In a rehabilitation exoskeleton, stable and safe operation is of central importance in rehabilitation training. This article develops a soft prescribed performance (SPP)-based reinforcement learning (RL) control method to address the conflict between performance constraints and system degradation, ensuring high accuracy and safe operation. First, a tunnel-type prescribed performance function is used to achieve faster convergence and smaller overshoot. Safety boundaries are used to define the tolerable error range, and an intermediate system links the safety and soft boundaries. The soft boundaries are dynamically adjusted to ensure safe operation by temporarily relaxing constraints during performance degradation. An RL approach based on an actor-critic (AC) structure is employed to handle unknown lumped disturbance. Theoretical analysis confirms the stability of the closed-loop system. Furthermore, a series of experiments is conducted on a self-built upper-limb rehabilitation exoskeleton robot driven by pneumatic artificial muscles to validate the effectiveness and robustness of the proposed method.

By mimicking the movement of biological muscles, pneumatic artificial muscles (PAMs) are developed as a novel type of bionic actuator known for their compliance and high safety; however, the inherent characteristics of PAMs (e.g., hysteresis and creep) increase the difficulty in modeling and control. Moreover, unmodeled dynamics in PAM-actuated parallel robots are unavoidable, which further complicates the efficient tracking task of PAM-actuated parallel robots. Therefore, we propose an output feedback controller with interval type-2 fuzzy neural networks (IT2FNNs) for PAM-actuated parallel robots to obtain satisfactory tracking results. Specifically, compared with most existing methods using the interval type-1 fuzzy neural network (NN), the IT2FNN used is more beneficial for dealing with unmodeled dynamics and system uncertainties on PAM-actuated parallel robots. Meanwhile, considering that most practical systems are often only equipped with displacement/angle sensors and lack velocity sensors, an observer is designed to estimate unmeasurable velocity signals. Next, based on Lyapunov techniques, the convergence of tracking errors is proven through theoretical analysis. To our knowledge, this article is the first to apply IT2FNNs with observation information to PAM-actuated parallel robots with unknown dynamics and unmeasurable velocity signals, and provides rigorous stability analysis. Further, several experiments are implemented, and the corresponding results illustrate the effectiveness and robustness of the proposed controller.

In robotic orthopedic surgery, the optical tracking system (OTS) is typically placed in a fixed location. In surgery, the OTS’s line of sight is likely to be blocked. This will interrupt the navigation and affect surgical safety. To solve this occlusion problem, an RGB-D camera is used to detect possible occluders and a navigation robot is utilized to actively adjust the OTS viewpoint before occlusion occurs. To guarantee the applicability, the occluder is enveloped using a convex polytope, and a two-phase optimization method is proposed based on duality of convex optimization. The effectiveness of the proposed method is verified via simulations and experiments. Experimental results show that the proposed method can avoid occlusion between the OTS and the occluder, and the targets are located near the center of the measurement volume. This active navigation guarantees the continuity of intraoperative navigation, and thus helps to improve the safety in robotic orthopedic surgery.

Hysteresis has posed significant challenges to the modeling and control of flexible endoscopic robots, which impedes the advancement of automated endoscopic operation. Despite numerous hysteresis modeling approaches aimed at improving accuracy, there are still several unresolved issues, such as inappropriate model selection and non-ideal assumption of noise. Focusing on these challenges, a novel reinforcement active modeling (RAM) scheme is proposed in this paper. By incorporating reinforcement learning, this method augments an Extended Kalman Filter (EKF)-based active modeling strategy, which improves the insensitivity and generalization ability to non-Gaussian noise that is not introduced in training. Finally, a series of comparative experiments are conducted on the self-built flexible endoscopic robot to validate the improvement achieved by the proposed scheme. Compared with some widely-applied methods, the proposed scheme achieved at least 63.8% improvement in the root mean square error (RMSE) in modeling accuracy under Gaussian noise conditions, and at least 36.5% improvement in RMSE under Poisson noise conditions.

