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ABSTRACT The current contribution introduces a nonsingular fixed‐time sliding mode control (SMC) scheme for position and velocity tracking of robot manipulators. The approach avoids singularities by introducing a new sliding surface with the special attribute that the exponent employed to achieve fixed time convergence depends on the tracking error and is smaller than one except when the error is exactly zero, whereas the exponent becomes one at zero, which makes the derivative at zero to be well defined. A new theoretical result has been introduced in the form of a lemma to prove this innovative property. Furthermore, model uncertainties are handled by means of a time‐varying gain given by a polynomial of the powers of the norms of the tracking and velocity errors. The fixed‐time convergence is proven employing Lyapunov theory, and the result holds globally. Simulation outcomes confirm the developed theory, and the advantages of the proposed scheme are shown qualitatively by comparing its performance with well‐known equivalent control schemes. [ABSTRACT FROM AUTHOR]

The control of bilateral teleoperation systems with time-varying delays is a challenging problem that is frequently addressed with advanced control techniques. Widely known controllers, like Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID), are seldom employed independently and are typically combined with other approaches, or at least with gravity compensation. This work aims to address a gap in the analysis of bilateral systems by demonstrating that the standard PID control law alone can achieve regulation in these systems when a human operator moves any of the robots while exchanging delayed positions. Experimental results are consistent with the theoretical analysis. Additionally, to illustrate the high degree of robustness of the standard PID, further experiments are conducted in constrained motion, both with and without force feedback. [ABSTRACT FROM AUTHOR]

This article develops a predefined‐time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined‐time stabilization problem of robotic manipulator system, a predefined‐time sliding mode surface is proposed, ensuring system states converge to origin within a predefined‐time once sliding mode surface is attained. Compared to conventional fixed‐time and finite‐time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined‐time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot's adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot's operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm's efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot's adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics. [ABSTRACT FROM AUTHOR]

Although widely used in industrial applications, strong nonlinearity and coupling, high computational complexity prevent high precision tracking control of manipulator. In this paper, to overcome the rely on system model and achieve prescribed convergence, a novel hybrid observer‐based model‐free adaptive high‐order fast terminal sliding model control scheme (HO‐MHTSMC) with prescribed performance is proposed for trajectory tracking control of robot manipulators in the existence of friction and external disturbance. The ultra‐local model is used to approximate the original complex system in a model free form in a short sliding time window, which avoid the accurate modeling of the manipulator system. To compensate for the lumped uncertainties, a hybrid observer based on adaptive time‐delay estimation and adaptive second order sliding mode observer (SOSM) is proposed to achieve finite‐time observation and zero estimation error. Besides, a transformation using prescribed performance function is applied to the system to ensure the transient and steady‐state performance of the trajectory tracking in joint space. Furthermore, a high‐order fast terminal sliding mode control algorithm with backstepping control strategy is used to stabilize the whole system and reduce the chattering problem in conventional sliding mode control. The stability analysis of the system is provided by Lyapunov theorem. Finally, numerical study and co‐simulations show that the proposed control scheme has better performance in tracking accuracy and robustness compared with conventional control schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a nonfragile prescribed performance control (PPC) scheme for robot manipulators with actuator faults, which can address the fragility problem of the existing prescribed performance control and guarantee the transient steady-state performance of the tracking error. Firstly, a novel performance function with small overshoot, finite time convergence, and an adjustment term is proposed. Its adjustment term can adjust the constraint range when the error approaches the boundary, thus avoiding the control singularity problem. Then, error transformation is employed to convert the tracking problem with performance constraints into a stabilization problem for the new system. On this basis, fuzzy neural networks are utilized to address the model uncertainty. Stability analysis of the designed controller is conducted utilizing the Lyapunov method. Finally, numerical simulations are employed to verify the effectiveness and superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents a simple approach for designing a linear adaptive controller, employing the model reference systems concept, to enable the tracking of uncertain robotic manipulators under the influence of input disturbances. The combined impact of model uncertainties and matched disturbances on the robot's behavior is considered as a total matched disturbance, attributed to a double integral system. Subsequently, a novel linear disturbance estimator, augmented by a feed‐forward correction term, is employed to estimate this lumped disturbance within the double integrator system. As a result of this procedure, the requisite adaptive law, based on the model reference system, is formulated for the nominal control parameter instead of arbitrary free‐parameter selections. The effectiveness of the proposed approach is theoretically justified and further supported by its application in the analysis of an active magnetic bearing system. [ABSTRACT FROM AUTHOR]

