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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]

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]

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]

This paper proposes an intelligent proportional integral (PI) control based on an ultra-local model and a Kalman filter with automatic parameter tuning for vehicle yaw-rate control. A good vehicle yaw-rate controller is essential for autonomous driving. The traditional PI controller proposed in the conventional method cannot realize the desired yaw-rate response because the vehicle velocity yaw-rate characteristics change with vehicle velocity. Although an intelligent model-free PI control can improve the yaw-rate response, it does not account for the measurement noise, so the noise amplified by feedback control is added to the control input. This problem is crucial because a large control input velocity (i.e. a high steering-angle velocity) may cause wheel damage. A Kalman filter was introduced to conventional intelligent PI control to address this problem. In addition, we automatically tune the design parameters of the Kalman filter by Bayesian optimization. The effectiveness of the proposed method was investigated in a vehicle simulator. The experimental results confirm higher control performance of the proposed method than of conventional methods in noisy cases.

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]

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 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]

This paper investigates the problem of attitude tracking in quadrotor unmanned aerial vehicles (UAVs) using a model-free online learning control (MFOLC) scheme. The attitude system, which is represented by unit quaternions, is considered in the presence of uncertain and unknown inertia parameters, time-varying external disturbances, and angular velocity measurement noise. A computationally low-cost control scheme consisting of a model-free baseline controller and a module capable of learning from previous control input is designed. The proposed controller does not require precise inertial parameters and does not involve feedforward terms that use these parameters and true system states. This ensures that the approach can protect the control effort from sensor noise as well as parameter uncertainty. We also show that all the signals in the closed-loop system are uniformly ultimately bounded. Comparative simulations and real-world experiments are conducted for validation, which demonstrate the effectiveness and fine performance of the proposed scheme. [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]

Animals are capable of robust and reliable control in unstructured environments, where they effortlessly overcome the uncertainty of interaction and are capable of exploiting singularities. These conditions are a well-known challenge for robots due to the limitations of projected dynamics, which requires accurate modelling and is susceptible to singularities. This work proposes a compliant passive control method for redundant manipulators based on a superimposition of multiple passive task-space controllers in a hierarchy without requiring any knowledge of the robot dynamics. The proposed control framework of passive controllers is inherently stable, numerically well-conditioned (as no matrix inversions are required), and computationally inexpensive (as no optimisation is used). This method leverages and introduces a novel stiffness profile for a recently proposed passive controller with smooth transitions between the divergence and convergence phases making it particularly suitable when multiple passive controllers are combined through superimposition. The experimental results demonstrate that the proposed method achieves sub-centimetre tracking performance during demanding dynamic tasks with fast-changing references, while remaining safe to interact with and robust to singularities. The data further show that the robot can fully take advantage of the redundancy to maintain the primary task accuracy while compensating for unknown environmental interactions, which is not possible from current frameworks that require accurate contact information. [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]

This paper establishes a model-free finite-time tracking control of nonlinear robotic manipulator systems. The proposed controller incorporates both Time Delay Estimation (TDE) and an enhanced Terminal Sliding Mode Control (TSMC). The improved TSMC scheme is devised using Fractional-Order TSMC (FOTSMC) and Proportional-Integral-Derivative (PID) control to obtain robust tracking and high control performance. The TDE is designed to estimate the unknown nonlinear dynamics of robotic manipulators, including the Stribeck friction and the external disturbances. Due to Stribeck friction, the efiect of TDE error may fail to obtain the desired error performance; thus, another TDE loop is devised to compensate for TDE error generated by non-smooth frictions. The Lyapunov criterion is used to investigate the finite-time stability to analyze the behavior of the designed approach. Finally, computer simulations of the proposed method on PUMA 560 robotic manipulators are performed in contrast with FOTSMC and Adaptive Fractional-Order Nonsingular Terminal Sliding Mode Control (AFONTSMC). [ABSTRACT FROM AUTHOR]

Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications. [ABSTRACT FROM AUTHOR]

