The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

Uncertainties in the kinematic and dynamic parameters of a parallel robot are unavoidable. The problem is more crucial in the cases where the manipulator interacts with the environment and when it is large–scale or deployable. Furthermore, precise measurement of the velocity of the end-effector is almost inaccessible in practice. This paper addresses the above shortcomings by designing of an adaptive trajectory tracking controller with merely position feedback of joint and task space variables. Simplicity of implementation, separation of adaptation laws of dynamic and kinematic parameters, and reduction of the number of adaptation laws such that in some cases, e.g., cable-driven robots, it is identically equivalent to the number of unknown parameters are some advantages of the proposed controller. The method’s efficiency is shown via implementation on a cable-driven parallel manipulator and an intraocular surgery robot. Note to Practitioners—In order to achieve a suitable response in parallel robots with traditional controllers, a precise knowledge of kinematic and dynamic parameters, together with accurate measurement of velocity, is required. In practice, these values are usually derived by robot calibration and identification. Since the system’s parameters may alter or depend on external factors such as temperature, these time-consuming methods should be implemented repeatedly while the robot is out of duty. Additionally, precise velocity measurement is a prohibitive task and requires expensive instruments. This article presents a controller to address the above shortcomings. By this means, a simple adaptive controller based on position feedback is designed such that via suitable estimation of kinematic and dynamic parameters, trajectory tracking is obtained without the need for accurate initial estimates of the parameters. The proposed method has a number of advantages, including separation of the adaptation laws of kinematic parameters and dynamic parameters, simple representation of the Jacobian matrix in regressor form, and there is no requirement for velocity feedback. Furthermore, a force distribution method is introduced for redundant robots. Hence, the proposed controller is an appropriate alternative to traditional controllers widely used in industries.

Conventionally, deep odometry networks use objective functions that only penalize short-term deviations from the true path. Since such an objective does not impose any constraints on the long-term deviations from the path, a second consistency-based loss term may be added to lower long-term drift. However, maintaining a balance between the two loss terms is challenging and often treated as a design hyperparameter. To mitigate this balancing issue, we propose to use the uncertainty over both odometry and the long-term transformations in a maximum likelihood setting and allow the network to tune the weighting between the two loss terms. To this end, we derive the odometry uncertainty alongside the pose outputs using the network itself and to derive the covariance matrix over the integrated transformation, we propose to propagate the odometry uncertainty through each iteration. This formulation provides an adaptive and statistically consistent method to weigh the incremental and integrated loss terms against each other, noting the increase in uncertainty as more steps are integrated over. We show that our approach to consistency-based losses allows the network to surpass the accuracy of the state-of-the-art visual odometry approaches. Then, the efficacy of the derived uncertainty as weighting medium is visualized and the performance benefits of uncertainty quantification are shown in a pose-graph based localization scenario.

Although the Bayesian paradigm offers a formal framework for estimating the entire probability distribution over uncertain parameters, its online implementation can be challenging due to high computational costs. We suggest the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method, which eliminates the shortcomings of conventional online techniques while computing the entire probability density function of model parameters. The limitations to Gaussian noise, the application to only linear in the parameters (LIP) systems, and the persistent excitation (PE) needs are some of these drawbacks. In ARMCMC, a temporal forgetting factor (TFF)-based variable jump distribution is proposed. The forgetting factor can be presented adaptively using the TFF in many dynamical systems as an alternative to a constant hyperparameter. By offering a trade-off between exploitation and exploration, the specific jump distribution has been optimised towards hybrid/multi-modal systems that permit inferences among modes. These trade-off are adjusted based on parameter evolution rate. We demonstrate that ARMCMC requires fewer samples than conventional MCMC methods to achieve the same precision and reliability. We demonstrate our approach using parameter estimation in a soft bending actuator and the Hunt-Crossley dynamic model, two challenging hybrid/multi-modal benchmarks. Additionally, we compare our method with recursive least squares and the particle filter, and show that our technique has significantly more accurate point estimates as well as a decrease in tracking error of the value of interest.