Intraoperative optical navigation is widely utilized in robotic surgery systems. Typically, the observation pose of the optical tracking system (OTS) is manually adjusted and then fixed throughout the surgery. However, fixed OTS suffers from limited measurement volume (MV) and visual interferences, making consistent navigation challenging in clinics. In this paper, an operation-navigation dual-robot collaborative system is proposed for orthopedic surgeries. An extra navigation robot is introduced to actively adjust the observation pose of the OTS. A collaborative preoperative planning method is proposed for this dual-robot system, including osteotomy path planning of the operation robot and collaborative planning of the navigation robot. Firstly, osteotomy paths of the operation robot are generated according to the surgery regulations and the geometric features of the vertebral foramen. Secondly, based on the generated osteotomy paths, the collaborative planning of the navigation robot is formulated into a multi-objective optimization problem to find the optimal poses of the OTS for each osteotomy plane. Compared with fixed OTS, active navigation is capable of keeping all the targets within the MV of the OTS throughout the surgery. Semi-laminectomy on a human spine phantom is adopted as an example to experimentally evaluate the effectiveness of the proposed method.Note to Practitioners—As the demand for robot-assisted surgery is increasing, the precision of operation has become one of the key safety requirements. Preoperative planning provides guidance for the surgeon, and intraoperative navigation monitor the status of the lesion and the surgical tool in real-time. In conventional intraoperative optical navigation, the OTS is manually adjusted and remains stationary. However, the limited MV and the visual interferences introduce risks and uncertainties to the surgical system. In order to address the limitations of fixed OTS, an operation-navigation dual-robot collaborative system is proposed for orthopedic surgeries, which is composed of a surgical operation module and an active navigation module. The navigation robot is used to actively adjust the pose of the OTS. A collaborative preoperative planning for the operation-navigation dual-robot orthopedic surgery system is proposed in this paper. The effectiveness of the proposed method is verified on a human spine phantom. Experimental results show that the active navigation provides more freedom to the overall system by freely adjusting the OTS, which ensures the stability of the surgical navigation. In future work, efforts will be directed toward the identification and avoidance of the obstacle.

Pneumatic artificial muscle (PAM), featuring good flexibility and safety, has been widely used in rehabilitation and bionic robots. However, the complex hysteretic nonlinearities and uncertainties of the PAM cause great difficulties and challenges to the accurate modeling and controller design, especially when confronted with unknown external disturbances in applications. This paper proposes a robust control strategy with disturbance compensation for the hysteresis compensation and trajectory tracking of PAMs. Considering the high hysteretic nonlinearity of the PAM, a modified Prandtl-Ishlinskii model is used as a feedforward hysteresis compensator. For the linearized system, adaptive set-membership filtering (ASMF) is used to estimate the nonlinear terms and external disturbances of the overall system. A sliding mode controller (SMC) with disturbance compensation is designed and cascaded to the feedforward hysteresis compensator in series. The stability of the closed-loop system is theoretically proved. The proposed method guarantees that the tracking error of the PAM system is bounded. Finally, the effectiveness and robustness of the proposed controller are verified via a series of experiments on an in-house built testbench for PAMs. Note to Practitioners—With the increasing demand on human-robot interaction, the safety and compliance of robots have become one key requirement. PAM is a compliant actuator, exhibiting good flexibility, safety, and clean energy. PAM is widely used in rehabilitation robots, whereas its strong hysteresis nonlinearity and sensitivity to external disturbances affect its motion accuracy. This paper proposes an ASMF-based discrete SMC, which uses an inverse hysteresis model to compensate for the strong hysteresis of the PAM and uses ASMF to estimate the lumped disturbance of the system. Compared with the other filters, ASMF is unique in that its estimation error is bounded, which is very useful in the stability proof of the overall system. The effectiveness of the proposed controller is experimentally verified. Experimental results show that PAM’s hysteresis can be efficiently compensated, and the influence of external disturbances can be attenuated by the proposed controller, resulting in improved motion accuracy and robustness. In future work, efforts will be directed towards the modeling and control of PAMs in multi-DOF robots.

