Manipulator compliance based on joint torque control

We examined the patterns of joint kinematics and torques in two kinds of sagittal plane reaching movements. One consisted of movements from a fixed initial position with the arm partially outstretched, to different targets, equidistant from the initial position and located according to the hours of a clock. The other series added movements from different initial positions and directions and >40-80 cm distances. Dynamic muscle torque was calculated by inverse dynamic equations with the gravitational components removed. In making movements in almost every direction, the dynamic components of the muscle torques at both the elbow and shoulder were related almost linearly to each other. Both were similarly shaped, biphasic, almost synchronous and symmetrical pulses. These findings are consistent with our previously reported observations, which we termed a linear synergy. The relative scaling of the two joint torques changes continuously and regularly with movement direction. This was confirmed by calculating a vector defined by the dynamic components of the shoulder and elbow torques. The vector rotates smoothly about an ellipse in intrinsic, joint torque space as the direction of hand motion rotates about a circle in extrinsic Cartesian space. This confirms a second implication of linear synergy that the scaling constant between the linearly related joint torques is directionally dependent. Multiple linear regression showed that the torque at each joint scales as a simple linear function of the angular displacement at both joints, in spite of the complex nonlinear dynamics of multijoint movement. The coefficients of this function are independent of the initial arm position and movement distance and are the same for all subjects. This is an unanticipated finding. We discuss these observations in terms of the hypothesis that voluntary, multiple degrees of freedom, rapid reaching movements may use rule-based, feed-forward control of dynamic joint torque. Rule-based control of joint torque with separate dynamic and static controllers is an alternative to models such as those based on the equilibrium point hypotheses that rely on a positionally based controller to produce both dynamic and static torque components. It is also an alternative to feed-forward models that directly solve the problems of inverse dynamics. Our experimental findings are not necessarily incompatible with any of the alternative models, but they describe new, additional findings for which we need to account. The rules are chosen by the nervous system according to features of the kinematic task to couple muscle contraction at the shoulder and elbow in a linear synergy. Speed and load control preserves the relative magnitudes of the dynamic torques while directional control is accomplished by modulating them in a differential manner. This control system operates in parallel with a positional control system that solves the problems of postural stability.

Design of a digital adaptive algorithm for torque-controlled robotic joints

Precise position force control is the core and difficulty of robot technology, especially for robots with redundant degrees of freedom. For example, track-based controls often fail to grind the robot due to the intolerable impact force applied to the end-effector. The main difficulties lie in the coupling of motion and contact forces, redundancy analysis and physical constraints. In this chapter, we propose a new motion force control strategy under the framework of recursive neural network. The tracking error and contact force are described respectively in the orthogonal space. By choosing the minimum joint torque as the secondary task, the control problem is transformed into the QP problem under multi-constraint conditions. In order to obtain real-time optimization of the joint torque relative to the non-convex joint angle, the original QP is reconstructed at the velocity level, and the original objective function is replaced by the time derivative. Then a convergent dynamic neural network is established to solve the improved QP problem online. The robot position control based on recursive neural network is extended to the robot position control based on position force, which opens a new way for the robot to turn from simple control angle to crossover design with convergence and optimality. Numerical results show that the proposed method can realize precise position force control, deal with inequality constraints such as joint angular velocity and torque limitation, and reduce joint torque consumption by 16% on average.

This paper examines the effects of errors in applied force on robot force-controlled task performance. Two configuration-dependent effects of force errors are identified. Kinematic stability errors occur when the robot configuration is such that small inaccuracies in joint torque create relatively large robot link displacements. This sensitivity to torque errors can seriously inhibit task performance in certain configurations. Task performance can also be affected by errors in the endpoint force angle which can be significant for even small changes in joint torque. Performance maps are developed as tools to identify robot configurations sensitive to these errors. The effects of force errors are demonstrated in the performance of a planar peg and hole task by a three link manipulator under joint torque control. >

