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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. [ABSTRACT FROM AUTHOR]

This paper proposes a model-free super-twisting terminal sliding mode controller (MF-STTSMC) for the n-DOF upper-limb rehabilitation exoskeleton in the presence of external disturbance and backlash hysteresis. This ultra-local model-based control scheme consists of a sliding mode disturbance observer (SMDO) and a super-twisting non-singular fast terminal sliding mode controller (ST-NFTSMC). The ultra-local model is used to approximate the origin complex exoskeleton system in a short sliding time window, which avoids the requirement for accurate modelling and decreases the difficulty of controller design. To compensate for the lumped uncertainties, the SMDO is constructed to achieve finite-time observation and zero estimation error. Besides, to realise finite-time convergence of sliding surface and tracking error with less chattering, the ST-NFTSMC is proposed by employing a super-twisting algorithm. Furthermore, the stability of a closed-loop system with MF-STTSMC is ensured by using the Lyapunov theorem. Finally, to validate the proposed strategy, co-simulations of 7-DOF iReHave upper-limb exoskeleton are realised by using SolidWorks, SimMechanics, and Matlab/Simulink. Compared to time-delay estimation-based intelligent proportional-derivative control (TDE-iPD), α-variable adaptive model-free controller (α-AMFC), and inclusive and enhanced time delay controller (IETDC), the higher performances of the proposed MF-STTSMC are demonstrated. [ABSTRACT FROM AUTHOR]

Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL's performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the traditional time-delay estimation-based control ( TDEC/ TDC) method is difficult to effectively balance the control accuracy and the input chattering phenomenon when controlling industrial robot arms. An improved TDEC strategy based on adaptive estimation of model uncertainty was proposed. First, the time delay estimation(TDE)controller was constructed by combining the dynamic model of a multi-degree-of-freedom manipulator. Then, an adaptive estimator for the uncertainty of the manipulator dynamic model was proposed by using the TDE and the corresponding gradient value of the disturbance term. TDEC and sliding mode control was used as auxiliary control strategies and combined with the proposed adaptive estimator to form a complete improved TDEC framework for the manipulator. Further, the stability analysis of the improved TDEC control strategy was carried out based on Lyapunov method. Finally, the superiority of the improved TDEC control method was verified by simulation analysis and experiment. The results show that compared with the traditional TDEC method, the control accuracy of the improved TDEC control strategy can be improved by 62. 29% and 45. 38% respectively under the loading and unloading conditions. Comparing with the existing adaptive TDEC method, the control accuracy of the improved TDEC strategy can be improved by 23. 31% and 36. 40% respectively under the loading and unloading conditions. At the same time, by measuring and calculating the square value integral of the control input energy consumption, it is shown that the improved TDEC control method can effectively suppress the input chattering phenomenon on the basis of retaining the advantages of conventional TDEC, and can be effectively combined with the existing adaptive TDEC method for improving the control effect, showing its flexibility. [ABSTRACT FROM AUTHOR]

Vector control of an induction machine (IM) is typically performed by using cascade control structures with conventional linear proportional–integral (PI) controllers, the inner loop being designed for current control and the outer loop for rotor flux and speed control. In this paper, starting with the dq model of the IM, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling of the two dq currents, predictive control algorithms are employed to independently control the currents, considering the constraints imposed by the electrical signal physics limitations. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a nonsingular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation in the homotopy parameter, the plant model is variable and, for this reason, model-free algorithms are used to control the flux and speed of the IM due to their capabilities to manage complex dynamics from data without requiring knowledge of the plant model. The performances of the proposed cascade control strategy with advanced algorithms in the two loops were tested by simulation and compared with those obtained with conventional PI controllers, resulting in better dynamic behavior for predictive and model-free control. [ABSTRACT FROM AUTHOR]

Summary: This article presents a Model‐Free Adaptive Nonsingular Fast Terminal Sliding Mode Controller with Super Twisting and Multi‐Layer Perceptron (MLP) neural network for motion control of a 10 DOFs lower limb exoskeleton used in rehabilitation. The proposed controller employs a second‐order ultra‐local model to replace the complex dynamics of the exoskeleton and uses an MLP neural network to estimate the lumped disturbance of the ultra‐local model. To ensure accurate tracking of the desired trajectory and address the estimation errors of the MLP, an Adaptive Nonsingular Fast Terminal Sliding Mode Controller is introduced. Moreover, a Super Twisting approach is employed to eliminate the chattering phenomenon. The system's stability is analyzed using Lyapunov theory, and the desired trajectories are obtained from surface electromyography (EMG) signal measurements. The effectiveness of the proposed controller is validated through co‐simulation experiments using SolidWorks, Simscape Multibody, and MATLAB/Robotics Toolbox. Results demonstrate significant improvements in stability and precision compared to existing model‐free controllers. [ABSTRACT FROM AUTHOR]