For a class of nonlinear systems with uncertain parameters, this paper proposes a novel data-driven adaptive control method. This method utilizes a designed parameter estimator to steer the closed-loop system to the predefined ideal system on the manifold. It achieves finite-time convergence of the system through a terminal sliding mode controller. Based on the data-driven concept, the parameter regression matrix is expanded to acquire the unknown parameters of the system indirectly. By introducing a perturbation matrix, the issue that the expanded parameter regression matrix needs to satisfy certain excitation conditions to be full-rank is overcome, and an algebraic equation-based parameter estimator is constructed to achieve an arbitrary small convergence of the parameter estimation error. A global non-singular fast terminal sliding mode controller is designed for the system on the manifold, achieving finite-time convergence of the system. The stability of the closed-loop system is verified through Lyapunov-based stability analysis. As an application, the effectiveness and superiority of the proposed method are validated through numerical simulations of Euler-Lagrange systems with unknown inertia parameters. [ABSTRACT FROM AUTHOR]

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms. [ABSTRACT FROM AUTHOR]

This study addresses the tracking problem for robot arms with parametric uncertainties in the model, position measurements available, and no velocity measurements. A filtering technique based on position information is used to compensate for the unmeasured velocity information. A linear filter-based controller is designed to eliminate the controller's dependence on velocity measurements by utilizing adaptive neural networks for model uncertainties. The stability of the closed-loop system is guaranteed by the Lyapunov method. To demonstrate the performance of the proposed controller, numerical simulation results are generated using a two-degree-of-freedom robot arm model and compared comparatively with adaptive fuzzy logic method. [ABSTRACT FROM AUTHOR]

This paper presents the hybrid force control for a robot manipulator, which separates position and force control axes in the Cartesian space to perform force and position control separately by a selection matrix. Since the coupling dynamics with uncertainties in the Cartesian space causes poor tracking performance, decoupling and compensating for the uncertain dynamics of position and force-controlled axis are required. A time-delayed control method is combined with the hybrid force control to achieve independent axis control. Stability analysis of the combined control scheme is derived to obtain the bound of the Cartesian inertial value in terms of a joint inertia matrix and Jacobian matrix by decomposing dynamics into position and force-controlled dynamics in the Cartesian space. Force tracking control performances of a robot manipulator are simulated to validate the proposed control method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel adaptive law that uses a quasi-convex function and a novel sliding variable in an adaptive sliding mode control (ASMC) scheme for robot manipulators. Since the dynamic equations of robot manipulators inevitably include model uncertainties and disturbances, time-delay estimation (TDE) errors occur when using the time-delay control (TDC) approach. Further, the ASMC method used to compensate for TDE errors naturally causes a chattering phenomenon. To improve tracking performance while reducing or maintaining chattering, this paper proposes an adaptive law based on a quasi-convex function that is convex at the origin and concave at the gain switching point, respectively. We also adopt a novel sliding variable that uses previously sampled tracking errors and their time derivatives. Further, this paper proves that the sliding variable of the robot manipulator controlled by the proposed ASMC satisfies uniformly ultimately bounded stability. The simulation and experimental results illustrate the effectiveness of the proposed methods in terms of tracking performance. [ABSTRACT FROM AUTHOR]

Trajectory interception is a critical synchronization element in the transportation and manufacturing sectors using robotic platforms. This is usually performed by matching the position and velocity of a target object with the position and velocity of the robot interceptor. However, the synchronization task is exasperated by (i) the proper gain tuning of the controller, (ii) the dynamic response of the robotic platform, (iii) the velocity constraints in the actuators, and (iv) the trajectory profile exhibited by the moving object. This means that the interception time is not controlled, which is critical for energy optimization, resources, and production. This paper proposes a prescribed time trajectory interception algorithm for robot manipulators. The approach uses the finite-time convergence properties of sliding mode control combined with a terminal attractor based on a time base generator. The combined approach guarantees trajectory interception in a prescribed time with robust properties. Simulation studies are conducted using the first three degrees of freedom (DOFs) of a RV-M1 robot under single- and multi-object interception tasks. The results verify the effectiveness of the proposed methodology under different hyperparameter configurations. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this paper is to propose a method involving vision, manipulation, and learning techniques to effectively address the task of bottle opening. The process begins with the acquisition of bottle and cap positions using an RGB-D camera and computer vision. Subsequently, the robot picks the bottle with one gripper and grips the cap with the other, each by planning safe trajectories. Then, the opening procedure is executed via a position and force control scheme that ensures both grippers follow the unscrewing path defined by the cap thread. Within the control loop, force sensor information is employed to control the vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal angle increments for rotation. The results demonstrate the successful training of the learning agent. The experiments confirm the effectiveness of the proposed method in bottle opening with the TIAGo++ robot, showcasing the practical viability of the approach. [ABSTRACT FROM AUTHOR]

