Adaptive Control Improves Sclera Force Safety in Robot-Assisted Eye Surgery: A Clinical Study.

A surgeon's physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon's hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the tool-sclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.

Notable challenges during retinal surgery lend themselves to robotic assistance which has proven beneficial in providing a safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g. instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics (FWK) of the Steady-Hand Eye Robot (SHER) and Fiber Brag Grating (FBG) sensor measurements. This is accomplished using a Kalman Filtering (KF) approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from pre-operative offline calibrations for stiffness.

This work introduces the design of an adaptive tracking control strategy for a 6-DOF Staubli TX2-60L industrial robot manipulator operating under dynamic model uncertainties caused by variable payloads. A complete kinematic and dynamic model of the robot is derived using Denavit–Hartenberg parameters and the Lagrangian formulation, respectively. To address the limitations of conventional control methods under payload variations, an adaptive controller is designed, which continuously updates its parameters based on real-time tracking errors. A Lyapunov stability analysis is conducted to rigorously establish that the closed-loop system guarantees convergent tracking errors and bounded internal states. Simulation studies with different payload scenarios, including no load, step load variation, and continuous load changes, show the performance of the proposed control strategy. The results confirm that the adaptive controller maintains precise trajectory tracking and robust stability despite significant model uncertainties, highlighting its suitability for industrial pick-and-place and high-precision automation tasks. Keywords: Robot manipulator, kinematic model, dynamic model, variable payloads, adaptive control, stability analysis.

Muscle weakness is the primary impairment causing mobility difficulty among stroke survivors. Millions of people are unable to live normally because of mobility difficulty every year. Strength training is an effective method to improve lower extremity ability but is limited by the shortage of medical staff. Thus, this paper proposes a robot-assisted active training (RAAT) by an adaptive admittance control scheme with virtual reality interaction (AACVRI). AACVRI consists of a stiffness variable admittance controller, an adaptive controller, and virtual reality (VR) interactions. In order to provide human-robot reality interactions corresponding to virtual scenes, an admittance control law with variable stiffness term was developed to define the mechanics property of the end effector. The adaptive controller improves tracking performances by compensating interaction forces and dynamics model deviations. A virtual training environment including action following, event feedback, and competition mechanism is utilized for improving boring training experience and engaging users to maintain active state in cycling training. To verify controller performances and the feasibility of RAAT, experiments were conducted with eight subjects. Admittance control provides desired variable interactions along the trajectory. The robot responds to different virtual events by changing admittance parameters according to trigger feedbacks. Adaptive control ensures tracking errors at a low level. Subjects were maintained in active state during this strength training. Their physiological signals significantly increased, and interaction forces were at a high level. RAAT is a feasible approach for lower limb strength training, and users can independently complete high-quality active strength training under RAAT.

In this paper, a T–S (Takagi–Sugeno) adaptive tracking algorithm control based on small gain theorem is proposed for an uncertain robot system with $$n$$-link manipulators. A nonzero time-varying parameter is introduced in the common T–S fuzzy logic system, the T–S type fuzzy logic system with updated parameters laws is build, then the new and original universal approximation with parameter is introduced. The approximation accuracy can be updated on-line by the parameters, which is not limited by the number of fuzzy rules. With the novel property of universal approximation, the proposed adaptive control can be synthetized to overcome the limitations such as on-line learning computation burden in conventional fuzzy logic systems. The originality T–S fuzzy logic system is used to compensate the unknown model of robot manipulators and the adaptive tracking control algorithm is designed with the new property of universal approximation. Based on the analysis of small gain theorem and ISS theory (input-to-state stability), all signals in closed-loop system can be guaranteed to be bounded, and the system can be extended from semi-global stability to global stability by employing the proposed adaptive control scheme. Finally, simulation results are shown to demonstrate the effectiveness of the adaptive control scheme.

Adaptive Tracking Control of Rigid Robot Manipulators by Using Only Joint Position Measurements

This paper deals with an adaptive control problem of direct drive (DD) robotic manipulators with force sensor stiffness. Since DD motor has advantages to force control in robotic application, so that many works have been reported so far in control problems. However, the dynamic compensation for the sensor stiffness has little been reported so far, even though the force sensor in DD robots decreases the stiffness of the overall DD robots. In order to make use of high stiffness of DD motors itself in the servo loop, it is necessary to take into account the sensor stiffness for the high gain dynamic control. Therefore, an adaptive dynamic compensation method is introduced here in the vein of the model reference adaptive control to widen the applicability of the DD robots, because the feedback with the constant gains may cause unstable behaviors, depending on objects dynamics. The applicability of the proposed method could be verified with some experiments. Then this method is applied to nonlinear objects, such as rotary channel. The experiments show the better performance by the proposed method for the nonlinear compensation than the conventional feed-back control method. Thus, it is shown that the DD motor can have much wider applicabilities by the proposed adaptive force control.