Ease of calibration and high-accuracy task-space state-estimation purely based on onboard sensors is a key requirement for enabling easily deployable cable robots in real-world applications. In this work, we incorporate the onboard camera and kinematic sensors to drive a statistical fusion framework that presents a unified localization and calibration system which requires no initial values for the kinematic parameters. This is achieved by formulating a Monte-Carlo algorithm that initializes a factor-graph representation of the calibration and localization problem. With this, we are able to jointly identify both the kinematic parameters and the visual odometry scale alongside their corresponding uncertainties. We demonstrate the practical applicability of the framework using our state-estimation dataset recorded with the ARAS-CAM suspended cable driven parallel robot, and published as part of this manuscript.

In underactuated robots with closed kinematic chains, the task space configuration variables are coupled through complex dynamics. In this article, the regulation control of a 3-DOF underactuated cable-driven parallel robot is investigated by using the interconnection and damping assignment passivity-based control approach, which is designed based on the solution of some challenging partial differential equations (PDEs). Additionally, as cables can only pull, positive tension in cables shall also be taken into account. Here, the corresponding PDE for the controller design is solved by transforming the PDE into some Pfaffian differential equations. Then, boundedness of control efforts are ensured via suitable modification of the gains. Furthermore, an adaptation law for the mass of the robot is designed and the system stability is investigated through the Lyapunov direct method. The efficiency of the proposed controller is confirmed and compared to another method through experiment.

Robot-assisted arthroscopic surgery is transforming the tradition in orthopaedic surgery. Compliance and stability are essential features that a surgical robot must have for safe physical human-robot interaction (PHRI). Surgical tools attached at the robot end-effector and human-robot interaction will affect the robot dynamics inevitably. This could undermine the utility and stability of the robotic system if the varying robot dynamics are not identified and updated in the robot control law. In this paper, an integrated frame-work for robot impedance control and nonlinear disturbance observer (NDOB)-based compensation of uncertain dynamics is proposed, where the former ensures compliant robot behavior and the latter compensates for dynamic uncertainties when necessary. The combination of impedance controller and NDOB is analyzed theoretically in three scenarios. A complete simulation and experimental studies involving three common conditions are then conducted to evaluate the theoretical analyses. A preliminary$p$HRI application on arthroscopic surgery is designed to implement the proposed framework on a robotic surgeonassist system and evaluate its effectiveness experimentally. By integrating impedance controller with NDOB, the proposed framework allows an accurate impedance control when dynamic model inaccuracy and external disturbance exist.

Ultrasound (US) imaging is a common but physically demanding task in the medical field, and sonographers may need to put in considerable physical effort for producing high-quality US images. During physical human-robot interaction on US imaging, robot compliance is a critical feature that can ensure human user safety while automatic force regulation ability can help to improve task performance. However, higher robot compliance may mean lower force regulation accuracy, and vice versa. Especially, the contact/non-contact status transition can largely affect the control system stability. In this paper, a novel electromyography (EMG)-based hybrid impedance-force control system is developed for US imaging task. The proposed control system incorporates the robot compliance and force regulation ability via a hybrid controller while the EMG channel enables the user to online modulate the trade-off between the two features as necessary. Two experiments are conducted to examine the hybrid controller and show the necessity of involving an EMG-based modulator. A proof-of-concept study on US imaging is performed with implementing the proposed EMG-based control system, and the effectiveness is demonstrated. The proposed control system is promising to ensure robot's stability and patient's safety, thus obtain high-quality US images, while monitoring and reducing sonographer's fatigue. Furthermore, it can be easily adapted to other physically demanding tasks in the field of medicine.

This paper proposes a variable impedance control architecture to facilitate eye surgery training in a dual-user haptic system. In this system, an expert surgeon (the trainer) and a novice surgeon (the trainee) collaborate on a surgical procedure using their own haptic devices. The mechanical impedance parameters of the trainer's haptic device remain constant during the operation, whereas those of the trainee vary with his/her proficiency level. The trainee's relative proficiency might be objectively quantified in real-time based on position error between the trainer and the trainee. The proposed architecture enables the trainer to intervene in the training process as needed to ensure the trainee is following the right course of action and to avoid the trainee's from potential tissue injuries. The stability of the overall nonlinear closed-loop system has been investigated using the input-to-state stability (ISS) criterion. High-gain observer with unknown inputs is considered in this work to estimate the interaction forces. Simulation and experimental results under different scenarios confirm the effectiveness of the proposed control methods.