In this paper, a novel recursive and efficient algorithm for real-time implementation of the adaptive and passivity-based controllers in model-based control of robot manipulators is proposed. Many of the previous methods on these topics involve the computation of the regressor matrix explicitly or non-recursive computations, which remains as the main challenge in practical applications. The proposed method achieves a compact and fully recursive reformulation without computing the regressor matrix or its elements. This paper is based on a comprehensive literature review of the previously proposed methods, presented in a unified mathematical framework suitable for understanding the fundamentals and making comparisons. The considered methods are implemented on several processors and their performances are compared in terms of real-time computational efficiency. Computational results show that the proposed Adaptive Newton-Euler Algorithm significantly reduces the computation time of the control law per cycle time in the implementation of adaptive control laws. In addition, using the dynamic simulation of an industrial robot with 6-DoF, trajectory tracking performances of the adaptive controllers are compared with those of non-adaptive control methods where dynamic parameters are assumed to be known. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses a new robust control scheme for tracking control of the robot manipulators. This algorithm employs time delay estimation (TDE) technique in combination with backstepping control strategy. The suggested control scheme has no dependency on the robot dynamic model and knowing the bound of the inertia matrix is enough to develop this controller. To develop the idea, at first the boundedness of the TDE errors is analyzed by Lyapunov‐Krasovskii approach. Next, the proposed TDE based adaptive backstepping nonsingular terminal sliding mode control (ABNTSMC) algorithm is developed. In this way, a novel adaptive‐robust term is employed to defeat the chattering phenomenon. Moreover, the closed‐loop stability of the system is proven through Lyapunov second approach. Fast transient response, supreme robustness, and chattering‐free as the premier specifications are gained employing the proposed control algorithm. The efficiency of the suggested control scheme is illustrated through some simulations on an industrial robot and the results are compared with some other control algorithms. Finally, the practical effectiveness of the proposed TDE based control algorithm is verified through some experiments on a parallelogram robot manipulator. [ABSTRACT FROM AUTHOR]

This article presents a data-driven control application to robot manipulation for implementing the time-delayed control (TDC) algorithm. TDC scheme uses the previous information to cancel out all the dynamics except the inertial torque in robot manipulators. The accuracy of estimating the inertia matrix plays an important role in control performance as well as the stability of TDC. Necessary information for the time-delayed control is inertia and acceleration signals. Since selecting the constant inertia matrix is simple but concerned with the poor performance, better estimation is required. Based on the input and output data of a robot manipulator, necessary models are obtained by a recursive least squares (RLS) algorithm and those models are used for estimating acceleration signals by designing a state observer (SOB). Here the models of a robot arm are decoupled, linearized, and identified by RLS algorithm and the joint acceleration signals are identified by a state observer in on-line fashion. Combining RLS, SOB, and TDC yields RST scheme for a robot manipulator to improve the tracking control performance by providing solutions for TDC problems. Tracking control performances of a mobile manipulator by the RST scheme are empirically tested. [ABSTRACT FROM AUTHOR]

This study addresses the problem of hand–eye calibration in robotic systems by developing Continual Learning (CL)-based approaches. Traditionally, robots require explicit models to transfer knowledge from camera observations to their hands or base. However, this poses limitations, as the hand–eye calibration parameters are typically valid only for the current camera configuration. We, therefore, propose a flexible and autonomous hand–eye calibration system that can adapt to changes in camera pose over time. Three CL-based approaches are introduced: the naive CL approach, the reservoir rehearsal approach, and the hybrid approach combining reservoir sampling with new data evaluation. The naive CL approach suffers from catastrophic forgetting, while the reservoir rehearsal approach mitigates this issue by sampling uniformly from past data. The hybrid approach further enhances performance by incorporating reservoir sampling and assessing new data for novelty. Experiments conducted in simulated and real-world environments demonstrate that the CL-based approaches, except for the naive approach, achieve competitive performance compared to traditional batch learning-based methods. This suggests that treating hand–eye calibration as a time sequence problem enables the extension of the learned space without complete retraining. The adaptability of the CL-based approaches facilitates accommodating changes in camera pose, leading to an improved hand–eye calibration system. [ABSTRACT FROM AUTHOR]

