Model-free Adaptive Control Research Articles

Backstepping model‐free adaptive control for a class of second‐order nonlinear systems

SummaryThis paper proposes a backstepping model‐free adaptive control (BS‐MFAC) algorithm for the trajectory tracking control problem of a class of second‐order nonlinear systems. Firstly, the model‐free adaptive control (MFAC) is employed to track the virtual desired velocity. Then, based on the measured state data at the current moment, a virtual desired velocity is designed, combining MFAC with backstepping control in a clever manner. By designing a discrete form of Lyapunov function, it is proven that the system velocity error under this algorithm converges asymptotically and stably. Furthermore, a nonhomogeneous difference equation solution related to the system position is obtained through calculations. The analysis of the solution indicates that the system position output error will slide into a neighborhood around zero. This algorithm possesses advantages such as simple structure, model independence, and strong robustness. Additionally, a planar two‐link space robot arm model is constructed using the Mbdyn platform, a multibody dynamics simulation software. Trajectory tracking control of joint angles is achieved under the proposed control algorithm in this paper. The simulation outcomes unequivocally establish the efficacy of the algorithm, which adeptly exploits the pseudo partial derivatives (PPD) estimation for second‐order nonlinear systems in MFAC. The algorithm yields expedited and superior trajectory tracking control of said systems.

Differential flatness properties and multivariable adaptive fuzzy control of hormonal system dynamics

Computer-based control for hormonal (ovarian) system dynamics and for administering in a computationally optimized manner the associated pharmaceutical treatment has been little investigated up to now. Although there has been prior work on the modeling of the ovarian system dynamics and its stability features, there have been hardly any results for applying to the ovarian system exogenous computer-based control. This is due to the complexity, nonlinearity and the high dimensionality of this system. Actually, the ovarian system exhibits nonlinear dynamics which is modeled by a set of thirteen coupled nonlinear differential equations, while receiving only two control inputs. Besides, the fact that the parameters of the model cannot be precisely identified and the fact that any mathematical model for such a biosystem is likely to be subjected to uncertainties and external perturbations, make the application of model-based control schemes for this system be questionable. To overcome this, the paper proposes model-free adaptive fuzzy control based on differential flatness theory for the complex dynamics of the ovarian system. It is proven that the dynamic model of the ovarian system, having as state variables the LH and the FSH hormones and their derivatives, is a differentially flat one. This means that all its state variables and its control inputs can be written as differential functions of the flat output and its derivatives. By exploiting differential flatness properties the system's dynamic model is written in the multivariable linear canonical (Brunovsky) form, for which the design of a state feedback controller becomes possible. After this transformation, the new control inputs of the system contain unknown nonlinear parts, which are identified with the use of neurofuzzy approximators. Lyapunov stability analysis shows that H-infinity tracking performance is achieved for the feedback control loop and this assures improved robustness to the aforementioned model uncertainty as well as to external perturbations.

An Improved Data-Driven Integral Sliding-Mode Control and Its Automation Application

Circulating fluidized bed (CFB) boilers are widely used in industrial production due to their high combustion efficiency, low pollutant emissions and wide load-adjustment range. However, the water-level-control system of a CFB boiler exhibits time-varying behavior and nonlinearity, which affect the control performance of the industrial system. This paper proposes a novel data-driven adaptive integral sliding-mode control (ISMC) method for the CFB control system with external disturbances. Firstly, the scheme designs a discrete ISMC law based on the full-format dynamic linearization (FFDL) data model, which is equivalent to a nonlinear system. Furthermore, a new reaching law is proposed to quickly drive the system state onto the sliding-mode surface. The improved ISMC control scheme only utilizes the input–output data during the design process and does not require model information. After theoretically verifying the stability of the method proposed in this paper, it is further applied in MIMO systems. Finally, the control and practical effects of this method are evaluated by using the DHX25-1.25 CFB boiler installed in the special-equipment testing center. The experimental results show that, compared with the traditional sliding-mode control (SMC) and model-free adaptive-control (MFAC) methods, the improved control method can quickly track the given signal and exhibit resistance to noise interference. Furthermore, it can rapidly respond to changes in the working conditions of the CFB system.