In this paper, a controller is proposed for a quadrotor Unmanned Aerial Vehicle (UAV) based on a Fractional Order Fast Terminal Sliding Mode Control (FOFTSMC) scheme. In order to obtain better tracking performance in the position and attitude of the system, the system model is re-formulated into a fully actuated subsystem and an under-actuated subsystem. Then, the flight controllers are designed such that to guarantee the finite-time convergence of the tracking errors of all the system state variables to zero. The developed FOFTSMC approach helps to reduce the amplitude of the control signals, which leads to enhancing the closed-loop relative stability of the quadrotor. The proposed FOFTSMC technique is chattering free and robust against the air drag, aerodynamic forces, and moments considered as the external disturbances. Furthermore, the Lyapunov direct method is used to analyze the stability of the closed-loop systems. The simulation results show the promising results of the proposed method in practice.

Human Activity Recognition (HAR) is a crucial factor in assisted living systems and elderly care solutions where the activity of the subject can be used to ensure the safety of the elderly and provide more efficient services. HAR is a challenging problem that needs advanced solutions than using handcrafted features to achieve a desirable performance. Deep learning has been proposed as a solution to obtain more accurate HAR systems being robust against noise. In this paper, we introduce ARC-Net and propose the utilization of capsules to fuse the information from multiple inertial measurement units (IMUs) to predict the activity performed by the subject. We hypothesize that this network will be able to tune out the unnecessary information and will be able to make more accurate decisions through the iterative mechanism embedded in capsule networks. We provide heatmaps of the priors, learned by the network, to visualize the utilization of each of the data sources by the trained network. Then, gradient based interpretations are provided and further discussed. By using the proposed network, we were able to increase the accuracy of the state-of-the-art approaches by 2%. Furthermore, we investigate the directionality of the confusion matrices of our results and discuss the specificity of the activities based on the provided data.

In this study, a new approach to design a controller for a visual servoing (VS) system is proposed. Kernel‐measurement is used to track the motion of a featureless object which is defined as sum of weighted‐image value through smooth kernel functions. This approach was used in kernel‐based VS (KBVS). To improve the tracking error and expand the stability region, sliding mode control is integrated with kernel measurement. Proportional–integral‐type sliding surface is chosen as a suitable manifold to produce the required control effort. Moreover, the stability of this algorithm is analysed via Lyapunov theory and its performance is verified experimentally by implementing the proposed method on a five degrees of freedom industrial robot. Through experimental results, it is shown that the performance of tracking error in the proposed method is more suitable than KBVS, for a wider workspace and when the object is placed near the boundary of the camera's field of view.

In this paper the problem of loop closing from depth or camera image information in an unknown environment is investigated. A sparse model is constructed from a parametric dictionary for every range or camera image as mobile robot observations. In contrast to high-dimensional feature-based representations, in this model, the dimension of the sensor measurements' representations is reduced. Considering the loop closure detection as a clustering problem in high-dimensional space, little attention has been paid to the curse of dimensionality in the existing state-of-the-art algorithms. In this paper, a representation is developed from a sparse model of images, with a lower dimension than original sensor observations. Exploiting the algorithmic information theory, the representation is developed such that it has the geometrically transformation invariant property in the sense of Kolmogorov complexity. A universal normalized metric is used for comparison of complexity based representations of image models. Finally, a distinctive property of normalized compression distance is exploited for detecting similar places and rejecting incorrect loop closure candidates. Experimental results show efficiency and accuracy of the proposed method in comparison to the state-of-the-art algorithms and some recently proposed methods.

In this paper, control of fully-constrained parallel cable robots with elastic cables is studied in detail. In the modeling process, longitudinal vibration of cables is considered as their dominant dynamics, and the governing equations of motion are rewritten to the standard form of singular perturbation. The proposed composite controller consists of two main components. A rigid controller is designed based on the slow or rigid model of the system and a corrective term is added to guarantee asymptotic stability of the fast dynamics. Then, by using Tikhonov theorem, slow and fast variables are separated and incorporated into the stability analysis of the overall closed-loop system, and a set of sufficient conditions for the stability of the total system is derived. Finally, the effectiveness of the proposed control law is verified through simulations.