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers a detailed review of recent, significant research in this domain, highlighting the use of ANNs in optimizing key energy systems, such as HVAC systems, domestic water heating (DHW) systems, lighting systems (LSs), and renewable energy sources (RESs), which have been integrated into the building environment. After illustrating the conceptual background of the most common ANN architectures for controlling BEMSs, the current work dives deep into relative research applications, thereby exhibiting their methodology and outcomes. By summarizing the numerous impactful applications during 2015–2023, this paper categorizes the predominant ANN-based techniques according to their methodological approach, specific energy equipment, and experimental setups. Grounded in the different perspectives that the integrated studies illustrate, the primary focus of this paper is to evaluate the overall status of ANN-driven control in building energy management, as well as to offer a deep understanding of the prevailing trends at the building level. Leveraging detailed graphical depictions and comparisons between different concepts, future directions, and fruitful conclusions are drawn, and the upcoming innovations of ANN-based control frameworks in BEMSs are highlighted. [ABSTRACT FROM AUTHOR]

Summary This study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system. [ABSTRACT FROM AUTHOR]

In this work, a fixed‐time asymmetric barrier Lyapunov function (BLF)‐based model‐free hybrid position/force controller (FBMHC) is proposed for 3‐DOF SEA‐based manipulator for large surface machining with asymmetric time‐varying output constraints. The proposed FBMHC has a dual‐loop structure, which consists of a force sub‐control loop and a position sub‐control loop. The force sub‐control loop converts the control of the interaction force into position control through the idea of impedance control. The position sub‐control loop is designed under the model‐free control framework, which includes asymmetric time‐varying BLF, time‐delay estimator (TDE), and adaptive compensation term, so as to reduce the complexity of controller design and keep the joint angles within the asymmetric time‐varying output constraints. Strict mathematical derivation proves that the proposed method can achieve fixed‐time convergence, so the influence of the initial value on the control effect is reduced. Finally, the effectiveness of FBMHC is verified by comparison simulation with TDE‐intelligent PD and finite time BLF‐based model‐free control. [ABSTRACT FROM AUTHOR]

Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initially, an enhanced algorithm for multi-innovation model-free control, incorporating error feedback, is presented based on the error feedback principle. Subsequently, a novel control strategy is introduced by delving into PID neural network (NN) recognition and control theory, merging PID NN control with multi-innovation feedback control. Through meticulous mathematical derivation, the proposed strategy is proven to ensure system stability. Compared with traditional NN PID controllers, the convergence rate of the proposed scheme is 50 s faster and the steady-state errors are limited to ±1. [ABSTRACT FROM AUTHOR]

Summary: Model‐based adaptive control suffers over parametrization from the many adaptive parameters compared to the order of system dynamics, leading to sluggish tracking with a poor adaptation transient without robustness. Likewise, adaptive model‐free neurocontrol that relies on the Stone–Weierstrass theorem also suffers from similar problems in addition to over‐fitting to approximate inverse dynamics. This article proposes a novel reinforced adaptive mechanism to guarantee a transient and robustness for the model‐free adaptive control of nonlinear Lagrangian systems. Inspired by the symbiosis of Actor‐Critic (AC) architecture and integral sliding modes, the reinforced stage neural network, analogous to the critic, injects excitation signals to reinforce the parametric learning of the adaptive stage neural network, analogous to the actor to improve the approximation of inverse dynamics. The underlying integral sliding surface error drives improved learning onto a low‐dimensional invariant manifold to guarantee local exponential convergence of tracking errors. Lyapunov stability substantiates the robustness with an improved transient response. Our proposal stands for a hybrid approach between AC and neurocontrol, where the reinforced stage does not require a value function nor reward to provide automatic reinforcement to the adaptive stage parametric adaptation. Dynamic simulations are presented for a nonlinear robot manipulator under different conditions. [ABSTRACT FROM AUTHOR]

Effective control of rehabilitation robots is of paramount importance and requires increased attention to achieve a fully reliable, automated system for practical applications. As the domain of robotic rehabilitation progresses rapidly, the imperative for precise and dependable control mechanisms grows. In this study, we present an innovative control scheme integrating state-of-the-art machine learning algorithms with traditional control techniques. Our approach offers enhanced adaptability to patient-specific needs while ensuring safety and effectiveness. We introduce a model-free feedback linearization control method underpinned by deep neural networks and online observation. While our controller is model-free, and system dynamics are learned during training phases, we employ an online observer to robustly estimate uncertainties that the systems may face in real-time, beyond their training. The proposed technique was tested through different simulations with varying initial conditions and step references, demonstrating the controller's robustness and adaptability. These simulations, combined with Lyapunov's stability verification, validate the efficacy of our proposed scheme in effectively controlling the system under diverse conditions. [ABSTRACT FROM AUTHOR]

The efficient control of HVAC devices in building structures is mandatory for achieving energy savings and comfort. To balance these objectives efficiently, it is essential to incorporate adequate advanced control strategies to adapt to varying environmental conditions and occupant preferences. Model-free control approaches for building HVAC systems have gained significant interest due to their flexibility and ability to adapt to complex, dynamic systems without relying on explicit mathematical models. The current review presents the recent advancements in HVAC control, with an emphasis on reinforcement learning, artificial neural networks, fuzzy logic control, and their hybrid integration with other model-free algorithms. The main focus of this study is a literature review of the most notable research from 2015 to 2023, highlighting the most highly cited applications and their contributions to the field. After analyzing the concept of each work according to its control strategy, a detailed evaluation across different thematic areas is conducted. To this end, the prevalence of methodologies, utilization of different HVAC equipment, and diverse testbed features, such as building zoning and utilization, are further discussed considering the entire body of work to identify different patterns and trends in the field of model-free HVAC control. Last but not least, based on a detailed evaluation of the research in the field, the current work provides future directions for model-free HVAC control considering different aspects and thematic areas. [ABSTRACT FROM AUTHOR]

