses on addressing three practical issues encountered when applying a data-driven model-free adaptive control (MFAC) approach to mobile robots. The first practical issue lies in a common assumption in MFAC schemes that the sign of all elements in pseudo-partial derivative (PPD) should be constant, while it cannot be satisfied if omnidirectional mobile manipulators (OMMs) move with platform rotation. To solve this problem, a new coordinate frame is introduced, which is crucial for applying MFAC to any mobile robots with rotation. The second one is that the initial value setting method for estimation of PPD is unclear. Improper settings may easily lead to control system instability. An initial value setting method for estimation of PPD is proposed with explicit physical interpretation. Lastly, applying the typical MFAC scheme directly to OMM fails to converge well to the desired trajectory. To tackle this, a new data-driven MFAC controller is proposed by incorporating a sliding mode control. Finally, experimental tests on an OMM are carried out to verify the effectiveness of the proposed control scheme. To the best of our knowledge, this is the first MFAC scheme that has been experimentally verified on a prototype mobile robot with rotation.

Model-free adaptive robust control method for high-speed trains

Abstract Aiming at the robustness issue in high-speed trains (HSTs) operation control, this article proposes a model-free adaptive control (MFAC) scheme to suppress disturbance. Firstly, the dynamic linearization data model of train system under the action of measurement disturbance is given, and the Kalman filter (KF) based on this model is derived under the minimum variance estimation criterion. Then, according to the KF, an anti-interference MFAC scheme is designed. This scheme only needs the input and output data of the controlled system to realize the MFAC of the train under strong disturbance. Finally, the simulation experiment of CRH380A HSTs is carried out and compared with the traditional MFAC and the MFAC with attenuation factor. The proposed control algorithm can effectively suppress the measurement disturbance, and obtain smaller tracking error and larger signal to noise ratio with better applicability.

Event-Triggered Data-Driven Control for Nonlinear Systems Under Frequency-Duration-Constrained DoS Attacks

This paper addresses the event-triggered model free adaptive control (MFAC) problem for unknown nonlinear systems under denial-of-service (DoS) attacks, where the design and analysis are discussed under the data-driven framework. Firstly, by using the novel pseudo partial derivative, the nonlinear systems are converted into an equivalent data-relationship model. Then, the DoS attacks are described as limited by their frequency and duration, and without any more specific assumptions about the attack structure or strategy. Next, a novel event-triggered MFAC scheme is proposed. By employing the Lyapunov stability theory, the stability performance is analyzed. Furthermore, a compensation algorithm is designed to against the adverse impact brought by the DoS attacks. Finally, simulations including a numerical example and a load frequency control (LFC) example for multi-area power systems are given to demonstrate the validity and applicability of the proposed schemes.

Data-Driven Iterative Learning Predictive Control for Power Converters

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

Vibration control for various structures with time-varying properties via model-free adaptive controller based on virtual controlled object and SPSA

This study presents a simple active vibration controller with a self-tuning mechanism free from a mathematical model of an actual controlled object. First, a virtual controlled object (VCO), which is defined as a single-degree-of-freedom (SDOF) system, is inserted between an actuator model and an actual controlled object. Traditional model-based control theories are applied to a two-degree-of-freedom (2DOF) system composed of the actuator and the VCO instead of a model of the actual controlled object to realize a model-free vibration controller. Then a self-tuning method based on the simultaneous perturbation stochastic approximation (SPSA) is introduced to the VCO-based model-free controller to obtain sufficient damping performances for various controlled objects. The model-free controller design is easily achieved because the traditional model-based control theory can be applied to the 2DOF system composed of the actuator and the VCO. Moreover, the proposed self-tuning mechanism provides sufficient vibration suppression effects for various controlled objects without manual controller tunings. Simulation studies compare the damping performance of the VCO-based model-free adaptive control scheme with that of the conventional approach. The simulations employ five controlled objects with different structures and characteristics, including time-varying properties. The proposed control scheme provides better damping effects than the conventional method for all controlled objects.