This paper proposes a method for mobile robot exploration based on the idea of frontier exploration which suggests navigating the robot toward the boundaries between free and unknown areas in the map. A global occupancy grid map of the environment is constantly updated, based on which a global frontier map is calculated. Then, a histogram based approach is adopted to cluster frontier cells and score these clusters based on their distance from the robot as well as the number of frontier cells they contain. In each stage of the algorithm, a sub-goal is set for the robot to navigate. A combination of distance transform and A* search algorithms is utilized to generate a plausible path toward the sub-goal through the free space. This way keeping a reliable distance from obstacles is guaranteed while searching for the shortest path toward the sub-goal. When such a path is generated, a B-spline interpolated and smoothed trajectory is produced as the control reference for the mobile robot to follow. The whole process is iterated until no unexplored area remains in the map. The efficiency of the method is shown through simulated and real experiments.

In this paper, redundancy resolution of a cable-driven parallel manipulator is performed through an analytic-iterative scheme. The redundancy resolution scheme is formulated as a convex optimization problem with inequality constraints that are imposed by manipulator structure and cable dynamics. The Karush-Kuhn-Tucker theorem is used to analyze the optimization problem and to draw an analytic-iterative solution for it. Subsequently, a tractable and iterative search algorithm is proposed to implement the redundancy resolution of such redundant manipulators. Furthermore, it is shown through simulations that the worst case and average elapsed time that is required to implement the proposed redundancy resolution scheme in a closed-loop implementation is considerably less than that of other numerical optimization methods.

This paper presents an approach to the control of the KNTU CDRPM using an integrated control scheme. The goal in this approach is achieving accurate trajectory tracking while assuring positive tension in the cables. By the proposed controller, the inherent nonlinear behavior of the cable and the target tracking errors are simultaneously compensated. In this paper asymptotic stability analysis of the close loop system is studied in detail. Moreover, it is shown that the integrated control strategy reduces the tracking error by 80% compared to that of a single loop controller in the considered manipulator. The closed-loop performance of the control topology is analyzed by a simulation study that is performed on the manipulator. The simulation study verifies that the proposed controller is not only promising to be implemented on the KNTU CDRPM, but also being suitable for other cable driven manipulators.

Newly developed cable driven redundant parallel manipulators (CDRPM) have numerous advantages compared to that of the conventional parallel mechanisms. However, there exist some challenging issues in over-constrained mechanisms like CDRPMs. In contrast to serial manipulators, complexity of parallel manipulator forward kinematics (FK) is one of the main issues being under study in the control of such manipulators. Moreover, using extra sensory data is a common approach in the FK solution of rigid-linked parallel manipulators, which is considered by fewer researchers for CDRPMs. In this paper, tension force sensors of the cables are used as an extra sensor to simplify analytical solution of the FK for a planar CDRPM. To find a suitable solution, geometrical and physical characteristics of the robot are analyzed. It is shown that the proposed method provides the required accuracy and significantly improves the process time compared to the conventional methods.

Dynamic analysis of parallel manipulators plays a vital role in the design and control of such manipulators. Closed-chain kinematic structure affects the dynamics formulations by several constraints. Therefore, especially for higher degrees of freedom manipulators, manipulation of implicit and bulky dynamics formulation looses the tractability of the analysis. In this paper, a methodology and some simplification tools are introduced to achieve explicit dynamics formulation for parallel manipulators. This methodology is applied for the dynamics analysis of the most celebrated parallel manipulator, namely Stewart-Gough platform. By avoiding any recursive or component-wise derivations, the resulting dynamics formulation provides more insight for designers, and can be much easier used in any model-based control of such manipulators. In order to verify the resulting dynamics equations, Lagrange method is used to derive and compare the manipulator mass matrix. This methodology can be further used to formulate the explicit dynamics of other parallel manipulators.

In this paper a new strategy for handling the observation information of a bearing-range sensor throughout the filtering process of EKF-SLAM is proposed. This new strategy is advised based on a thorough consistency analysis and aims to improve the process consistency while reducing the computational cost. At first, three different possible observation models are introduced for the EKF-SLAM solution for a robot equipped with a bearing-range sensor. General form of the covariance matrix and the level of inconsistency in the robot orientation estimate is then calculated for these variants, and based on the numerical comparison of the estimation results, it is proposed to use the bearing and range information of a feature in the initialization step of EKF-SLAM. However, it is recommended to use only the bearing information to perform other iteration steps. The simulation observations verify that the new strategy yields to more consistent estimates both for the robot and the features. Moreover, through the proposed consistency analysis, it is shown that since the source of consistency improvement is independent from the choice of the motion model, it gives us an advantage over other existing methods that assume a specific motion models for consistency improvement.