Copyright of Electric Machines & Control / Dianji Yu Kongzhi Xuebao is the property of Electric Machines & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Renewable energy generation is increasingly important due to serious energy issues. A Doubly Salient Permanent Magnet Generator (DSPMG) can be an interesting candidate for tidal stream renewable energy systems. However, the special structure makes the system nonlinear and strongly coupled even after Park transformation and involves a larger torque ripple. Previous research mainly focused on model-based control for this machine, which is very sensitive to the parameters. Thus, to control the complex systems stably and accurately, two model-free control algorithms, Active Disturbance Rejection-Based Iterative Learning Control (ADRILC) and Active Disturbance Rejection Control–Iterative Learning Control (ADRC–ILC), are proposed for the current and speed control loops of a DSPMG-based Tidal Stream Turbine (TST), respectively. ADRC–ILC uses ADRC to deal with the external non-periodic speed ripple and adopts ILC to reduce the internal periodic speed ripple. ADRILC employs an iterative method to improve the ESO for the enhancement of the convergence rate of ILC. Considering the variable tidal speed, when the speed is above the rated value, Maximum Power Point Tracking (MPPT) must be changed to a power limitation strategy for limiting the generator power to the rated value and extending the system operating range. Thus, Optimal Tip Speed Ratio (OTSR)-based MPPT (for a low tidal current speed) and Leading Angle Flux-Weakening Control (LAFWC) (for a high tidal current speed) strategies are also proposed. According to the simulation results, the proposed ADRC–ILC + ADRILC has the lowest torque ripple, the highest control accuracy, as well as a good current tracking capability and strong robustness. At the rated speed, the proposed method reduces the torque ripple by more than 20% and the speed error by about 80% compared with PI control: the current difference is limited in 2A. The LAFWC proposed for an excessive tidal current speed is effective in conserving the electromagnetic power and increasing the generator speed. [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]

To maintain a safe distance between the autonomous vehicle and the leader, ensure that the vehicle runs at its expected speed as far as possible, and achieve various control requirements such as speed, distance and collision avoidance, a model-free prescribed performance adaptive cruise control (ACC) algorithm based on funnel control is proposed. The contributions of this paper are that the designed ACC algorithm only requires the speed and position information and can constrain their tracking errors within a predetermined range. When the follower is far away from the leader, the speed-prescribed performance controller adjusts the follower vehicle's speed to the reference velocity. When the follower vehicle approaches the leader vehicle, a distance-prescribed performance controller is designed to adjust the distance between the follower and the leader. On this basis, the prescribed performance function can expand the switching interval, thereby improving the robustness of the speed and distance control switching process. The effectiveness of the designed algorithm is demonstrated in three scenarios, such as approaching and following, emergency braking, and frequent starting and stopping. The results show that during the speed control stage, the designed algorithm allows the vehicle's operating speed to vary within a predetermined spatial range; in the distance control stage, the designed algorithm strictly limits the distance error within the preset range. The speed and distance of the vehicle change smoothly, and there is no overshoot during the initial state adjustment, emergency braking, and frequent start and stop stages, demonstrating a good control effect. [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 this paper, an adaptive neural network global fractional order fast terminal sliding mode model-free intelligent PID control strategy (termed as TDE-ANNGFOFTSMC-MFIPIDC) is proposed for the hypersonic vehicle ground thermal environment simulation test device (GTESTD). Firstly, the mathematical model of the GTESTD is transformed into an ultra-local model to ensure that the control strategy design process does not rely on the potentially inaccurate dynamic GTESTD model. Meanwhile, time delay estimation (TDE) is employed to estimate the unknown terms of the ultra-local model. Next, a global fractional-order fast terminal sliding mode surface (GFOFTSMS) is introduced to effectively reduce the estimation error generated by TDE. It also eliminates arrival time, accelerates the convergence speed of the sliding phase, guarantees finite time arrival, avoids the singularity phenomenon, and bolsters robustness. Then, as the upper bound of the disturbance error is unknown, an adaptive neural network (ANN) control is designed to approximate the upper bound of the estimation error closely and mitigate the chattering phenomenon. Furthermore, the stability of the control system and the convergence time are proven by the Lyapunov stability theorem and are calculated, respectively. Finally, simulation results are conducted to validate the efficacy of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of robust and high precision trajectory tracking control of uncertain robot manipulators with prescribed performance. Time delay estimation (TDE) technique is employed to estimate system model, then a new self‐adjusting strategy (SAS) is designed to adjust TDE gain online. Prescribed performance control (PPC) method is used to guarantee transient response speed and steady‐state accuracy. Moreover, the transform function of PPC employed in this paper has unlimited domain, which greatly improves the robustness and stability of system. An improved fixed‐time dynamical system is firstly deduced and analyzed, then a new fast nonsingular terminal sliding mode surface is designed to accelerate convergence rate of tracking errors. Finally, the whole system is strictly proven to be practical finite‐time stable, which means that tracking errors can converge to a small neighborhood of zero within a uniformly bounded convergence time. Main advantages of the proposed approach include model‐free, robust, singularity‐free, faster transient response and higher steady‐state tracking precision. Experimental results carried out on the Rethink Sawyer Robot also verified the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article has the objective of presenting our method to develop and test a motion control system for a heavy-duty hydraulically actuated manipulator, which is part of a newly developed prototype featuring a fully-autonomous unmanned forestry machine. This control algorithm is based on functional analysis and differential algebra, under the concepts of a new type of approach known as model-free intelligent PID control (iPID). As it can be unsafe to test this form of control directly on real hardware, our main contribution is to introduce a framework for developing and testing control software. This framework incorporates a desktop-size mockup crane equipped with comparable hardware as the real one, which we design and manufactured using 3D-printing. This downscaled mechatronic system allows to safely test the implementation of control software in real-time hardware directly on our desks, prior to the actual testing on the real machine. The results demonstrate that this development framework is useful to safely test control software for heavy-duty systems, and it helped us present the first experiments with the world’s first unmanned forestry machine capable of performing fully autonomous forestry tasks.