Heading tracking of 6WID/4WIS unmanned ground vehicles with variable wheelbase based on model free adaptive control

The six-wheel independent drive and four-wheel independent steering unmanned ground vehicle (6WID/4WIS UGV) with variable wheelbase has unique advantages in environmental adaptability and maneuverability. However, due to its strong nonlinearity and uncertainty, it has the great challenges in control. Based on model free adaptive control (MFAC) method and particle swarm optimization (PSO) algorithm, the heading tracking problem of 6WID/4WIS UGV under the influence of uncertainties is studied in this paper. First, to improve the convergence characteristics, the improved MFAC scheme is proposed, which can effectively overcome the influence of time delay and wheelbase. Then, based on the desired steering angle and yaw moment obtained by MFAC method and the single-track model of 6WID/4WIS UGV, the torque of six electric wheels is distributed by PSO algorithm to achieve the coordinated control of driving, braking and steering. Finally, the feasibility and effectiveness of the proposed control strategy are verified by simulation results.

A Model Free Adaptive Scheme for Integrated Control of Civil Aircraft Trajectory and Attitude

The adaptive trajectory and attitude control is essential for the four-dimensional (4D) trajectory operation of civil aircraft in symmetric thrust flight. In this work, an integrated trajectory and attitude control scheme is proposed based on the =multi-input multi-output (MIMO) model free adaptive control (MFAC) method. First, the full-form dynamic linearization technique is adopted to build the equivalent data model of aircraft. Also, the MIMO MFAC scheme with saturation constraint is designed to achieve an accurate tracking control for a given 4D trajectory and attitude. Besides, the performance limitations of aircraft are taken into consideration, and the MIMO MFAC scheme with hard constraints is designed. In addition, to improve the simulation efficiency, a control scheme with mixed constraints, i.e., saturation and hard constraints, is further proposed. It can be seen from the simulation results that the proposed method can perform an integrated control of the aircraft 4D trajectory and attitude without precise modeling, and the control performance is better than that of the model-based control method in terms of flight altitude and yaw angle control. The integrated data-driven control scheme proposed in this paper provides a theoretical solution for the precise operation of aircraft under 4D trajectory.

Internal Boundary Control of Lane-Free Automated Vehicle Traffic using a Model-Free Adaptive Controller

In lane-free traffic, recently proposed for connected automated vehicles (CAV), incremental changes of the road width lead to corresponding incremental changes of the traffic flow capacity. This property enables the controlled shifting of the internal road boundary separating the two opposite traffic directions, so as to optimize the road infrastructure utilization. Internal boundary control aims at flexible sharing of the total road width and capacity among the two traffic directions of a road in real-time-, in response to the prevailing traffic conditions. A model-free adaptive control scheme is applied to efficiently address this problem. Simulation investigations, involving a realistic highway stretch and challenging demand scenario, demonstrate that the efficiency of the proposed control scheme.

Compensated model-free adaptive tracking control scheme for autonomous underwater vehicles via extended state observer

This paper investigates the problem of precise trajectory tracking control of an autonomous underwater vehicle (AUV) under parameter perturbations and external disturbances. To design a data-driven control scheme with high tracking accuracy and strong robustness, a compensated model-free adaptive control (MFAC) scheme is proposed by combining an extended state observer (ESO). Specifically, a data-driven structure-improved linear ESO (SLESO) is derived to online estimate the model approximation error generated by pseudo Jacobian matrix estimation in the typical MFAC scheme. Another problem of the typical MFAC is that it cannot be directly applied to robotic systems with rotation in the inertial frame. To tackle this issue, a two-loop controller architecture is used to design the proposed SLESO-MFAC scheme. In addition, the structure of the mathematical model of AUVs and force analysis are used in MFAC design for the first time, thus providing a straightforward initial value setting method with explicit physical interpretation and helping to judge if the assumptions in the MFAC scheme can be satisfied. Furthermore, the stability analysis of the designed control system is given. Finally, the robustness and effectiveness of the proposed SLESO-MFAC scheme are substantiated via simulations and comparisons using a realistic dynamic model of the Falcon AUV.

Data-Driven Model-Free Adaptive Current Control of a Wound Rotor Synchronous Machine Drive System

The lack of precise mathematical model of nonlinear systems, such as electric machines, brings certain limits to design a powerful controller for such systems. This article presents a model-free-adaptive-control (MFAC) scheme as a good alternative to the commonly used model-based-control (MBC) methods in order to improve the controller design limitations due to the need for system model information. In this regard, an MFAC is developed in this article for current control of a wound rotor synchronous machine (WRSM) drive system and compared with a proportional-integral (PI) controller as a well-known model-based controller. Since the controller performance can be affected by unexpected phenomena, such as model errors, parameter variations, and changes in operating point, beforehand, the robustness of developed MFAC is first tested under the mentioned conditions in the simulation area. Then, through experimental validation on a specifically designed test bench, a satisfactory performance was obtained for the MFAC scheme.