The partial potential energy shaping control of fully actuated torque–driven robot manipulators in joint space is addressed in this paper. In contrast to the well-known potential energy shaping control of robot manipulators–which achieves global joint position regulation–here the term partial means to cancel out the natural potential energy at the joints selected by the user via the feedback control law. This formulation is useful when the robot joints are intended to track a desired time-varying trajectory that has joints with null potential energy. To the best of the authors' knowledge, this is the first time that a formal analysis is presented on joint position tracking of robot manipulators by means of an adequate kinetic energy shaping plus total damping injection with partial potential energy shaping. The proposed controller is designed via an energy shaping plus damping injection approach, and the closed-loop system analysis is carried out via the Lyapunov's theory and LaSalle's theorem. Real-time experimental results on a manipulator arm model of two degrees of freedom illustrate the main results. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of two methods for planning and coordinating the movement of robot manipulators in dynamic environments: a neural network-based approach for solving dynamic production scenarios and the rapidly exploring random trees algorithm. The study aims to enhance the trajectory planning of robot manipulators by leveraging the strengths of intelligent systems. The neural network method is designed to perceive the environment, generate accurate control commands, and adapt to changing conditions in real-time. The paper the processes involved in environmental analysis, collision avoidance, and control signal generation for actuators, with an emphasis on the neural network architecture tailored for these tasks. The results demonstrate that the neural network approach offers significant improvements in adaptability and efficiency, providing a robust solution for optimizing automated processes in dynamic production environments. [ABSTRACT FROM AUTHOR]

This paper provides a new sliding mode control (SMC) approach, by which both the nominal and robust stability associated with a trajectory tracking problem for an uncertain robot manipulator are achieved. More precisely, the new control law consists of linear and nonlinear functions of tracking errors, in which the former is for the nominal stability and the latter is to ensure the robust stability of the resulting closed‐loop systems. The nonlinear function can be interpreted as an extended version of conventional SMC approach, and the reaching phase corresponding to a pregiven sliding surface is shown to be completed in a finite time; the tracking errors arrive at the sliding surface in a finite time and do not deviate from it after the arrival. In the sliding phase, the tracking errors are also ensured to converge to the origin. Finally, some simulation results are given to demonstrate both the theoretical validity and practical effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method. [ABSTRACT FROM AUTHOR]

The paper investigates a modified adaptive super-twisting sliding mode control (ASTSMC) for robotic manipulators with input saturation. To avoid singular perturbation while increasing the convergence rate, a modified sliding mode surface (SMS) is developed in this method. Using the proposed SMS, an ASTSMC is developed for robot manipulators, which not only achieves strong robustness but also ensures finite-time convergence. The boundary of lumped uncertainties cannot be easily obtained. A modified adaptive law is developed such that the boundaries of time-varying disturbance and its derivative are not required. Considering input saturation in practical cases, an ASTSMC with saturation compensation is proposed to reduce the effect of input saturation on tracking performances of robot manipulators. The finite-time convergence of the proposed scheme is analyzed. Through comparative simulations against two other sliding mode control schemes, the proposed method has been validated to possess strong adaptability, effectively adjusting control gains; simultaneously, it demonstrates robustness against disturbances and uncertainties. [ABSTRACT FROM AUTHOR]

This paper is devoted to proposing a feedback trajectory tracking control method for hydraulically driven rock drilling robotic manipulators. The rock drilling robot has a large-scale long boom and multiple joints (6R-2P), so the deformation of the arm may not be neglected. A kinematic model with the flexible deformation of the boom is developed by adding a modified coordinate transformation matrix to the general model. Each joint motion is determined by solving the inverse kinematic model in an optimization algorithm, which aims at minimizing the positional error and smoothing the motion. A dual-loop feedback controller is proposed to track the end-effector's position and orientation. Based on the model predictive control (MPC) method, the inner-loop controller controls the cylinder motion by controlling the cylinder motion. The outer-loop controller uses the fuzzy-PID control to correct the position error which is caused by the inner-loop controller. Several reference trajectories are designed to validate the effectiveness of the proposed dual-loop control strategy, which demonstrates it has higher accuracy and lower control fluctuation than single-loop and other dual-loop controllers. [ABSTRACT FROM AUTHOR]

In this paper, a robust dynamic slip mode controller for an electrical robot manipulator is presented. The control law calculates the motor voltage based on the voltage control strategy. Uncertainties are estimated using the Fourier series expansion and the cutting error is compensated. Fourier coefficients are adjusted based on stability analysis. Also in this paper is the design of a robust controller using a new adaptive Fourier series extension. Compared to previous related works based on the Fourier series expansion, the advantage of this paper is that it provides a matching law for the main frequency of the Fourier series expansion and thus eliminates the need for trial and error in its regulation. A case study of a Scara robot powered by DC magnet electric motors. The effect of uncertainty estimation based on the Fourier series expansion is studied instead of using the sign function. The proposed method is also compared with Legendre polynomials. The simulation results confirm the robust and satisfactory performance of the proposed controller.