As pneumatic artificial muscles (PAMs) are similar to biological muscles in structure and movement mechanisms, parallel robots actuated by PAMs have development prospects in rehabilitation and industry, with advantages such as compliance, high safety, strong bearing capacity, and satisfactory dynamic performance. However, the parameter uncertainties and model complexity related to inherent characteristics of parallel robots actuated by PAMs (e.g., time-varying, coupling, hysteresis, creep, and high nonlinearity), bring challenges to accurate dynamic modeling and controller design. Therefore, to achieve satisfactory tracking performance, this article presents an adaptive compensation tracking controller with error constraints for parallel robots actuated by PAMs. The proposed controller deals with parameter uncertainties by estimating system parameters to ensure accurate tracking, which is indicated as an effective solution for a combination of PAMs and parallel robots. Furthermore, using desired trajectory signals in the complicated regression matrix, the online computational burden is significantly reduced. Moreover, to improve operation safety further, an auxiliary term with a theoretical demonstration guarantees that the tracking errors are maintained within allowable ranges. Then, the closed-loop stability is demonstrated by Lyapunov techniques. As far as we know, it is the first time that the challenges of parameter uncertainties, computational burdens, and error constraints of parallel robots actuated by PAMs are simultaneously addressed, which has both theoretical significance and practical value. Finally, the hardware experiments are implemented under different scenarios, and the results indicate that the proposed method achieves satisfactory tracking performance.

Due to high biological adaptability and flexibility, pneumatic artificial muscle (PAM) systems are widely employed in exoskeleton robots to accomplish rehabilitation training with repetitive motions. However, some intrinsic characteristics of PAMs and inevitable practical factors, e.g., high nonlinearity, hysteresis, uncertain dynamics, and limited working space, may badly degrade tracking performance and safety. Hence, this paper designs a new learning-based motion controller for PAMs, to simultaneously compensate for model uncertainties, eliminate tracking errors, and satisfy preset motion constraints. Particularly, when PAMs suffer from periodically non-parametric uncertainties, the elaborately designed continuous update algorithm can repetitively learn them online to enhance tracking accuracy, without employing upper/lower bounds of unknown parts for controller design and gain selections. Meanwhile, some non-periodic uncertainties are handled by a robust term, whose value is only related to the initial states of PAMs, instead of exact upper bounds of unknown dynamics. From safety concerns, we introduce error-related saturation terms to limit initial amplitudes of control inputs within saturation constraints and avoid overlarge errors inducing overlarge acceleration. Meanwhile, the constraint-related auxiliary term is utilized to keep tracking errors within allowable ranges. To the best of our knowledge, this paper presents the first learning-based error-constrained controller for uncertain PAM-actuated exoskeleton robots, to realize high-precision tracking control and improve safety without additional gain conditions. Moreover, the asymptotic convergence of tracking errors is strictly proven by Lyapunov-based stability analysis. Finally, based on a self-built exoskeleton robot, the effectiveness of the proposed controller is verified by hardware experiments. Note to Practitioners—This work is motivated by the practical requirements of exoskeleton robots in rehabilitation training and exploration fields. Currently, PAM systems, as a kind of new flexible actuator equipment, are playing increasingly important roles in the development of exoskeleton robot control. However, uncertain (or time-varying) parameters/structures and highly nonlinear dynamics, such as creep and hysteresis, may badly increase the control difficulty of PAMs. Moreover, higher and higher tracking accuracy and safety requirements also induce urgently solved problems to practical PAM-actuated exoskeleton robots, e.g., smooth start, motion constraints, and rapid error elimination. To this end, this paper proposes a new learning-based adaptive controller, which realizes accurate tracking control for PAM-actuated exoskeleton robots by utilizing an elaborately designed repetitive learning algorithm and a robust term to handle periodic and non-periodic uncertainties, respectively. More importantly, the proposed controller simultaneously enhances transient performance of PAMs, including gradually improved tracking accuracy, effective constraints for startup acceleration and tracking errors. Additionally, it is not required to consider the upper bounds of unknown dynamics and additional gain selection conditions, which is theoretically and practically important for PAM systems. Some hardware experiments further verify the effectiveness and robustness of the suggested controller. In our future work, we intend to design more effective methods for PAMs with unmeasurable states and time-delay.