Perimeter Control of Urban Traffic Networks Based on Model-Free Adaptive Control

In urban traffic networks, abundant traffic data is generated everyday. Therefore, data-driven control approaches for urban traffic control without utilizing mathematical models might be a promising research direction. In this paper, a hierarchical perimeter control strategy for urban traffic networks based on model-free adaptive control (MFAC) scheme is proposed. At the outer level, the signal settings in the periphery of the congested region are optimized to meter the number of vehicles entering the congested region; while at the inner level, traffic flow distribution within the congested region is adjusted to improve the traffic efficiency. MFAC scheme is applied in both outer level and inner level to derive the detailed signal settings in the congested region and in the periphery of the congested region. Joint simulations using VISSIM and MATLAB verify the feasibility and effectiveness of the proposed method.

Model free adaptive control for a class of nonlinear systems with fading measurements

The problem of model free adaptive control (MFAC) for a class of non-affine nonlinear systems with fading measurements is investigated in this paper. The signal fading is considered to exist in output channels and this phenomenon is modeled as an independent stochastic process with a known probability distribution. The convergence of the MFAC algorithm applying the faded measurements is proved and the effects of fading phenomenon on system performance are analyzed. It is shown that the ultimate tracking error depends on the upper bound on the desired output and fading coefficient together. Then to suppress the effects of fading phenomenon, a compensated MFAC scheme with decreasing gains is proposed. The theoretical analysis shows that the improved algorithm is effective in restraining the adverse effects of fading phenomenon. Finally, a numerical example and a quadrotor attitude control example are given to illustrate the effectiveness of the proposed algorithms.

Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton

A new model-free adaptive robust control method has been proposed for the robotic exoskeleton, and the proposed control scheme depends only on the input and output data, which is different from model-based control algorithms that require exact dynamic model knowledge of the robotic exoskeleton. The dependence of the control algorithm on the prior knowledge of the robotic exoskeleton dynamics model is reduced, and the influence of the system uncertainties are compensated by using the model-free adaptive sliding mode controller based on data-driven methodology and neural network estimator, which improves the robustness of the system. Finally, real-time experimental results show that the control scheme proposed in this paper achieves better control performances with good robustness with respect to system uncertainties and external wind disturbances compared with the model-free adaptive control scheme and model-free sliding mode adaptive control scheme.

On Model-Free Adaptive Control and Its Stability Analysis

In this paper, the main issues of model-based control methods are first reviewed, followed by the motivations and the state of the art of the model-free adaptive control (MFAC). MFAC is a novel data-driven control method for a class of unknown nonaffine nonlinear discrete-time systems since neither explicit physical model nor Lyapunov stability theory or key technical lemma is used in the controller design and theoretical analysis except only for the input/output (I/O) measurement data. The basis of MFAC is the dynamic linearization data modeling method at each operating point of the closed-loop system. The established dynamic linearization data model is a virtual equivalent data relationship in the I/O sense to the original nonlinear plant by means of a novel concept called pseudo-partial derivative (PPD) or pseudo-gradient (PG) vector. Based on this virtual equivalent dynamic linearization data model and the time-varying PPD or PG estimation algorithm designed merely using the I/O measurements of a controlled plant, the MFAC system is constructed. The main contribution of this paper is that the theoretical analysis of the bounded-input bounded-output stability, the monotonic convergence of the tracking error dynamics, and the internal stability of the full form dynamic linearization based MFAC scheme are rigorously presented by the contraction mapping principle; the well known PID control and the traditional adaptive control for linear time-invariant systems are explicitly shown as the special cases of this MFAC. The simulation results verify the effectiveness of the proposed approach.

Load frequency regulation for multi‐area power system using integral reinforcement learning

Active load variations in uncertain dynamical power system environments affect the energy exchange and efficiency in multi-area power systems, which could compromise the stability of power grids. Hence, model-free load frequency control mechanisms are needed in order to sustain proper performances under such conditions. An online model-free adaptive control scheme based on integral reinforcement learning is proposed to regulate load frequency deviations in multi-area power systems. This scheme takes into account the generation rate constraints of the power generation units and the optimal control decisions do not employ any knowledge about the dynamical model of the power system. This approach reformulates Bellman equation and approximates the associated solving value functions and model-free control strategies using neural networks. The adaption mechanism uses value iteration processes to evaluate the underlying modified-Bellman equation and model-free control strategy in real time. The performance of the adaptive learning scheme is compared with other control methodologies using challenging validation scenarios.