In this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values ​​of the parameters in the torque controllers have not converged to their true values ​​due to the large modelless dynamics, while they have converged to their true values ​​in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed.

The control of bilateral teleoperation systems with time-varying delays is a challenging problem that is frequently addressed with advanced control techniques. Widely known controllers, like Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID), are seldom employed independently and are typically combined with other approaches, or at least with gravity compensation. This work aims to address a gap in the analysis of bilateral systems by demonstrating that the standard PID control law alone can achieve regulation in these systems when a human operator moves any of the robots while exchanging delayed positions. Experimental results are consistent with the theoretical analysis. Additionally, to illustrate the high degree of robustness of the standard PID, further experiments are conducted in constrained motion, both with and without force feedback.

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

In order to address the fragility issues associated with the current prescribed performance control (PPC) strategy and ensure both transient and steady-state performance of the tracking error, a non-fragility prescribed performance control scheme is proposed. A non-fragile prescribed performance control method for robotic systems with model uncertainties and unknown disturbances is developed. This method not only addresses the inherent vulnerability defects of the existing prescribed performance control but also effectively reduces the computational complexity of the controller. Firstly, addressing the fragility issues of existing PPC, a new non-fragile prescribed performance control strategy is proposed. To address the fragile issue with the current PPC, the shift function is employed to handle the tracking error. Based on the non-fragile PPC mentioned above, a new prescribed performance controller is designed without the requirement for approximation or estimation. This effectively reduces the complexity of controller design. At last, the feasibility of achieving non-fragile prescribed performance is verified through stability analysis, and the superiority of the designed controller is confirmed through simulation comparisons. The results show that the designed controller effectively resolves the control singularity issue arising from the inherent limitations of the PPC. [ABSTRACT FROM AUTHOR]

This paper investigates the high-precision model-free control of robot manipulators. To this end, a model-free robust prescribed performance controller with an improved fast nonsingular terminal sliding mode surface (IFNTSM) and unknown system dynamics estimator (USDE) has been designed. An USDE method is employed to estimate model informations and further to achieve model-free control, which can avoid complex mathematical model calculation. Compared with some other model-free control methods like time-delay control (TDC) and neural-networks control (NNC), the USED does not require acceleration signal and is easy to implement. Then the prescribed performance control (PPC) has been used to limit error trajectory, which means the error can be pre-limit in a constraint band. A new transform function (TF) is designed for PPC, it has unlimited domain and can still maintain stability although tracking error will exceed PPC boundary sometimes, but the PPC with traditional TF will crash in this case. This is a great improvement for the stability of system compared with traditional TF. An improved fast nonsingular terminal sliding mode surface (IFNTSM) with a new adaptive law is proposed to accelerate convergence rate and improve steady-state accuracy on the sliding manifold. Finally, a practical finite-time controller (PFTC) has been constructed to drive sliding variable to a set centered on zero within a finite time, which means the convergence time can be calculated depending on the initial state. The transient response time can be shorten compared with traditional asymptotic stable. Abundant simulations and experimental results also verified the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This work studies to improve the efficiency of the tracking control system based on the nonsingular terminal sliding mode technique for the robot manipulators in case of sudden faults. In the proposed control strategy, the adaptability is first enhanced by the self-updating algorithms for all control gains of the main sliding mode controller. Next, the unknown dynamics and abrupt faults are estimated to ensure the robustness and tracking performances of the control system. In addition, the proposed control method has more advantages in addressing the inevitable updating/estimating errors and guaranteeing the continuity and smoothness of control signals through an added robust controller. And based on the Lyapunov theorem, the designed adaptive updating/estimating control strategy has guaranteed for the stability, robustness, and finite time convergence of the control system. In the end, the improved features of the proposed control method are verified by the compared numerical simulation results. [ABSTRACT FROM AUTHOR]

Adaptive control of robot manipulators has been widely used in the last decades. The main control goal is usually trajectory tracking, but exact parameter estimation is always desirable. In the recent years, the so called dynamic regressor extension and mixing (DREM) procedure was developed to provide an alternative in the design of adaptive laws with more relaxed parameter convergence conditions than the Persistency of Excitation (PE). This article proposes an innovative DREM based adaptive scheme with the ability of increasing a minimal amount of excitation to the level necessary to obtain exact parameter estimation in finite time. Furthermore, a new robust control law is proposed to guarantee trajectory tracking even if exact parameter estimation does not take place. Simulation results are in good accordance with the developed theory. [ABSTRACT FROM AUTHOR]