In this paper the dynamic analysis of a cable-driven parallel manipulator is studied in detail. The manipulator architecture is a simplified planar version adopted from the structure of large adaptive reflector (LAR), the Canadian design of next generation giant radio telescopes. This structure consists of a parallel redundant manipulator actuated by long cables. The dynamic equations of this structure are nonlinear and implicit. Long cables, large amounts of impelling forces and high accelerations raise more concern about the elasticity of cables during dynamic analysis, which has been neglected in the preceding works. In this paper, the kinematic analysis of such manipulator is illustrated first. Then the nonlinear dynamic of such mechanism is derived using Newton-Euler formulation. Next a simple model for cable dynamics containing elastic and damping behavior is proposed. The proposed model neither ignores longitude elasticity properties of cable nor makes dynamic formulations heavily complicated like previous researches. Finally, manipulator dynamic with cable dynamic is derived, and the cable elasticity effects are compared in a simulation study. The results show significant role of elasticity in a cable-driven parallel manipulator such as the one used in LAR mechanism.

This paper proposes a method for augmenting the information of a monocular camera and a range finder. This method is a valuable step towards solving the SLAM problem in unstructured environments free from problems of using encoderspsila data. Proposed algorithm causes the robot to benefit from a feature-based map for filtering purposes, while it exploits an accurate motion model, based on point-wise raw range scan matching rather than unreliable feature-based range scan matching, in unstructured environments. Moreover, robust loop closure detection procedure is the other consequence of this method. Experiments with a low-cost IEEE 1394 webcam and a range finder illustrate the effectiveness of the proposed method in drift-free SLAM at loop closing motions in unstructured environments.

KNTU CDRPM is a cable driven redundant parallel manipulator, which is under investigation for possible high speed and large workspace applications. This newly developed mechanisms have several advantages compared to the conventional parallel mechanisms. Its rotational motion range is relatively large, its redundancy improves safety for failure in cables, and its design is suitable for long-time high acceleration motions. In this paper, collision-free workspace of the manipulator is derived by applying fast geometrical intersection detection method, which can be used for any fully parallel manipulator. Implementation of the algorithm on the Neuron design of the KNTU CDRPM leads to significant results, which introduce a new style of design of a spatial cable-driven parallel manipulators. The results are elaborated in three presentations; constant-orientation workspace, total orientation workspace and orientation workspace.

This paper is devoted to the control of a cable driven redundant parallel manipulator, which is a challenging problem due the optimal resolution of its inherent redundancy. Additionally to complicated forward kinematics, having a wide workspace makes it difficult to directly measure the pose of the end-effector. The goal of the controller is trajectory tracking in a large and singular free workspace, and to guarantee that the cables are always under tension. A control topology is proposed in this paper which is capable to fulfill the stringent positioning requirements for these type of manipulators. Closed-loop performance of various control topologies are compared by simulation of the closed-loop dynamics of the KNTU CDRPM, while the equations of parallel manipulator dynamics are implicit in structure and only special integration routines can be used for their integration. It is shown that the proposed joint space controller is capable to satisfy the required tracking performance, despite the inherent limitation of task space pose measurement.

In this paper, a controller design method for flexible joint robots (FJR), considering actuator saturation is proposed and its robust stability is thoroughly analyzed. This method consists of a composite control structure, with a PD controller on the fast dynamics and a PID controller on slow dynamics. Moreover, the need of powerful actuator is removed by decreasing the bandwidth of the fast controller during critical occasions, with the use of a supervisory loop. Fuzzy logic is used in the supervisory law, in order to adjust the proper gain in the forward path. It is then shown that UUB stability of the overall system is guaranteed in presence of uncertainties, provided that the PD and the PID gains are tuned to satisfy certain conditions.

Adaptive robust controller is proposed for read/write head systems for hard disk drives (HDD). This structure can be applied to both track seeking and track following modes, and it makes the mode switching control algorithms proposed in conventional HDD servo system unnecessary. This controller theoretically guarantees a prescribed transient performance and tracking in presence of parametric uncertainties. An improved desired compensation ARC (IDCARC) scheme is then proposed, which has powered by a dynamic adaptive term compared to DCARC. The regressor is calculated using reference trajectory information. This has been done by structural vibration minimized acceleration trajectory control method. Simulation result show that the dynamic adaptation mechanism in IDCARC provide better performance compared to that of ARC, DCARC and the conventional servo system with mode switches control law.