This paper investigates an optimal model-free control design for a synchronous reluctance motor (Syn-RM) with unknown nonlinear dynamic functions, parameter variations, and disturbances. The idea is to combine a predictive control with a time-delay estimation technique (TDE) in order to successfully deal with the system's uncertainties and make the Syn-RM control scheme easy to implement in real-time. This model-free control strategy comprises two cascade control loops namely outer and inner loops. The outer loop is designed for the mechanical part of Syn-RM to ensure the convergence of the speed dynamics by using a proportional-integral controller while the inner loop is developed to control the uncertain dynamics of currents via an optimal robust controller. In the proposed current loop, the predictive control is enhanced by the inclusion of ultra-local model theory where dynamic functions and disturbances are estimated by instantaneous input-output measurements of the Syn-RM using the TDE approach. Moreover, a particle swarm optimization (PSO) algorithm is also proposed to find the optimal design parameters to improve the dynamic performances of the closed-loop control system. Numerical validation tests of the proposed TDE-based model-free predictive current control (TDE-MFPCC) method are performed in the simulation environment of the Syn-RM system, and the results show the robustness and the effectiveness of the proposed TDE-MFPCC compared to the conventional model-based PCC. [ABSTRACT FROM AUTHOR]

In order to obtain better dynamic response and steady-state accuracy of permanent magnet synchronous linear motor (PMSLM), a hyperlocal model-free position control (LOMFC) method with load disturbance observer was designed to solve the problems of poor control accuracy and stability of traditional PI control methods. The second order hyperlocal model is used to integrate the position controller and the velocity controller to simplify the control structure and optimize the PI control. The double closed-loop model-free position control (MFPC) and triple closed-loop PI control systems were built, and the simulation and experimental comparison of sudden loading and unloading and steady-state operation were carried out respectively. The comparative experiment shows the superiority of this method. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid-based model-free iterative learning control algorithm is proposed to improve the robustness and convergence speed of model-free iterative learning control in noisy environments. The proposed algorithm divides the iterative process into a rapidly decreasing error phase and an error convergence phase, and uses different control algorithms in different phases, thus combining different advantages of the original algorithms. In addition to this, this work proves the convergence and robustness of the proposed algorithm and summarises the design idea of this controller. Finally, the convergence performance of the algorithm in noisy environments and in variable reference trajectory environment is simulated to demonstrate the effectiveness of the algorithm proposed in this work. [ABSTRACT FROM AUTHOR]

Summary: In this work, an asymmetric time‐varying barrier Lyapunov function‐based model‐free hybrid force/position controller (ABLF‐MFC) is proposed for the series elastic actuator‐based 2‐DOF manipulator. Inspired by large surface machining, many scenarios require hybrid force/position control, and the simple position control can no longer meet the above requirements. Therefore, ABLF‐MFC with a dual‐loop structure is established, which are force sub‐control loop and position sub‐control loop. Based on the idea of admittance control, the force sub‐control loop generates a reference trajectory according to the desired interaction force and trajectory. Then, for the position sub‐control loop, to simplify the controller design process, an ultra‐local model (ULM) is introduced, which can approximate the original system. Since the ULM has an unknown term, time‐delay estimation (TDE) is applied to estimate it, which can also reduce the dependence on accurate model parameters. The position sub‐control loop is designed by an asymmetric time‐varying barrier Lyapunov function, an integral‐type Lyapunov function and an adaptive compensation term, so the tracking error can be kept within the preset boundary while ensuring the convergence, and the TDE estimation error can be compensated. Rigorous mathematical proofs and simulation results verify the effectiveness of ABLF‐MFC. [ABSTRACT FROM AUTHOR]

• A fault-tolerant model-free predictive approach is developed to control spacecraft coupled 6-DOF relative motion. In this paper, the previous work of the authors on Subspace Predictive Control (SPC) in the context of spacecraft roto-translational relative motion is pursued, and the introduced novel SPC-based approach to fault-tolerant control (FTC) of nonlinear time-variant systems is further investigated. Firstly, the effectiveness of the proposed SPC-based framework for adaptive control allocation is exploited. Subsequently, separating nonlinear control and control allocation blocks yields a new layered SPC-based control scheme. Furthermore, designating a dedicated controller for each of the coupled motions leads to a novel distributed SPC-based architecture. Accordingly, three complete model-free fault-tolerant controllers for coupled nonlinear time-variant plants are developed, which solely need the knowledge of the occurrence time of faults as prerequisite. An internal fault diagnosis capability is also introduced, which makes the framework completely self-sufficient. Finally, the proposed framework is verified by challenging simulated scenarios. [ABSTRACT FROM AUTHOR]