One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller’s performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort.

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]

This paper presents the application of an ultra-local model-free controller to control the high pressure in a direct injection fuel system, which has high nonlinearities and is subjected to disturbance due to fuel injections. A wide range of operating points is considered, making the controller tuning a difficult task. The main target is to select only one adjustment that meets the performance requirements. A mathematical model is established for simulation, considering the main parts of the direct injection system. The steps used for empirically tuning the controller parameters are discussed. Simulations are carried out, and the results demonstrate the ability of model-free to control a nonlinear system without knowledge of the system parameters. [ABSTRACT FROM AUTHOR]

This paper considers a model-free control approach to Euler-Lagrange equations and proposes a new quantitative performance measure with its Lyapunov-based computation method. More precisely, this paper aims to solve a trajectory tracking problem for uncertain Euler-Lagrange equations by using a model-free controller with a proportional-integral-derivative (PID) control form. The L ∞ -gain is evaluated for the closed-loop systems obtained through the feedback connection between the Euler-Lagrange equation and the model-free controller. To this end, the input-to-state stability (ISS) for the closed-loop systems is first established by deriving an appropriate Lyapunov function. The study further extends these arguments to develop a computational approach to determine the L ∞ -gain. Finally, the theoretical validity and effectiveness of the proposed quantitative performance measure are demonstrated through a simulation of a 2 -degree-of-freedom ( 2 -DOF) robot manipulator, which is one of the most representative examples of Euler-Lagrange equations. [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]

The article proposes a model-free sliding mode prescribed performance control method for robotic manipulator. For accelerating the errors converging rate as well as bringing down the real-time control torque, an error-driven nonsingular fast terminal sliding mode is employed in this control method. Besides, a modified power reaching law is newly designed, which is able to offer better chattering elimination performance than traditional power reaching law. To solve the problem that the controller's performance highly relies on the accuracy of robotic model, a model-free control idea is introduced where an ultra-local model is established and the estimation of time delay is used to approximate the unknown part of the ultra-local model. Considering the transient performance is a significant element that effects the using security and system stability, the technique of prescribed performance control is introduced to limit the state errors in a prescribed region. The closed-loop stability proof of the whole system is achieved according to Lyapunov's stability theorem. The proposed controller's superiority and feasibility compared with other controllers are demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• 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]