This paper presents a control method for a twolink industrial robot manipulator system that uses Fuzzy Neural Networks (FNNs) based on Sliding Mode Control (SMC) to investigate joint position control for periodic motion and predefined trajectory tracking control. The proposed control scheme addresses the challenges of designing a suitable control system that can achieve the required approximation errors while ensuring the stability and robustness of the control system in the face of joint friction forces, parameter variations, and external disturbances. The control scheme uses four layers of FNNs to approximate nonlinear robot dynamics and remove chattering control efforts in the SMC system. The adaptive turning algorithms of network parameters are derived using a projection algorithm and the Lyapunov stability theorem. The proposed control scheme guarantees global stability and robustness of the control system, and position is proven. Simulation and experiment results from a two-link IRM in an electric power substation are presented in comparison to PID and AF control to demonstrate the superior tracking precision and robustness of the proposed intelligent control scheme. [ABSTRACT FROM AUTHOR]

Solving inverse kinematic (IK) of general robot manipulators remains significant challenge in current industrial manufacturing, particularly in human–robot collaborative scenarios. Most current approaches employ numerical, analytical, or machine learning methods to solve IK. However, accurately determining the end-effector (EE) position, solving complexity, and handling multiple solutions are unresolved challenges in these existing methods. In this paper, we propose a hybrid method that combines forward and backward reaching inverse kinematics (FABRIK) with a custom artificial neural network (ANN) to solve IK for a broad range of serial robot manipulators. The results demonstrate that the hybrid method yields a unique solution and achieves a lower position error (up to 0.003 in) compared to a standard ANN implementation. Furthermore, compared to the numerical method (FABRIK and Jacobian), the hybrid approach offers a more versatile framework for solving IK, resulting in superior overall performance in terms of solving complexity, computational efficiency, and accuracy among the three methods. [ABSTRACT FROM AUTHOR]

This paper presents an advanced control strategy based on Fractional-Order Sliding Mode Control (FO-SMC), which introduces a robust solution to significantly improve the reliability of robotic manipulator systems and increase its control performance. The proposed FO-SMC strategy includes a two-key term-based Fractional Sliding Function (FSF) that presents the main contribution of this work. Additionally, a fractional-order-based Lyapunov stability analysis is developed for a class of nonlinear systems to guarantee the asymptotic stability of the closed loop system. Four FSF-based versions of the designed FO-SMC are studied and discussed. Various scenarios of the proposed control strategy are tested on a 3-degree-of-freedom SCARA robotic arm and compared to recent FO-SMC works, demonstrating the effectiveness of the new proposed control strategy to (i) ensure the asymptotic stability, (ii) achieve a smooth start-up, (iii) cancel the static error, giving a good tracking trajectory, and (iv) reduce the control torques, yielding a consumed energy minimization. [ABSTRACT FROM AUTHOR]

This article proposes a singularity-free fixed-time neuro-adaptive control strategy for robot manipulators, with the goal of addressing trajectory-tracking challenges presented by model uncertainties, external disturbances, and input saturation. To mitigate the impact of input saturation, an auxiliary system is introduced. Combining the backstepping technique, a fixed-time neuro-adaptive controller is designed to ensure that tracking errors converge within a small region around the origin within a fixed time, with the upper bound of convergence time being independent of initial conditions. Notably, the direct avoidance of singularity is achieved by constructing quadratic-fraction functions in both the virtual controller and the actual controller, eliminating the need for filters or piecewise continuous functions. This simplifies and streamlines the stability analysis process. To validate the effectiveness of this strategy, numerical simulations are conducted.

The challenge in controlling a manipulator robot is to model the system to obtain an efficient control system design. One approach that can be used to model the dynamics of a manipulator robot is data-driven modeling. However, in its implementation, data-driven modeling is highly sensitive to sensor noise, which significantly affects the accuracy of the system identification. In addition, the existing approach yields only a generalized form of the differential equation for each joint, which has not been divided into inertial, Coriolis, and gravitational variables that can be used for other purposes. In this study, a LASSO model selection criteria with a variable segregation algorithm (LMSCVS) is proposed to derive the dynamic equation of a 3-DoF manipulator robot, segregating the generalized form variables into Coriolis and centrifugal, inertia, and gravitational variables. Additionally, a Dynamic Expression Nonlinearization (DEx-N) algorithm is introduced to generate nonlinear candidates more efficiently to express the dynamics of the robot manipulator. The experimental results on the ROB3 hardware demonstrate that the proposed method successfully discovers mathematical equations, resulting in higher accuracy and sparsity compared to the previous method. The processing time of the proposed method is also significantly faster. Based on these results, the proposed method has a better performance in identifying real systems that usually have noise in the sensor data and in discovering the equation of robot manipulator dynamics for broader purposes.