In this paper a new and completely linear algorithm is proposed for composite robust control of flexible joint robots. Moreover, the robust stability of the closed loop system in the presence of structured and unstructured uncertainties is analyzed. To introduce the idea, flexible joint robot with structured and unstructured uncertainties is modelled and converted into singular perturbation form. A robust linear control algorithm is proposed for the slow dynamics and its robust stability conditions are derived using Thikhonov's theorem. Then the robust stability of the total system considering the proposed composite controller is analyzed, and sufficient conditions for robust stability of system is obtained. Finally the effectiveness of the proposed controller is verified through simulations. It is shown that not only the tracking performance of the proposed controller is very suitable, but also the actuator effort is much smaller than previous result.

In this paper the control of flexible joint manipulators is studied in detail. A composite control algorithm is proposed for the flexible joint robots, which consists of two main parts. Fast control, u/sub f/, which guarantees that the fast dynamics remains asymptotically stable, and the corresponding integral manifold remains invariant. Slow control, u/sub s/, itself consists of a robust PID designed based on the rigid model, and a corrective term designed based on the reduced flexible model. The stability of the overall closed loop system is proved to be UUB stable, by Lyapunov stability analysis. Finally, the effectiveness of the proposed control law is verified through simulations. It is shown that the proposed control law ensure the robust stability and performance, despite the modeling uncertainties.

In this paper a robust composite control algorithm is proposed for flexible joint manipulators, with the emphasis on satisfying control effort limitations. An H/sub /spl infin// framework is used for the slow subsystem controller design, instead of robust PID synthesis introduced in the literature. Linear identification techniques are used to represent the nonlinear dynamics of the system into a linear model plus multiplicative uncertainty. An H/sub /spl infin// controller is designed in the framework of composite control, in order to optimize the required control effort, along with satisfying robust stability and desirable performance. The effectiveness of the proposed control law is compared with other methods through a simulation study. The comparison results show a significant improvement in control effort, while satisfying both stability and performance requirements.

The control of flexible joint manipulators is studied in detail. The model of N-axis flexible joint manipulators is derived and reformulated in the form of singular perturbations, and the integral manifold is used to separate fast dynamics from slow dynamics. A composite control algorithm is proposed for the flexible joint robots, which consists of two main parts. Fast control, u/sub f/, which guarantees that the fast dynamics remains asymptotically stable, and the corresponding integral manifold remains invariant. Slow control, u/sub s/, itself consists of a robust PID design based on the rigid model, and a corrective term designed based on the reduced flexible model. The stability of the overall closed loop system is proved to be UUB stable, by Lyapunov stability analysis. Finally, the effectiveness of the proposed control law is verified through simulations. It is shown that the proposed control law ensures robust stability and performance, despite the modeling uncertainties.

A harmonic drive is a compact, light-weight and high-ratio torque transmission device which has almost zero backlash. Its unique performance features captures the attention of designers in many industrial applications, especially in robotics. However, the torque control of harmonic drive systems is still a challenging problem for researchers. In this paper the torque control of an harmonic drive system for constrained motion is examined in detail. A nominal model for the system is obtained from experimental frequency responses of the system, and the deviation of the system from the model is encapsulated by multiplicative uncertainty. A robust torque controller is designed using this information in an H/sub /spl infin//-framework, and implemented on two different setups. It is illustrated that the performance features of the closed-loop system is exceptionally good, both in time and frequency domains.

A harmonic drive is a compact, light-weight and high-ratio torque transmission device which is used in many electrically actuated robot manipulators. In many robotic control strategies it is assumed that the actuator is an ideal torque source. However, converting harmonic drive systems to ideal torque sources is still a challenging control problem for researchers. In this paper the torque control of harmonic drive system under free motion is examined in detail. A built-in torque sensor is developed in order to measure the torque, and by employing a Kalman filter the undesired torque signatures like torque ripples and misalignment torque are filtered out. An empirical nominal model for the system is obtained through experimental frequency response estimates, and the deviation of the system from the model is encapsulated by multiplicative uncertainty. A robust torque controller is subsequently designed in an H/sub /spl infin//-framework and implemented employing Kalman filtered torque estimates. From time and frequency domain experiments, it is shown that the closed-loop system retains robust stability, while improving the tracking performance exceptionally well.