This study experimentally verifies robustness of a model-free vibration controller based on a virtual controlled object (VCO) considering parametric uncertainty of actuator. A proof-mass actuator, which can be modeled as a single-degree-of-freedom (SDOF) system, is used. A VCO, which is defined as an SDOF structure, is introduced between a real controlled object and the actuator model. The parameters of the VCO are determined so as to achieve model-free vibration control. A state equation to derive the model-free controller is constructed using the two-degree-of-freedom (2DOF) structure composed of the actuator model and the VCO. The parametric uncertainty of the actuator is quantitatively characterized in the 2DOF structure. The mixed H 2 / H ∞ control theory is used to design a model-free controller. The vibration suppression performance and robustness to the actuator uncertainty of the proposed method are validated by experiments. Simulation studies are also conducted to enhance the validity of the experimental results. As a result, the proposed damping method exhibits good damping performance and strong robustness to the actuator uncertainty and characteristic changes in controlled object. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work describes the design of an adaptive cruise control (ACC) applied to a realistic automotive simulation model, considering both upper- and lower-level controllers. The upper-level controller provides a desired acceleration/deceleration to a host vehicle to maintain a safe distance related to a leader vehicle or to track a desired cruise speed otherwise. On the other hand, the lower-level controller provides control signals to the throttle and brake pedals of the host vehicle aiming to track the desired acceleration/deceleration setpoint from the upper-level controller. For the upper-level controller, we consider a control framework that unifies the stability/tracking objective, expressed as a control Lyapunov function (CLF), the safety constraint, expressed as a control barrier function (CBF), and the comfort constraint, by means of a quadratic programming (QP), where safety must be prioritized. The lower-level controller is implemented considering a model-free control. The results, obtained by numerical simulations, demonstrate that the safe distance between the vehicles is ensured and the desired cruise speed is tracked adequately. Therefore, the stability/tracking objective and the safety and comfort constraints, related to the upper-level controller, are properly satisfied. The lower-level controller tracks the desired acceleration/deceleration demanded by the upper-level controller with good performance. [ABSTRACT FROM AUTHOR]

One of the challenges of autonomous driving is to increase the number of situations in which an intelligent vehicle can continue to operate without human intervention. This requires path-tracking control to keep the vehicle stable while following the road, regardless of the shape of the road or the longitudinal speed at which it is moving. In this work, a control strategy framed in the Model-Free Control paradigm is presented to control the lateral vehicle dynamics in a decoupled control architecture. This strategy is designed to guide the vehicle through trajectories with diverse dynamic constraints and over a wide speed range. A design method for this control strategy is proposed, and metrics for trajectory tracking quality, system stability, and passenger comfort are applied to evaluate the controller's performance. Finally, simulation and real-world tests show that the developed strategy is able to track realistic trajectories with a high degree of accuracy, safety, and comfort. [ABSTRACT FROM AUTHOR]

This paper proposes a control algorithm for controlling the position and attitude of a coaxial rotor drone without knowing the model dynamics. To overcome the major drawback of model-dependent approaches, an optimal model-free fuzzy controller (OMFFC) based on the estimation of the unknown dynamic function of the system is proposed. A time-delay estimation (TDE) technique is effectively exploited to approximate the unknown dynamic function of the system. The estimation error is then offset using a robust adaptive fuzzy logic compensator. Based on Lyapunov stability arguments, the global asymptotic stability of the coaxial rotor drone system is proven. Moreover, a flower pollination-based algorithm is also proposed to generate the optimal parameters to address the trade-off between optimal tracking performance and the design conditions related to the closed-loop stability requirements. The numerical simulations illustrate how the proposed methodology leads to the best performance, as well as less computational complexity compared to the standard proportional–integral–derivative and time-delay estimation-based controllers in the presence of external disturbances. [ABSTRACT FROM AUTHOR]

This paper considers a model-free control approach to Euler-Lagrange equations and proposes a new quantitative performance measure with its Lyapunov-based computation method. More precisely, this paper aims to solve a trajectory tracking problem for uncertain Euler-Lagrange equations by using a model-free controller with a proportional-integral-derivative (PID) control form. The $ L_\infty $-gain is evaluated for the closed-loop systems obtained through the feedback connection between the Euler-Lagrange equation and the model-free controller. To this end, the input-to-state stability (ISS) for the closed-loop systems is first established by deriving an appropriate Lyapunov function. The study further extends these arguments to develop a computational approach to determine the $ L_\infty $-gain. Finally, the theoretical validity and effectiveness of the proposed quantitative performance measure are demonstrated through a simulation of a $ 2 $-degree-of-freedom ($ 2 $-DOF) robot manipulator, which is one of the most representative examples of Euler-Lagrange equations.