In this work, an Adaptive Linear Quadratic Regulator (LQR) is proposed for the tracking control of a 3 DOF articulated robotic manipulator. The main objective of this work is to drive the robotic manipulator to its equilibrium point from any initial position of the end effector of a robotic manipulator in an adaptive way even if the system dynamics consist of modelling uncertainties. The conventional LQR is able to provide a good measure of robustness against disturbances and achieve good tracking performance. However, it will give degraded tracking performance in case of the presence of model uncertainties. Therefore LQR tracking performance is enhanced by using a Model Reference Adaptive Controller (MRAC) in the literature for enabling a 2 DOF system to achieve good tracking against modelling uncertainties. But it has not been applied for a 3 DOF system till date. Therefore in this work, it is proposed to be applied for such a system for the first time to realize an adaptive control. The forward and inverse kinematics level equations are formulated using an indirect form of the Denavit Hartenberg (DH) convention. The dynamics of robotic manipulator are derived using the Euler-Lagrange formulation. The dynamics of manipulator with the LQR control are considered to be the reference dynamics for the MRAC control and the modelling uncertainty will be added to the system dynamics to test its performance. The MRAC augmented LQR provides faster convergence with improved stability against modelling uncertainties.

Presents an approach to the design and real-time implementation of an adaptive controller for a robotic manipulator based on digital signal processors. The Texas Instruments DSP (TMS320C31) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for robotic manipulators. In the proposed scheme, adaptation laws are derived from the direct model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feedforward and feedback controller and PI-type time-varying auxiliary control elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for an industrial robot with four joints in the joint space and Cartesian space.

We experimentally demonstrate adaptive control of linearly polarized (LP) modes based on a fiber Bragg grating (FBG), where feedback for the adaptive algorithm is provided by the optical signal transmitted through and reflected by the FBG. In this approach, by maximizing the ratio of optical power transmitted and reflected by the FBG, we can selectively excite the LP 01 and the LP 11 modes at an optical wavelength associat ed with the appropriate FBG reflection peak. Combining this method with time-division-multiplexing, it should be possible to adaptively control guided modes at any desired location within a multimode fiber network. Keywords: Adaptive optics, mode control, few mode fiber, fiber Bragg grating 1. INTRODUCTION Traditionally, single mode fiber (SMF) are highly preferred fo r important applications such as optical commination and sensing. However, with recent developm ent of mode-division-multiplexing (MDM), there has been renewed interest in multimode fibers (MMFs). For example, in MDM, different linearly polarized (LP) modes in few mode fibers (FMFs) have been utilized as a multiplexing parameter to increase the information capacity of optical communications

Purpose Interaction force control is crucial for robotic manipulators in contact tasks. For different tasks, robotic manipulators are expected to possess adaptive capability to modulate interaction force performance. To this end, this paper aims to propose an interaction force control scheme for robotic contact tasks via prescribed performance-enhanced impedance regulation. Design/methodology/approach The proposed scheme integrates Dual Prescribed Performance boundary in impedance outer loop and position inner loop in parallel, named DPP-IC. Based on the performance boundary, the interaction force deviation is transformed into matching position adjustment compensation for enhancing impedance regulation in contact tasks. The compensation together with the output of the standard impedance model is then used as desired input in our designed prescribed performance PID controller (PPC-PID) in position loop. This can confine interaction force within prescribed performance boundary, thereby simultaneously improving transient and steady-state control performance. Findings Comparison experiments with three mainstream impedance control methods of standard impedance control, adaptive law-based impedance control and learning-based variable impedance control are conducted on our self-designed robotic manipulators. The results of this study prove that the advantages of the proposed scheme in improving interaction force control. Originality/value This paper first introduces prescribed performance boundary into standard impedance model in parallel, improving inter-action force control performance for robotic contact tasks on a wide range of environmental stiffness.

Barriers in Ophthalmology Residency Applications for Students Identifying as Underrepresented in Medicine: A San Francisco Match Analysis

A switched adaptive controller is designed for robot manipulators with friction and changing loads. The nonlinear friction is depicted by a nonlinear friction model, and a switched nonlinear system is used to model the parameter jump caused by load change. Hyperstability theory is used in the designing procedure, which provides more options for adaptive laws than Lyapunov theory. In the presence of friction and changing loads, asymptotic tracking is achieved under arbitrary switching, which is not able to accomplish by a non-switched adaptive controller. The proposed method is validated by a simulation of a 2 degree of freedom manipulator.

On the fault tolerant control of robotic manipulators under the saturation of actuators, a tracking controller with bounded feedback is designed in this article, which is based on gravity compensation and a filter function. An adaptive controller is presented to compensate the variance of gravity on-line. Both of them are proved to be semi-global asymptotic stable. Simulation results on the two-link robotic manipulators demonstrate the validity of the controllers and the stability of the closed-loop systems.

A neural network (NN)-based adaptive control law is proposed for the tracking control of an n-link robot manipulator with unknown dynamic nonlinearities. Basis-function-like networks are employed to approximate the plant nonlinearities, and the bound on the NN reconstruction error is assumed to be unknown. The proposed NN-based adaptive control approach integrates the NN approach and an adaptive implementation of the discrete variable structure control, with a simple estimation mechanism for the upper bound on the NN reconstruction errors and an additional control input as a function of the estimate. Lyapunov stability theory is used to prove the uniform ultimate boundedness of the tracking error, and simulation results demonstrate the applicability of the proposed method to achieve desired performance.