For the waist assisting, this paper proposes a novel parallel back-support exoskeleton for rehabilitation or power augmentation (PBE-RePA), which is suitable for assisting many types of waist movements and reducing restrictions on daily activities. Considering repetitive waist movements, a time-delay estimation based dual model-free control with initial rectifying mechanism is designed for the trajectory tracking control of PBE-RePA. The system dynamics are reconstructed by ultra-local model while time-delay estimation is utilized to obtain the lumped unknown part in that model. To realize more accurate tracking, an adaptive iterative learning compensator is added to form a new dual model-free control structure, which has excellent control performance in time domain and can enable asymptotically precise tracking along iteration axis. Moreover, the identical initial condition is relaxed by introducing the initial rectifying mechanism to modify the reference trajectory. The virtual prototype of PBE-RePA is established as a platform for visualized co-simulation in MATLAB/Simscape Multibody. The results of two co-simulation cases demonstrate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In industrial hot-dip galvanizing lines, the crossbow flatness defect of steel strips constitutes one of the main sources for coating thickness deviations and inhomogeneities. In this work, a novel algorithm for online minimization of the crossbow flatness defect based on the automatic adjustment of the pot roll intermesh is developed. The devised model-free control algorithm can be easily implemented on an industrial programmable logic controller, even with very limited computational resources. The controller uses measurement data from an electromagnetic strip stabilization device and avoids the use of the delayed measurement data from the downstream radiometric coating thickness gauges. Assuming that a hot-dip galvanizing line is already equipped with an electromagnetic strip stabilization device, additional hardware is not required for implementation. The control algorithm can also be easily adapted to processing lines without an electromagnetic strip stabilizer. A detailed stability analysis allows to derive some basic guidelines to guarantee the convergence of the overall control scheme. A short- and long-term evaluation of measurement results from an industrial hot-dip galvanizing line demonstrates the effectiveness and robustness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Active Queue Management (AQM) for mitigating Internet congestion has been addressed via various feedback control syntheses, especially P, PI, and PID regulators, by using a linear approximation where the "round trip time," i.e., the delay, is assumed to be constant. This constraint is lifted here by using a nonlinear modeling with a variable delay, introduced more than 20 years ago. This delay, intimately linked to the congestion phenomenon, may be viewed as " a flat output." All other system variables, especially the control variable, i.e., the packet loss ratio, are expressed as a function of the delay and its derivatives: they are frozen if the delay is kept constant. This flatness-like property, which demonstrates the mathematical discrepancy of the linear approximation adopted until today, yields also our control strategy in two steps: Firstly, designing an open-loop control, thanks to straightforward Flatness-Based Control (FBC) techniques, and secondly, closing the loop via Model-Free Control (MFC) in order to take into account severe model mismatches, like, here, the number of TCP sessions. Several convincing computer simulations, which are easily implementable, are presented and discussed. • Perhaps the first work using a nonlinear differential equation with a variable delay. • A new flatness-like property is encountered: the delay is a flat output. • Flatness and model-free control lead to easily implementable feedback laws. • Computer experiments show great robustness w.r.t. severe TCP connections variations. • Usual constant linear delay approximations contradict the AQM nonlinear modeling. [ABSTRACT FROM AUTHOR]

This paper suggests to replace Proportional‐Integral and Proportional‐Integral‐Derivative regulators, which play a key rôle in control engineering, by intelligent Proportional‐Derivative feedback loops, or iPDs, which are derived from model‐free control. This standpoint is enhanced by a laboratory experiment. [ABSTRACT FROM AUTHOR]

Starting from Fliess's model-free control (MFC) technique developed 15 years ago, this article aims to provide a systematic framework for characterizing, benchmarking, and generalizing this emerging control technique, with a particular focus on power electronics (PE). It examines the performance of MFC in terms of dynamic response, stability, and robustness, using the classical control theory as a basic tool. A theoretical comparison is conducted with the conventional linear control techniques on dynamic response and performance robustness. A generalized MFC theory and means to enhance its robustness performance are also highlighted. This article suggests that MFC, in contrast to the conventional understanding based on model-independent error dynamics, is practically a model-based control technique. Such model dependence characteristics under MFC become more severe for PE systems. However, following a new design principle, MFC is found possible to possess extraordinarily robust performance against model variations as compared to most existing model-based control methods. On top of that, stability margin is found to be the key bottleneck hindering the performance robustness of the existing MFC techniques. A new MFC with greater stability margin and performance robustness is proposed in this article. Comprehensive Pareto fronts analysis, simulations, and experiments are conducted on a buck converter system to verify the new understandings and conclusions drawn from the framework. With the new design principle and the new MFC, the system is able to demonstrate an almost constant dynamic response despite 25-fold circuit parameter (R, C, and L) variations and 1.85-fold input voltage variations. [ABSTRACT FROM AUTHOR]

