Adaptive Robot Control Research Articles

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Fuzzy Observer-Based Command Filtered Adaptive Control of Flexible Joint Robots With Time-Varying Output Constraints

Key technology of temporary support robot for rapid excavation of coal mine roadway

AbstractThe coal industry has long been troubled by the imbalance between mining and tunneling, and between excavating and support. The main cause of this problem is the inability to perform excavating and permanent support operations in parallel. Additionally, the limited space near the tunneling face hampers the efficiency of permanent support. Temporary support is an effective method to ensure the stability of the surrounding rock and expand the space for parallel operations between excavating and permanent support activities. This work provides a brief overview of the current research status on temporary support, emphasizing that the key to achieving safe, efficient, and rapid excavation lies in the development of temporary support robots. To meet the development needs of temporary support robots, three key technologies are proposed: the construction of a coupling model between the robot and the surrounding rock, spatial layout optimization of the rapid advancement system, and adaptive control of the robot. This work details the methods and approaches for constructing the coupling model, the elements of system spatial layout optimization, and the methods and strategies for the robot's adaptive control. Our team successfully tested the shield robot system at Xiaobaodang Mining Company, verifying the feasibility of these key technologies.

Adaptive optimal concurrent control for detumbling space non-cooperative target via multipoint repeated contact

Space debris generally exhibits complex tumbling motions, and its direct capture may cause damage to space manipulator and base spacecraft. Current detumbling strategies typically require continuous contact collisions with the target and do not take into account actuator limitations. Thus, this paper presents an adaptive optimal concurrent control of space free-floating multi-fingered robot (SFMR) for multipoint repeated contact detumbling of non-cooperative targets. Firstly, the multipoint repeated intermittent contact model between the SFMR and the target is established. Further, an optimal admittance control that considers manipulator actuator limits and target motion bounds is formulated to generate a compliant detumbling trajectory. Through transforming the state and input inequality constraints into extended dynamical subsystems and saturation functions, respectively, the optimal control problem (OCP) is transformed into a readily solvable equality constraint problem. Moreover, an enhanced nonsingular terminal sliding mode (ENTSM) control with radial basis function neural network (RBFNN) compensation is presented in the presence of lumped uncertainties and external disturbances, which achieves rapid finite-time convergence and low-chattering. The simulation results show that the proposed method can reduce the velocity of the space target effectively without causing base spacecraft interference while achieving accurate trajectory tracking.

A Novel Recursive Algorithm for the Implementation of Adaptive Robot Controllers

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.

High-Precision Adaptive Control of Cable-Driven Parallel Robots With Convergence Guarantee

High-Precision Adaptive Control of Cable-Driven Parallel Robots With Convergence Guarantee

Impedance Learning-Based Hybrid Adaptive Control of Upper Limb Rehabilitation Robots

This paper presents a hybrid adaptive control strategy for upper limb rehabilitation robots using impedance learning. The hybrid adaptation consists of a differential updating mechanism for the estimation of robotic modeling uncertainties and periodic adaptations for the online learning of time-varying impedance. The proposed hybrid adaptive controller guarantees asymptotical control stability and achieves variable impedance regulation for robots without interaction force measurements. According to Lyapunov’s theory, we proved that the proposed impedance learning controller guarantees the convergence of tracking errors and ensures the boundedness of the estimation errors of robotic uncertainties and impedance profiles. Simulations and experiments conducted on a parallel robot validated the effectiveness and the superiority of the proposed impedance learning controller in robot-assisted rehabilitation. The proposed hybrid adaptive control has potential applications in rehabilitation, exoskeletons, and some other repetitive interactive tasks.

Decentralized, norm-free, and adaptive event-triggered distributed control of nonholonomic mobile robots

The overarching contribution of this paper is a novel event-triggered distributed control architecture for multiagent systems that are composed of a team of nonholonomic mobile robots. Specifically, the robot dynamics are first feedback linearised to equivalently represent each robot with double integrator dynamics and the event-triggering law is then presented. This law is characterised by being decentralised (depending only on own signals of a robot), norm-free (independent of distances for reducing robot-to-robot data transmissions), and adaptive (estimating signals without using global information). Moreover, its system-theo- retical analysis is provided, which holds for both the sampled data exchange method and the solution-predictor curve-based data exchange method. In contrast to the well-studied sampled data exchange, the solution-predictor curve method further reduces robot-to-robot data transmissions by making each agent to store a generic form of this curve and exchange its parameters when an event occurs for approximating the solution trajectory of each robot. Finally, an illustrative numerical example is included to demonstrate the overall architecture.