A novel adaptive robust hybrid force/position control (ARHFPC) strategy is proposed for robot manipulator systems subject to dynamic uncertainties and unknown matched and mismatched disturbances under input saturation. First, the position controller is designed based on the backstepping approach. The first-order low-pass filter and the auxiliary dynamic system are synthesized into the controller to overcome the complex derivative operation of virtual control and handle the effect of input saturation, respectively. Radial basis function neural networks (RBFNNs) are utilized to approximate the dynamic uncertainties and matched disturbances. Then, a disturbance observer is designed for the mismatched disturbances. To enhance control accuracy of the interaction force between the end-effector and the external environment, a fuzzy proportional-integral (FPI) control scheme is presented. Theoretical analysis proves that all signals in the closed-loop control system of robot manipulators are locally uniformly ultimately bounded (UUB). Simulation results demonstrate the effectiveness and robustness of the proposed control scheme.

Trajectory tracking control for robot manipulators is an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems. Furthermore, the tracking control performance for robot manipulators is greatly affected by input constraints and external disturbances. This paper proposes a novel $ H_{\infty } $ optimal controller for robot manipulators with asymmetric input constraints and external disturbances based on adaptive dynamic programming (ADP). Firstly, a strict feedback nonlinear system is used to represent the robot manipulator dynamics, and then a feedforward controller is designed to construct the tracking error dynamics. Secondly, a value function is introduced, and the Hamilton-Jacobi-Isaacs equation is made and approximated online by the principle of adaptive dynamic programming. Thirdly, the optimal control law and disturbance compensation law are determined. The stability and convergence of the proposed algorithm are analyzed by the Lyapunov technique. Finally, the controller performance is verified through simulation and experimental results with STM32F407 of STMicroelectronics.

The global demand for button mushrooms has surged in recent years, driven by their health benefits, creating a significant challenge for the mushroom industry in meeting this increasing demand. The increasing reliance on human labor, which is becoming unsustainable due to labor shortages and rising wage costs, highlights the urgent need for automated harvesting solutions. This review examines the integration of automated systems in button mushroom harvesting, delving into the key components such as robots, mechanisms, machine elements, programming, and algorithms. It offers a thorough analysis of the performance, design, operational mechanisms, and advantages and limitations of robotic systems, comparing the different methods employed in automated harvesting. This paper compares the performance of all the mushroom harvesters, including the commercially available ones with manual harvesting, and identifies their potential and limitations. The commercial harvesters are shown to pick 2000 mushrooms per hour on average, which is similar to how much a skilled worker picks at the same time. However, commercial automation harvesting has a relatively low success rate, high initial cost, high operating cost, and energy consumption, identifying areas for future research and challenges. This paper serves as a valuable resource for researchers and industry professionals striving to advance automated harvesting technology and improve its efficiency in meeting the rising demand for button mushrooms.

This paper introduces a novel approach to robotic assistance in bottle opening using the dual-arm robot TIAGo++. The solution enhances accessibility by addressing the needs of individuals with injuries or disabilities who may require help with common manipulation tasks. The aim of this paper is to propose a method involving vision, manipulation, and learning techniques to effectively address the task of bottle opening. The process begins with the acquisition of bottle and cap positions using an RGB-D camera and computer vision. Subsequently, the robot picks the bottle with one gripper and grips the cap with the other, each by planning safe trajectories. Then, the opening procedure is executed via a position and force control scheme that ensures both grippers follow the unscrewing path defined by the cap thread. Within the control loop, force sensor information is employed to control the vertical axis movements, while gripper rotation control is achieved through a Deep Reinforcement Learning (DRL) algorithm trained to determine the optimal angle increments for rotation. The results demonstrate the successful training of the learning agent. The experiments confirm the effectiveness of the proposed method in bottle opening with the TIAGo++ robot, showcasing the practical viability of the approach.