In addition to the difficulty of system modeling, every physical system faces actuator saturation, making the real control different from the controller output (desired control input). When this occurs, the controller output does not regulate the system properly and errors occur as a result of incorrect updating. This is known as the windup phenomenon. In the event that the controller is configured to override actuator saturation, it can lead to a failure in the controller's performance, such as large overshoots, high response time, and even system instability in the worst case. Therefore, in this paper, a new robust model-free controller method is proposed. It is a combination of four nonlinear control techniques, namely model-free controller, fractional-order sliding mode controller, fractional integral–proportional (PI) controller, and anti-windup compensator, which gives rise to the new model-free controller algorithm called hybrid fractional-order intelligent PI fractional sliding mode controller with an anti-windup compensator termed (MF-FOiPI-FOSMC-AW). However, to illustrate the effectiveness of the new proposed MF-FOiPI-FOSMC-AW method, simulation and experimental results compared to classical PI and iPI controllers (with and without an anti-windup compensator) on a hydraulic system are presented in the presence or absence of external disturbances for different types of references. Finally, simulation is conducted through an experimental comparison of the proposed approach with other strategies such as the classical anti-windup PI controller and the anti-windup intelligent PI controller, which is carried out for this purpose. [ABSTRACT FROM AUTHOR]

Strong parameter dependence and large current ripple are two shortcomings that obstruct the development of model predictive control for voltage source inverters (VSIs). To solve these problems, in this article,a double-vector model-free predictive control (MFPC) method for VSI with visualization analysis is proposed. First, the ultralocal model of the VSI is established and a full-order sliding model observer is designed to estimate the lumped disturbance. MFPC is achieved with enhanced parameter robustness. Then, a double-vector MFPC method is further proposed. By applying two vectors per control period, the current ripple is reduced significantly. A detailed visualization analysis method is proposed, which verifies the effectiveness of the proposed double-vector MFPC method in theory. Besides, the proposed visualization analysis method has the potential to be used to analyze the effectiveness of other types of multivector model predictive control method with different cost functions. Detailed comparative experimental studies verify the effectiveness of the proposed double-vector MFPC method. [ABSTRACT FROM AUTHOR]

This paper presents the first framework (up to the authors' knowledge) to address time-varying objectives in finite-horizon Deep Reinforcement Learning (DeepRL), based on a switching control solution developed on the ground of Bellman's principle of optimality. By augmenting the state space of the system with information on its visit time, the DeepRL agent is able to solve problems in which its task dynamically changes within the same episode. To address the scalability problems caused by the state space augmentation, we propose a procedure to partition the episode length to define separate sub-problems that are then solved by specialised DeepRL agents. Contrary to standard solutions, with the proposed approach the DeepRL agents correctly estimate the value function at each time-step and are hence able to solve time-varying tasks. Numerical simulations validate the approach in a classic RL environment. [ABSTRACT FROM AUTHOR]

Predictive control offers many advantages such as simple design and a systematic way to handle constraints. Model predictive control (MPC) belongs to predictive control, which uses a model of the system for predictions used in predictive control. A major drawback of MPC is the dependence of its performance on the model of the system. Any discrepancy between the system model and actual plant behavior will greatly affect the performance of the MPC. Recently, model-free approaches have been gaining attention because they are not dependent on the system model parameters. To obtain the advantages of both a model-free approach and predictive control, model-free predictive control (MFPC) is being explored and reported in the literature for different applications such as power electronics and electric drives. This paper presents an overview of model-free predictive control. A comprehensive review of the application of MFPC in power converters, electric drives, power systems, and microgrids is presented in this paper. Moreover, challenges, opportunities, and emerging trends in MFPC are also discussed in this paper. [ABSTRACT FROM AUTHOR]

Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user's interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals' baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training. [ABSTRACT FROM AUTHOR]

This paper deals with the power generation control in variable-speed wind turbines. These systems have two operation regions which depend on wind turbine tip speed ratio. In this work, we propose the use of model-free control strategy to ensure the maximum power extraction in the low-wind speed region. This control approach offers attractive features such as strong robustness against unmodeled dynamics and easy parameters tuning since it is based on the so-called ultra-local model and the intelligent PID. An algebraic estimator of the ultra-local dynamics is introduced to get rid of the unavailability of accurate wind turbine model and easily implement the proposed control law. The suggested model-free control method has been tested on a 1.5MW three-blade wind turbine utilizing the Fatigue, Aerodynamics, Structures, and Turbulence wind turbine simulator from the National Renewable Energy Laboratory. The outcomes of this validation compared to the industry-standard approach indicate that the proposed control approach is efficient in power tracking. [ABSTRACT FROM AUTHOR]

A model-free optimal tracking controller is designed for discrete-time nonlinear systems through policy gradient adaptive critic designs (PGACDs) with experience replay (ER). By using system transformation, optimal tracking control problems are converted into optimal regulation problems. An off-policy PGACD algorithm is developed to minimize the iterative $Q$ -function and improve the tracking control performance. The proposed method is realized based on the critic network and the actor network (AN), which are applied to approximate the iterative $Q$ -function and the iterative control policy, respectively. Then, the policy gradient technique is introduced to derive a novel weight updating law of the AN explicitly by using measured system data only. The convergence of the iteration is established through theoretical analysis, and the uniform ultimate boundedness is demonstrated for the closed-loop system under the PGACD-based controller by using Lyapunov’s direct method. To guarantee the stability and increase the data usage efficiency of the learning process, an ER-based learning framework is designed to improve the realizability of the proposed method. Finally, simulation results of two examples are provided to demonstrate the performance of the off-policy PGACD algorithm. [ABSTRACT FROM AUTHOR]