Adaptive Neural Network Force Tracking Control of Flexible Joint Robot With an Uncertain Environment

Adaptive Neural Network Force Tracking Control of Flexible Joint Robot With an Uncertain Environment

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

Fixed-Time Adaptive Event-Triggered Guaranteed Performance Tracking Control of Nonholonomic Mobile Robots under Asymmetric State Constraints

A fixed-time adaptive guaranteed performance tracking control is investigated for a category of nonholonomic mobile robots (NMRs) under asymmetric state constraints. For the sake of favorable transient and steady-state properties of the system, a prescribed performance function (PPF) is introduced and a transform function is further constructed. Based on the backstepping technique, an asymmetric barrier Lyapunov function is formulated to ensure the tracking errors converge within a human-specified time. On the foundation of this, the occupation of communication channel is effectively reduced by assigning an event-triggered mechanism (ETM) with relative threshold to the process of controller design. By utilizing the proposed control strategy, the NMR is capable of implementing the enemy dislodging mission while the enemy can always be caught by the NMR and the collision would never be presented. Finally, two simulation experiments are given to verify the effectiveness of the proposed scheme.

MorAL: Learning Morphologically Adaptive Locomotion Controller for Quadrupedal Robots on Challenging Terrains

MorAL: Learning Morphologically Adaptive Locomotion Controller for Quadrupedal Robots on Challenging Terrains

Adaptive iterative learning control of soft robot for beating heart tracking

PurposeSoft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.Design/methodology/approachIn continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.FindingsThe tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.Originality/valueTracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

Managing safety of the human on the factory floor: a computer vision fusion approach

PurposeMaintaining the safety of the human is a major concern in factories where humans co-exist with robots and other physical tools. Typically, the area around the robots is monitored using lasers. However, lasers cannot distinguish between human and non-human objects in the robot’s path. Stopping or slowing down the robot when non-human objects approach is unproductive. This research contribution addresses that inefficiency by showing how computer-vision techniques can be used instead of lasers which improve up-time of the robot.Design/methodology/approachA computer-vision safety system is presented. Image segmentation, 3D point clouds, face recognition, hand gesture recognition, speed and trajectory tracking and a digital twin are used. Using speed and separation, the robot’s speed is controlled based on the nearest location of humans accurate to their body shape. The computer-vision safety system is compared to a traditional laser measure. The system is evaluated in a controlled test, and in the field.FindingsComputer-vision and lasers are shown to be equivalent by a measure of relationship and measure of agreement. R2 is given as 0.999983. The two methods are systematically producing similar results, as the bias is close to zero, at 0.060 mm. Using Bland–Altman analysis, 95% of the differences lie within the limits of maximum acceptable differences.Originality/valueIn this paper an original model for future computer-vision safety systems is described which is equivalent to existing laser systems, identifies and adapts to particular humans and reduces the need to slow and stop systems thereby improving efficiency. The implication is that computer-vision can be used to substitute lasers and permit adaptive robotic control in human–robot collaboration systems.

Adaptive patient-cooperative compliant control of lower limb rehabilitation robot

With the increase in the number of stroke patients, there is a growing demand for rehabilitation training. Robot-assisted training is expected to play a crucial role in meeting this demand. To ensure the safety and comfort of patients during rehabilitation training, it is important to have a patient-cooperative compliant control system for rehabilitation robots. In order to enhance the motion compliance of patients during rehabilitation training, a hierarchical adaptive patient-cooperative compliant control strategy that includes patient-passive exercise and patient-cooperative exercise is proposed. A low-level adaptive backstepping position controller is selected to ensure accurate tracking of the desired trajectory. At the high-level, an adaptive admittance controller is employed to plan the desired trajectory based on the interaction force between the patient and the robot. The results of the patient–robot cooperation experiment on a rehabilitation robot show a significant improvement in tracking trajectory, with a decrease of 76.45% in the dimensionless squared jerk (DSJ) and a decrease of 15.38% in the normalized root mean square deviation (NRMSD) when using the adaptive admittance controller. The proposed adaptive patient-cooperative control strategy effectively enhances the compliance of robot movements, thereby ensuring the safety and comfort of patients during rehabilitation training.

Structural design and adaptive tracking control of automatic welding robot for liquefied natural gas containment system

Liquefied natural gas (LNG) is commonly transported by LNG carriers and stored in cargo containment systems. The primary barrier of the MARK III cargo containment system is welded to a closed space with corrugated stainless steel plates. To meet the requirements of excellent sealing and thermal insulation for cargo containment, the welding process of the corrugated plate need to be strictly controlled, which poses a challenge to the development of related welding equipment. In this paper, we present a new five-axis automatic welding robot system used for plasma arc welding on corrugated surfaces. The moment transfer scheme of the dual linkage mechanism makes the rotary movement of the robot more accurate and stable which gives the system with simpler control algorithm and better overall force characteristics. To ensure tracking accuracy, a novel sensing method based on a LASER sensor, two contact sensors, and an angle sensor is proposed to implement multiple functions such as tracking the welding arc length, identifying corrugation shapes, and detecting welding gun posture. Based on the devised tracking sensor system and welding robot, a servo-control system with a surface-tracking welding control algorithm is established. The experimental results show that the robot system's welding speed is about 7 times that of hand welding and the welding qualification rate was 99%, significantly improving welding efficiency and quality as a critical equipment technology in the process of corrugated plate welding.