Trajectory interception is a critical synchronization element in the transportation and manufacturing sectors using robotic platforms. This is usually performed by matching the position and velocity of a target object with the position and velocity of the robot interceptor. However, the synchronization task is exasperated by (i) the proper gain tuning of the controller, (ii) the dynamic response of the robotic platform, (iii) the velocity constraints in the actuators, and (iv) the trajectory profile exhibited by the moving object. This means that the interception time is not controlled, which is critical for energy optimization, resources, and production. This paper proposes a prescribed time trajectory interception algorithm for robot manipulators. The approach uses the finite-time convergence properties of sliding mode control combined with a terminal attractor based on a time base generator. The combined approach guarantees trajectory interception in a prescribed time with robust properties. Simulation studies are conducted using the first three degrees of freedom (DOFs) of a RV-M1 robot under single- and multi-object interception tasks. The results verify the effectiveness of the proposed methodology under different hyperparameter configurations.

Summary: This paper presents a controller for joint position tracking of torque‐driven robot manipulators affected by torque disturbances. In particular, the proposed approach allows concluding global exponential stability (GES) of the state‐space origin of the closed‐loop system when constant disturbances affect the robot dynamics. Besides, when time‐varying disturbances are presented, the trajectories of the closed‐loop system are proven to be bounded as result of the input‐to‐state stability. The main contribution is the design of the control law and a nonlinear observer based on an alternative energy shaping approach. The nonlinear observer is designed to compute and compensate for unknown disturbances, whether they are constant or time‐varying. Furthermore, a detailed stability analysis of the closed‐loop system based on Lyapunov theory and input‐to‐state stability is presented. As far as the authors know, this is the first energy‐shaping controller for trajectory tracking control of robot manipulators affected by constant disturbances that achieves global exponential stability. Real‐time experiments on a manipulator arm of two degrees‐of‐freedom illustrate the performance of the proposed controller. [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]

Summary: Basic features such as convergence time and speed, number‐action control coefficients, free chattering, and proof of stability are significant in the design process and sliding mode control (SMC) efficiency. In this article, we propose an adaptive fuzzy global fast terminal SMC (AFGFTSMC) to handle the mentioned features in the task space control of the robot manipulator in the presence of dynamic and kinematic uncertainties. First, perturbed joint space dynamic equations of the system are transferred to task space, and a broad range of uncertainties are considered there. Then, a global fast terminal SMC (GFTSMC) is proposed for robot manipulators in task space, in which a flexible sliding surface improves the convergence time. Next, to have an intelligent adjustment of the sliding surface coefficients, which leads to a much faster convergence rate, a fuzzy approximator with just seven fuzzy rules is presented. In the following, to access the boundaries of the existing uncertainties, an adaptive fuzzy approximator is proposed, which has five fuzzy rules and only one adaptive law, increases the system's robustness, and eliminates the effect of chattering. Mathematical proof shows that the task space closed‐loop control system under the proposed AFGFTSMC and in the presence of dynamic and kinematic uncertainties has a finite‐time global asymptotic stability. The theoretical evidence and simulation results, which are conducted on a 2‐link robot manipulator, confirm the good efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper proposes a fixed-time tracking control for robot manipulators in the presence of parametric uncertainties and disturbances. An auxiliary function is first proposed for constructing a fixed-time sliding manifold. Benefited from this fixed-time sliding manifold, a singularity-free robust control is proposed to evade the effects of algebraic loop problem of the commonly-used sliding mode controls (SMC). The key advantages of the proposed approach are: (i) exact fixed-time stability featuring the convergence time does not relate to the initial conditions and is acquired in advance; (ii) the singularity and algebraic loop problems are eliminated completely; (iii) a simple and intuitive control structure is used for easy implementation of trajectory tracking control for uncertain robot manipulators with faster transient and higher steady-state precision. Simulations and experimental comparisons validate the improved tracking performance of the proposed approach. • Propose a non-singular terminal sliding mode control with fixed-time convergence. • Prove exact fixed-time tracking stability. • Features include exact fixed-time stability with simple structure. • Demonstrate the improved performance. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of bilinear force/position control (BFC) schemes for the guaranteed force tracking performance of a robot manipulator under unknown environment. Borrowing the concept of impedance force control and hybrid force control, BFC schemes are formulated by combining two force control algorithms. The proposed BFC scheme guarantees the desired force/position tracking performance for any environment with the help of a model-based control method by achieving independent axis control. Guaranteed force tracking control performances of three different bilinear functions are presented and analysed. Their performances are tested and compared without knowing any information on the environment such as position and stiffness a priori. Simulation studies of BFC tracking performances for a robot manipulator to follow the sinusoidal trajectory while regulating a desired force on the environment are performed to verify the practical force tracking control performance. [ABSTRACT FROM AUTHOR]