The asymmetric loads on wind turbines are becoming a fatal constraint factor with the increase of wind turbine size. In order to tackle this challenge, individual pitch control mechanism is introduced into the wind turbines. However, the application scopeof the traditional individual pitch control (IPC) is limited due to the complex dynamic coupling among wind turbine components, the strong nonlinearity of the pitch system and the inevitable unmodeled dynamics. Therefore, a multivariable model-free adaptive control (MIMO-DE-MFAC) strategy with differential characteristic is first proposed in this paper. Then, the proposed method is applied to the individual pitch control of the 5MW wind turbine. The simulation results from FAST demonstrate that the MIMO-DE-MFAC achieves a notable performance improvement on blade root compared with the PI. Furthermore, compared with other existing model-free adaptive control (MFAC) strategies, the proposed method not only achieves the optimal control results, but also has stronger robustness in speed-varying wind fields. Finally, the hardware-in-the-loop (HIL) experiment further verifies the reliability of the above simulation results, which provides a useful reference value for the field test. [ABSTRACT FROM AUTHOR]

In this article, a model-free control (MFC) using super twisting nonlinear fractional order sliding mode for aerodynamic heating ground simulation of hypersonic vehicles (AHGSHV) is proposed. Firstly, the mathematical model of AHGSHV is built up. To reduce order and simplify the dynamic model of AHGSHV, an ultra-local model of MFC is taken into consideration. Then, time delay estimation can be used to estimate systematic uncertainties and external unknown disturbances. On the basis of the original fractional order sliding mode surface, the nonlinear function fal is introduced to design the nonlinear fractional order sliding mode surface, which can guarantee stability, increase convergence rate, and reduce static error and saturation error. In addition, the super twisting reaching law is used to improve the control performance of the reaching phase, resulting from the existence of sign function in the integral term, and it can effectively reduce the high-frequency chattering. Moreover, the Lyapunov function is used to prove the stability of the whole system. Finally, several numerical simulations show that the designed controller has more advantages than others. [ABSTRACT FROM AUTHOR]

In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a 'black box' with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights' adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications. [ABSTRACT FROM AUTHOR]

A model-free control method based on iterative learning law combined with adaptive super-twisting is proposed to realize the synchronous coordination control of multi-lift overhead crane system for the problems of inaccurate modeling, system parameter variation, and disturbance uncertainty that exist in multi-lift overhead crane system. First, a load-coupling model of the double-container overhead crane considering the deformation tangential force in the interlocking mode is established. Second, a time-varying sliding mode surface (TSMC) designed using nonlinear functions effectively improves the convergence speed of the system state. The method of iterative learning control is introduced to compensate the system dynamics to achieve model-free control, and the dynamic iterative learning control (DILC) is designed to improve the convergence speed of the error of the system and the steady-state performance. To suppress uncertainty disturbances and avoid control gain overestimation, an adaptive gain is added to the generalized super-twisting algorithm, which has the advantages of both finite-time convergence and chattering suppression, and improves the robustness and tracking performance of the multi-lift overhead crane system. The stability of the controlled system is analyzed using Lyapunov stability theory. The simulation experiments illustrate the effectiveness of the proposed synchronization control scheme. [ABSTRACT FROM AUTHOR]

The core of the optimal tracking control problem for nonlinear systems is how to ensure that the controlled system tracks the desired trajectory. The utility functions in previous studies have different properties which affect the final tracking effect of the intelligent critic algorithm. In this paper, we introduce a novel utility function and propose a Q-function based policy iteration algorithm to eliminate the final tracking error. In addition, neural networks are used as function approximator to approximate the performance index and control policy. Considering the impact of the approximation error on the tracking performance, an approximation error bound for each iteration of the novel Q-function is established. Under the given conditions, the approximation Q-function converges to the finite neighborhood of the optimal value. Moreover, it is proved that weight estimation errors of neural networks are uniformly ultimately bounded. Finally, the effectiveness of the algorithm is verified by the simulation example. [ABSTRACT FROM AUTHOR]

This work presents the design and application of the Model-free controller with algebraic estimator and ultra-local model on the common-rail high-pressure in a Direct Injection Fuel System of an internal combustion engine. In this application, the PID controller is traditionally used, but it needs different settings as modifications in the operating point occur. These changes encompass motor rotation, injection time, or torque demand. On the other hand, this paper considers only a single set of control parameters that provides good performance considering a broad range of operating points. A commercial electronic control unit (FlexECU) offering the possibility of new software development was used to implement the control algorithm and manage all engine functions. The tuning process of the controller is presented and discussed. Tests were carried out, and the results were graphically demonstrated, evidencing the easy applicability, robustness, and ability of the considered Mode-free scheme to control nonlinear systems, even facing important dynamic variations. • Model-free control with algebraic estimator is applied to a direct injection system. • An intelligent proportional–integral controller is used in the model-free framework. • The controller algorithm is embedded in commercial electronic hardware (FlexECU). • Experimental validation is accomplished in an engine EA211 Flex Fuel turbocharged. • DI system achieved excellent performance with no changes in the controller parameters tuning. [ABSTRACT FROM AUTHOR]