Dynamic modeling and adaptive finite-time control of cable-driven parallel robots for ship cleaning

ABSTRACT Ship cleaning, often manually performed by numerous workers, is crucial in the shipping industry. This study introduces a marine cable-driven parallel cleaning robot (MCDPCR) to enhance cleaning security and efficiency. To investigate trajectory tracking under the moving base, the dynamic model of MCDPCR is established, and the optimisation of cable forces is considered. Then, an adaptive terminal sliding mode controller is designed, integrating the discontinuous sign function term to obtain a continuous actual control law and thus a chattering-free response. Meanwhile, the uncertainty is compensated by an adaptive method. The stability of the control strategy is analyzed using the Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by several simulation scenarios. The results show that the controller has a fast global convergence speed, high tracking accuracy, and strong robustness. The method can provide a valuable reference for developing automated marine cleaning equipment.

Adaptive Approximation Sliding-Mode Control of an Uncertain Continuum Robot with Input Nonlinearities and Disturbances

This paper develops an adaptive nonsingular fast terminal sliding-mode control (ANFTSMC) scheme for the continuum robot subjected to uncertain dynamics, external disturbances, and input nonlinearities (e.g., actuator deadzones/faults). Concretely, a function approximation technique (FAT) is utilized to estimate unknown robot dynamics and actuator deadzones/faults online. Furthermore, a disturbance observer (DO) is devised to make up for unknown external disturbances. Then, an ANFTSMC scheme combined with FAT and DO is developed, to expedite the restoration of the stability for the continuum robot. The proposed ANFTSMC not only can retain the benefits of traditional terminal sliding-mode control (TSMC), containing easy enforcement, quick response, and robustness to uncertainties but also dispose of the latent singularity for traditional faster TSMC designs. Afterward, the simulation results show that the proposed controller can effectively improve the trajectory tracking accuracy of the continuum robot, and the tracking root-mean-square errors are 0.0115 and 0.0128 rad. Finally, the effectiveness of ANFTSMC scheme is validated by experiments.

Adaptive Image-Based Moving-Target Tracking Control of Wheeled Mobile Robots With Visibility Maintenance and Obstacle Avoidance

Adaptive Image-Based Moving-Target Tracking Control of Wheeled Mobile Robots With Visibility Maintenance and Obstacle Avoidance

FedHIP: Federated learning for privacy-preserving human intention prediction in human-robot collaborative assembly tasks

Human-robot collaboration is a promising solution to relieve construction workers from repetitive and physically demanding tasks, thus improving construction safety and productivity. Many studies have developed various deep learning models for human intention prediction, which will form the basis for proactive and adaptive robot planning and control to enable intelligent human-robot collaboration. However, there remain two challenges. First, most research only focuses on a single type of human intention, without a holistic understanding of multi-level intention, including both high-level intended actions and objects of interest, and low-level body movements. Second, conventional deep learning approaches train a centralized model with aggregated datasets, which requires the sharing of sensitive information (e.g., personal images and behavior data), posing broad privacy concerns in practical implementation. This study proposes a vision-based multi-task federated learning (FL) framework, FedHIP, for multi-level human intention prediction in human-robot collaborative assembly tasks. Specifically, taking body movements and assembly components as inputs, a long short-term memory based multi-task learning model was developed to simultaneously predict multi-level human intention in assembly tasks. FL was employed to train the model in a distributed and privacy-preserving way on local clients without the need of transmitting sensitive data. The results show that the proposal FedHIP without and with pre-train can achieve an accuracy of 80.1% and 85.7% in action prediction, 97.6% and 97.8% in object prediction, and an average displacement error of 12.7 and 11.7 pixels in motion prediction, respectively. Models trained from FedHIP were also compared with those obtained from traditional centralized training and local training. It was found that FL leads to compatible accuracy with centralized training and much higher accuracy than local training while preserving data privacy.

A general update rule for Lyapunov‐based adaptive control of mobile robots with wheel slip

SummaryIn this article, we introduce a novel family of Lyapunov‐based adaptive kinematic control laws developed to solve the trajectory tracking problem for a differential‐drive mobile robot under the influence of both longitudinal and lateral wheel slip. Each adaptive controller in this family is constructed by augmenting a nonadaptive nominal controller, originally designed for the slip‐free case, with an update rule capable of estimating the longitudinal slip. In the absence of lateral slip and under constant longitudinal slip, we establish the convergence of the trajectory tracking error to zero and, assuming a persistent excitation condition, we also demonstrate the convergence of the slip estimate error to zero. When lateral slip is present, we analyse a particular control law from our family of adaptive controllers. This law ensures the trajectory tracking error is uniformly ultimately bounded around the origin, demonstrating the robustness of our adaptive control scheme in dealing with time‐varying longitudinal and lateral slip. The validity of our approach is assessed through comprehensive numerical simulations.