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.

Net Power Optimization Based on Extremum Search and Model-Free Adaptive Control of PEMFC Power Generation System for High Altitude

The operating characteristics of proton exchange membrane fuel cell (PEMFC) generation systems will change in high altitude. In order to improve the system output performance, this paper proposes a net power optimization (NPO) based on extremum search and model-free adaptive control (MFAC) of PEMFC power generation system. A PEMFC power generation system model for high-altitude environment is established, the off-line analysis shows that with the increase of altitude, the performance of the air compressor decreases, the optimal oxygen excess ratio (OER) of the system decreases, and surge phenomenon is more likely to occur. Considering the non-minimum phase characteristic of the net power to OER, the OER is optimized online by extremum search strategy and the optimization step and cycle ensure the convergence of the strategy. Combined with a model-free adaptive control strategy based on compact form dynamic linearization (CFDL), the optimal OER is tracked real time, the data model identified online ensures the adaptability of the system. Finally, a hardware-in-the-loop platform is built and comparative experiment is carried out. The results show that: compared with the off-line optimization method (OOM), (1) NPO can improve the net power of the system at different altitudes through the adaptive adjustment of OER and no off-line data is required inappropriate static operating points are avoided. (2) in high altitude areas, NPO has higher operational stability and can effectively avoid surge phenomenon.

Fuzzy Supervised Learning-Based Model-Free Adaptive Fault-Tolerant Spacecraft Attitude Control With Deferred Asymmetric Constraints

Fuzzy Supervised Learning-Based Model-Free Adaptive Fault-Tolerant Spacecraft Attitude Control With Deferred Asymmetric Constraints

Model-free adaptive control for wind power system based on grey prediction under unbalanced grid conditions

Model-free adaptive control for wind power system based on grey prediction under unbalanced grid conditions

A Novel Model-Free Adaptive Proportional–Integral–Derivative Control Method for Speed-Tracking Systems of Electric Balanced Forklifts

Similar to many complex systems, the operation process of electric balanced forklifts has characteristics such as time-varying model parameters and nonlinearity. Establishing an accurate mathematical model becomes challenging, making it difficult to apply model-based control methods in engineering practice. Aiming at the longitudinal control system of electric forklifts containing external disturbances, this paper proposes an improved full-format dynamic linearization model-free adaptive PID control (iFFDL-MFA-PID) method. Firstly, the full-format dynamic linearization (FFDL) method is employed to transform the operating system of the electric balanced forklift into a virtual equivalent linear data model. Secondly, the nonlinear residual term and pseudo-gradient (PG) of the data model are estimated using the difference estimation algorithm and the optimal criterion function, respectively. Furthermore, in order to enhance the robustness of the system, the idea of intelligent PID (iPID) is introduced and the principle of equivalent feedback is utilized to derive the iFFDL-MFA-PID control scheme. The design process of this scheme only requires the use of the input and output data of the system, without relying on the mathematical model of the system. Finally, the iFFDL-MFA-PID method proposed in this paper is simulated and tested with the EFG-BC/320 counterbalanced forklift equipped in the Special Equipment Testing Center and compared with the model-free adaptive control method (FFDL-MFAC) and the PID control method. Simulation results show that the speed-tracking error of the electric forklift truck under the action of the iFFDL-MFA-PID algorithm is maintained within ±0.132 m/s throughout the process, achieving higher tracking accuracy and better robustness compared to the MFAC and PID methods.

Pseudo-partial-derivative information-driven adaptive fault-tolerant tracking control for discrete-time systems

The fault-tolerant tracking control problem is studied for the discrete-time systems with actuator faults. To lessen adverse impacts of actuator fault, a PPD information-driven fault estimation algorithm is established to adaptively estimate actuator fault information online, which avoids the additional construction and training process of neural network. With the aid of the adaptive fault compensation, a model-free adaptive fault-tolerant tracking control algorithm is constructed to ensure that the expected output reference trajectory can be tracked by system output. Moreover, only input and output data are employed throughout the design process, system dynamics are not demanded. Ultimately, the availability of developed strategy is proved through a simulation.