In this work, a shifting asymmetric time-varying barrier Lyapunov function (BLF)-based model-free hybrid force/position controller (SABMHC) is proposed for the endpoint of the 3-DOF series elastic actuator (SEA)-based manipulator which can be used for machining of large curved surfaces. The proposed SABMHC contains a force sub-control loop and a position sub-control loop. The force sub-control loop converts the force control into position control based on the idea of impedance control, and realizes the hybrid force/position control through the position sub-control. Then, in order to achieve accurate trajectory tracking control of the position sub-control loop, a shifting asymmetric time-varying BLF-based model-free controller is proposed, which includes the error shifting function, asymmetric time varying BLF, time-delay estimation (TDE) technique, and adaptive compensation term. To prevent the random initial error from being so large that it exceeds the pre-set boundary and affects the steady-state response, an error shifting function is introduced. The error shifting function can make the shifting error independent of the tracking error before reaching the pre-set shifting time, so as to achieve better control performance by setting a small pre-set boundary for the shifting error. With the Lyapunov function, the designed SABMHC can ensure that the stability and the shifting error converges within the pre-set boundary in a finite time. Finally, the effectiveness and benefits of the proposed SABMHC are analyzed by comparison with the TDE-based intelligent PID (TDE-iPD) and BLF-based sliding-mode controller (BLF-SMC). The control structure of the proposed controller. • A dual-loop controller is proposed to implement hybrid force/position control 3-DOF SEA-based manipulator with random initial error. • The force sub-control loop converts the force control into position control based on the idea of impedance control. • The position sub-control is designed on error shifting function, asymmetric time varying BLF, TDE, and adaptive compensation term. • The error shifting function can ensure good steady-state response even when the random initial error exceeds the pre-set boundary. • The faster convergence speed can be obtained by free selection of smaller pre-set boundaries for the shifting error. [ABSTRACT FROM AUTHOR]

This vision article shows how to build on the framework of event-triggered Control Barrier Functions (CBFs) to design model-free controllers for safety-critical multi-agent systems with unknown dynamics, including humans in the loop. This event-triggered framework has been shown to be computationally efficient and robust while guaranteeing safety for systems with unknown dynamics. We show how to extend it to model-free safety critical control where a controllable ego agent does not need to model the dynamics of other agents and updates its control based only on events dependent on the error states of agents obtained by real-time sensor measurements. To facilitate the process of real-time sensor measurements critical in this approach, we also present CBF relative degree reduction methods, which can reduce the number of such measurements. We illustrate the effectiveness of the proposed framework on a multi-agent traffic merging decentralized control problem and on highway lane changing control with humans in the loop and relative degree reduction. We also compare the proposed event-driven method to the classical time-driven approach. [ABSTRACT FROM AUTHOR]

The problem of controlling coupled six-degrees-of-freedom (6-DOF) relative motion of an integrated-actuation spacecraft in the presence of actuator faults, and especially failures, is investigated in this paper. Considering the complications and limitations of the common control paradigm (which requires a mathematical model of a plant), a novel data-driven control framework is proposed. Specifically, the subspace predictive control (SPC) approach (which is an elegant data-driven framework based on a special combination of the model predictive control method and the subspace system identification technique) is developed and extended to control nonlinear plants and to tolerate abrupt and severe faults. Accordingly, a model-free fault-tolerant control method for coupled nonlinear time-variant plants is provided, such that its only fault diagnostic requirement is detection of the occurrence time of faults. Furthermore, a fully decentralized multispacecraft strategy is proposed and formulated that is quite suitable for a data-driven cooperative control approach. The effectiveness of the developed framework is demonstrated via simulation. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads. Design/methodology/approach: In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system's uncertainties and disturbances can be compensated. Findings: The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity. Practical implications: In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads. Originality/value: The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location. [ABSTRACT FROM AUTHOR]

Canals are constructed worldwidely to divert water from rich to arid areas to mitigate water shortages. Since water resource is fairly limited, it is essential to perform canal control efficiently to improve water-delivery performance. A promising solution is to leverage model predictive control (MPC), which calculates the desired canal action at each time step via reliable predictions of the model. However, the predictive model dependence degrades the practicability and the iterative calculation incurs intensive computations, especially for large-scale canals with high-dimensional state and action spaces (curse of dimensionality). This article presents a new canal control model named efficient model-free canal control (EMCC) that obtains control policies in a model-free way via deep reinforcement learning (DRL) and alleviates the curse of dimensionality via domain knowledge (control experience). EMCC adopts the hidden Markov model with Gaussian mixture densities (GMM-HMM) to model canal system dynamics with control experience, and initializes it according to the actual operation data. Besides, we design a reward generator collaborated with GMM-HMM to supervise the reinforcement learning around control experiences to obtain more efficient control policies. We evaluate EMCC via numerical simulations on Chinese largest water-delivery project (SNWTP). Experimental results show that EMCC leads to significant convergence performances compared with crude applications of DRL on large-scale canals, and achieves desired objectives more satisfactorily than MPC and control-experience under two typical water-delivery tasks. [ABSTRACT FROM AUTHOR]