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.

This paper investigates the problem of attitude tracking in quadrotor unmanned aerial vehicles (UAVs) using a model-free online learning control (MFOLC) scheme. The attitude system, which is represented by unit quaternions, is considered in the presence of uncertain and unknown inertia parameters, time-varying external disturbances, and angular velocity measurement noise. A computationally low-cost control scheme consisting of a model-free baseline controller and a module capable of learning from previous control input is designed. The proposed controller does not require precise inertial parameters and does not involve feedforward terms that use these parameters and true system states. This ensures that the approach can protect the control effort from sensor noise as well as parameter uncertainty. We also show that all the signals in the closed-loop system are uniformly ultimately bounded. Comparative simulations and real-world experiments are conducted for validation, which demonstrate the effectiveness and fine performance of the proposed scheme.

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.

Microrobotics has opened new horizons for various applications, especially in medicine. However, it also witnessed challenges in achieving maximum optimal performance. One key challenge is the intelligent, autonomous, and precise navigation control of microrobots in fluid environments. The intelligence and autonomy in microrobot control, without the need for prior knowledge of the entire system, can offer significant opportunities in scenarios where their models are unavailable. In this study, two control systems based on model-free deep reinforcement learning were implemented to control the movement of a disk-shaped magnetic microrobot in a real-world environment. The training and results of an off-policy SAC algorithm and an on-policy TRPO algorithm revealed that the microrobot successfully learned the optimal path to reach random target positions. During training, the TRPO exhibited a higher sample efficiency and greater stability. The TRPO and SAC showed 100% and 97.5% success rates in reaching the targets in the evaluation phase, respectively. These findings offer basic insights into achieving intelligent and autonomous navigation control for microrobots to advance their capabilities for various applications.

This paper proposes a noniterative direct data-driven gain-scheduled control. Gain-scheduled proportional–integral–derivative (PID) control is one of the most popular approaches for nonlinear systems. However, compared to fixed PID, gain-scheduled PID has considerably more scheduler parameters and requires time to tune them, which is the problem in conventional methods that include hand-tuning and model-based control design. To solve these problems, a noniterative data-driven tuning method for a gain-scheduled controller with polynomials via tuning approach using a fictitious reference signal is proposed. The proposed method enables tuning of the parameters from one-shot data obtained from a test without the system model to be controlled. To verify this method, the numerical simulation for two types of nonlinear systems is conducted. Therefore, the proposed method enables the parameters of the gain-scheduled controller to realize high tracking performance.

Most of the control strategies require a mathematical model or reasonable knowledge that is difficult to obtain for complex systems. Model-free control is a good alternative to avoid the difficulties and complex modeling procedures, especially if the knowledge about the system is insufficient. This paper presents a new control scheme completely independent of the system model. The proposed scheme combines sliding mode control (SMC) with intelligent proportional integral derivative (iPID) control based on a local model and extended state observer (ESO). Although the iPID control makes the proposed method model-free, it cannot guarantee that the tracking errors converge to zero asymptotically except the system is in a steady-state regime. Therefore, the SMC is added to the control scheme to ensure the convergence by minimizing the estimation errors of the observer. The proposed iPIDSMC controller is tested in the presence of different parameter variations and external disturbances on an inverted pendulum - cart (IPC), which is a highly unstable underactuated system with nonlinear coupled dynamics. The proposed controller is compared with the PID, iPID and Hierarchical Sliding Mode Control (HSMC) for a clearer evaluation. Simulation results showed that the proposed controller is extremely insensitive to parameter variations, matched and mismatched disturbances and the control signal of the proposed method is chattering-free, even though it is based on a discontinuous control action.