Model-free adaptive chaos control for the boost converter

In order to solve the dynamic analysis and chaotic motion control problems of the current-mode-controlled Boost converter system, a state equation model in the continuous current mode is established. The evolutionary path of the periodic motion of the system through the doubling bifurcation to the chaotic motion is analyzed by the bifurcation diagram, Poincaré sections and phase portraits. Then, according to the fundamental principle of the resonant parametric perturbation (RPP) method, a controller is designed based on the model-free adaptive control (MFAC) method to output the perturbation amplitude, and the disturbance quantity is calculated according to the perturbation amplitude. By adding a small amplitude disturbance to the controllable parameter of the controlled system, the chaotic motion of the system is guided to expected periodic orbit. The advantage of this method is that no precise mathematical model (PMM) of the system is required. At the same time, the design of the controller is simple and easy to implement.

Event-triggered model-free adaptive control for nonlinear systems using intuitionistic fuzzy neural network: simulation and experimental validation

This article presents model-free adaptive control based on an intuitionistic fuzzy neural network for nonlinear systems with event-triggered output. Essentially, model-free adaptive control (MFAC) is constructed by establishing an online approximate model of the controlled system using the pseudo-partial derivative (PPD) form. By the proposed scheme, first, an intuitionistic fuzzy neural network (IFNN) is developed as an estimator for time-varying PPD in both compact-form dynamic linearization (CFDL) and partial-form dynamic linearization (PFDL) for the MFAC technique. Second, two periodic event-triggered output methods are integrated with the proposed IFNN-based MFAC in both forms to save communication resources and reduce the computation burden and energy consumption. Based on the Lyapunov theory and BIBO stability approach, necessary conditions are established to guarantee the convergence of the adaptive law of the IFNN controller and the boundary of the tracking error of the closed loop system. Third, regarding the feasibility and the effectiveness of the developed control method, two simulation examples including the continuous stirred-tank reactor (CSTR) system and the heat exchanger system are given. Finally, the practical validation of the proposed data-driven control method is conducted via the speed control of a DC motor.

Model-free adaptive dynamic event-triggered robust control for unknown nonlinear systems using iterative neural dynamic programming

This paper proposes an adaptive dynamic event-triggered (ADET) robust control method for unknown nonlinear systems using iterative neural dynamic programming (INDP). Firstly, the ADET robust control problem is transformed into an ADET optimal control problem by introducing an infinite domain integral function. Then, dynamic variables and adaptive thresholds are designed for discrete-time systems with auxiliary variables. The proposed ADET method reduces computation and transmission costs compared to existing static and dynamic event triggering methods. INDP is utilized to learn the optimal solution of the ADET Hamilton-Jacobi-Bellman equation within the heuristic dynamic programming (HDP) framework, providing system stability and algorithm convergence. The INDP algorithm employs model, action, and critic neural networks and includes a method to directly minimize the iterative cost function in the back-propagation process. The neural network implementation of the INDP algorithm is detailed. Simulation results demonstrate that the proposed ADET method based on INDP achieves faster convergence with fewer transmitted data and control updates.

Data Driven Control and slip estimation for Agricultural Tracked Vehicles

This paper focusses on the path following problem for autonomous Unmanned Ground Vehicles developed for precision agriculture. Such vehicles typically face rough terrain and narrow navigating spaces, so reliability of odometry is largely reduced. In this context, slip estimation/accommodation becomes crucial for localization and navigation of vehicles. In this work, a control architecture is presented coupling MIMO Data-Driven Control and Sliding Mode Control, with the inclusion of a fault estimation mechanism aimed at mitigating the actuators’ loss of effectiveness associated to slipping. The supporting theoretical development makes use of previous results available in the Model Free Adaptive Control framework, which have been extended in order to deal with the path following problem for the vehicle. On the basis of extensive simulations, the proposed control policy is shown able to strengthen the recovery capability and robustness inherently owned by the original adaptive mechanism and control laws.

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.

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.