Thanks to the great advances in the installations of large wind turbines, individual pitch control (IPC) has gained mounting interest among researchers. The actuator faults, however, have become a hindrance with the individual pitch process introducing more actuator actions and the pitch system operating in complex conditions. The traditional fault-tolerant control strategies of the IPC, which are mainly based on the system modeling information, and require the process of fault diagnosis, isolation or parameter estimation. The subsequent model errors and unmodeled dynamics have somehow limited the application of the strategies. Therefore, this paper proposes a passive fault-tolerant individual pitch control scheme independent of the above process, and based on the scheme, a dual multivariable model-free adaptive control strategy with differential characteristic is constructed. The benchmark 5 MW wind turbine model provided by FAST is employed to evaluate the performance. The results show that, compared with the traditional individual pitch control strategy, the proposed scheme can achieve almost identical load control and even better power control under fault-free conditions. Once the fault occurs, the proposed scheme can quickly compensate for the faulty actuator dynamics, and finally achieve almost the same performance in power and load control as under the nominal conditions. • A fault-tolerant individual pitch control scheme is proposed for the actuator faults. • The scheme is independent of fault diagnosis, isolation and parameter estimation. • A dual multivariable model-free adaptive control strategy is constructed. • The control strategy can guarantee robustness under various fault conditions. [ABSTRACT FROM AUTHOR]

The purpose of this research is to construct a simple and practical controller design method, considering the actuator's parameter uncertainty, without using a model of controlled objects. In this method, a controller is designed with an actuator model including a single-degree-of-freedom virtual structure inserted between actuator and controlled object, resulting in a model-free controller design. Furthermore, an H ∞ control problem is defined so that the actuator's parameter uncertainty is compensated by satisfying a robust stability condition. Because the actuator model including the virtual controlled object is a simple low-order system, and the actuator's parameter uncertainty is considered, a controller with high robustness to the actuator's parameter uncertainty can be designed based on traditional model-based control theory. The effectiveness of the proposed method is verified by both simulation and experiment. [ABSTRACT FROM AUTHOR]

• The complex dynamics is formulated by a second order ultra-local model as the controlled object. • Prescribed tracking performance is guaranteed via the universal asymmetric barrier Lyapunov function. • Fixed-time convergence of the closed-loop system. • Virtual prototype and visualization simulation. In this paper, a model-free fixed-time control strategy with prescribed performance is proposed for the wearable exoskeleton angle tracking control. Firstly, an ultra-local model is employed to formulate the plant dynamic relation between control input and output. Then, an intelligent-proportional-derivative controller is designed based on the time delay estimation technique. To improve the tracking precision, a prescribed performance sliding mode controller is designed and a uniform robust exact disturbance observer is employed to compensate the controller. Besides, based on the universal barrier Lyapunov function, the stability is analyzed for the closed-loop system and the prescribed performance is proved. Finally, a 5 degree-of-freedom wearable exoskeleton virtual prototype is developed in SOLIDWORKS and transferred to MATLAB/SimMechanics as the controlled object of visualization simulation, simulation results show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

An algebra-based active disturbance rejection control approach for uncertain second-order flat systems, perturbed by a matching perturbation, is presented. This approach consists of proposing an online algebraic estimator for the uncertain system, together with an algebraic reconstructor for the unknown velocity. To assess the efficiency of the approach, numerical simulations to control an uncertain longitudinal vehicle system were carried out. [ABSTRACT FROM AUTHOR]

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. • Adaptive law is introduced into MFAC algorithm to improve the robustness of AUV in complex environment. • The improved event triggering mechanism reduces the system channel occupancy. • A novel adaptive threshold is proposed, which can make the AUV converge to the desired domain smoothly. • The effectiveness of the algorithm in engineering is verified by practical experiments. [ABSTRACT FROM AUTHOR]

In this article, a model-free based control strategy is applied to a gripper activated by Mckibben's pneumatic muscles. The gripper is composed of three fingers and is based on an original mechanical design to grip a large range of objects (by a weight, size, form, ... point-of-view). A model-free based control strategy is proposed to manage the force control phases. An experimental setup is introduced to experimentally test and validate the proposed control strategy using a very simple setup composed of a single force sensor, an embedded electronic board, and a single pressure proportional solenoid valve. [Display omitted] • A model-free control applied to a gripper actuator by Mckibben's pneumatic muscles. • Control of the contact force between the fingers and the object. • Experimentation on a prototype. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • A robust control algorithm for efficient supply chain management: a new approach for controlling complex supply chains, in cases where traditional system modelling falls short. • New advances in time series for delay estimation and compensation: delay compensation is achieved via a theorem due to Cartier and Perrin which is expressed in term of nonstandard analysis. • Customer demand forecasting using new trends in time series analysis: predicting customer demand is a valuable solution which improves inventory control, production planning, and overall supply chain efficiency. • An efficient approach for bullwhip effect attenuation: The proposed approach has the potential to mitigate the bullwhip effect, which is one of the primary causes of supply chain efficiency degradation. • Qualitative and quantitative cost studies and analysis: Conducted studies and analyses substantiate this approach's effectiveness as a valuable tool for decision-making. [ABSTRACT FROM AUTHOR]