Pneumatic artificial muscle (PAM) systems are a kind of tube-like actuators, which can act roughly like human muscles by performing contractile or extensional motions actuated by pressurized air. At present, it is still an open and challenging issue to tackle positioning and tracking control problems of PAM systems, due to inherent characteristics, e.g., unidirectional inputs, high nonlinearities, hysteresis, time-varying characteristics, etc. In this paper, a new adaptive control method is proposed for PAM systems, which achieves satisfactory tracking performance. To this end, an update law is designed to estimate unknown system parameters online. Also, some control input transforming operations are applied to address unidirectional constraints (i.e., control inputs of PAM systems should always be positive). As far as we know, compared with most of the existing control methods, this paper gives the first continuous control solution for PAM systems that can simultaneously compensate parametric uncertainties, reject external disturbances, and meet unidirectional constraints. Without linearizing the nonlinear dynamics, the closed-loop system is theoretically proven to be asymptotically stable at the equilibrium point with the stability analysis. In addition, a series of hardware experiments are implemented on a self-built hardware platform, indicating that the proposed method achieves satisfactory tracking control and exhibits robustness against parametric uncertainties and disturbances.

For XYΘ-type flexure-based manipulators with nanometer accuracy, the coupled linear/angular motions of the end effector cause unwanted misalignments to the measurement system, leading to measurement error or even measurement failure. This paper proposes an indirect measuring method for such manipulators, where the 3-PRR topology is adopted to kinematically transfer the coupled motions of the end effector to the linear motions of three prismatic joints. Unlike the other XYΘ manipulators, three linear position sensors are used to measure the linear motions of the prismatic joints, not the coupled motions of the end effector. Accordingly, the position and orientation of the end effector can be obtained via the forward kinematics. This indirect method aims to eliminate the influence of the coupled motions of the end effector on the measurement system, and to guarantee the accuracies and effectiveness of the sensors' outputs. An XYΘ flexure-based manipulator is developed following this method. The design and kinematics modeling is presented, and the effectiveness of this method is computationally verified. The future work will focus on the experimental verification of this method.

In this paper, a robotic cell rotation method based on the minimum rotation force is presented to adjust oocyte orientation in biological applications. In this method, the minimum rotation force, which can control the rotation angle (RA) of the oocyte quantitatively and generate minimum oocyte deformations, is derived through a force analysis on the oocyte in rotation. To exert this force on the oocyte, the moving trajectories (MT) of the injection micropipette (IM), are determined using mechanical properties of the oocytes. Further, by moving the IM along the designed MT, the rotation force control is achieved. To verify the feasibility of this method, a robotic rotation experiment for batch porcine oocytes are performed. Experimental results demonstrate that this system rotates the oocyte a10t an average speed of 28.6s/cell and with a success rate of 93.3%. More importantly, this method can generate much less oocyte deformations during cell rotation process compared with the manual method, while the average control error of RA in each step is only 1.2° (versus averagely 8.3° in manual operation), which demonstrates that our method can effectively reduce cell deformations and improve control accuracy of the RA. Note to Practitioners - Using an IM to rotate the oocyte is the most popular method to adjust oocyte orientations in many cellular biological applications. To rotate the oocyte precisely and reduce mechanical damages to the oocyte, the rotation force exerted on the oocyte should be estimated and controlled precisely. However, it is a challenging task and has not been resolved well. This paper calculates the minimum rotation force through the force analysis on oocyte and uses it to improve control accuracy of the RA and limit the cell deformations. Using calibrated oocyte mechanical properties, the MT of the IM corresponding to the minimum rotation force is designed. Then, by moving the IM along designed MT, the rotation force control is achieved online. This method does not rely on the force sensors and is realized on traditional micro-operation systems. Coupled with previous works, this method is able to operate batch oocytes one by one. Therefore, it can easily be applied in biological applications and replace manual operations.