There are many tasks which require robotic manipulators interaction with environment. It consists of three control problems, i.e., position control, impact control and force control. The position control means the way of reaching to the environment. The moment of touching to the environment yields the impact control problem and the force control is to maintain the desired force trajectory after the impact with the environment. These three control problems occur in sequence, so each control algorithm can be developed independently. Especially for redundant manipulators, each of these three control problems has been important independent research topic. For example, joint torque minimization and impulse minimization are typical techniques for such control problems. The three control problems are considered as a single task in this paper. The position control strategy is developed to improve the performance of the task, i.e., minimization of the individual joint torques and impulse. Therefore, initial conditions of the impact control problem are optimized at the previous position control algorithm. Such a control strategy yields improved result of the impact control. Similarly, the initial conditions for the force control problem are indirectly optimized by the previous position control and impact control strategies. The force control algorithm uses the individual joint torque minimization concept. It also minimizes the force disturbances. The simulation results show the proposed control strategy works well.

We have proposed two different digital control methods, where the controlled variables are joint torque and joint angular velocity, of space robot manipulators using the transpose of Generalized Jacobian Matrix. These methods are developed in discrete time domain so that the stability of system can be guaranteed when digital computers are used as controllers. The relation between the two methods, however, is not reported, although the basic concepts of these methods ar similar. This paper presents a novel digital control method, which integrates joint torque control with joint velocity control, of nonredundant space robot manipulators using the transpose of Generalized Jacobian Matrix. The merit of this new control law is to be applicable to both joint's torque controller and joint's velocity controller by only tuning the controller gains. Furthermore, computer simulation of a 6-DOF space robot manipulator is performed. Simulation result demonstrates the effectiveness of the proposed method.

A humanoid robot operated with position control has the characteristic of high trajectory tracking accuracy. However, its ability to adapt to complex environments is insufficient during walking due to the rigidity of the joint. More complex methods of control are needed when walking on uneven ground. A humanoid robot operated using torque control has strong robustness to complex environments or disturbance, but the flexibility of the joint makes its operation imprecise. The study of human motion reveals that the flexibility of the ankle joint allows people to walk in complex environments, such as uneven ground, with great stability. This paper presents a stable walking strategy for a humanoid robot based on ankle joint torque control. In this method, a humanoid ankle joint with two degrees of freedom is used for joint torque control. We describe a feed-forward torque calculation method and the setting of controller gain parameters. An algorithm compliant in the z-direction was designed for swinging legs. Finally, the BHR-6P model was used to simulate and verify the proposed algorithm.

BackgroundFunctional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES.MethodsPrevious works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation.ResultsPrediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement.ConclusionThe proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.

This paper presents a stability analysis of force-position teleoperation under general end-effector contact. The analysis is based on the finite and infinite zero structure of the multivariable root-locus resulting from modulation of the environment stiffness. The starting point is an analysis of the stability of robot force control, motivated by the observation that the human-operator in a force reflection loop acts as a “force servo,” generating position commands in response to reflected force. Asymptotic root loci properties are used to establish passivity conditions on force feedback to give root locus interpretations of the well-known results that (1) feedback via the inverse joint Jacobian can lead to (kinematic) instability and that (2) passivity is preserved by kinematically proper force feedback through the transpose of the joint angle Jacobian. It is demonstrated that a fully constrained force-position teleoperation loop has an identical infinite zero structure to that of a slave manipulator under kinematically proper force control and that the dominant vibration modes of a force-position loop are fully described by a multivariable analogue of the single-input single-output pseudo-system investigated in a study by Daniel and McAree. Extension of the analysis to cover partial end-effector constraint provides a design tool for teleoperation control and serves to aid selection of teleoperation slave-arms. The paper concludes by giving a passivity condition for multiple-input multiple-output force-position teleoperation for stable contact against all environments.

A sliding mode control (SMC) approach was used in a pneumatic force control servo-system with uncertainties and external disturbances in this paper. To reach exact force control in pneumatic force servo-system, the E/P pressure proportional valve was used as an electro-pneumatic converter in constructing an electro-pneumatic force control servo system. The dynamic model of the pneumatic force servo system was built firstly, and then a boundary layer approach was used to the design of controller, in which using a saturation function to substitute for the sign function based on Lyapunov theory. Finally, the performance of the controller was simulated and tested. The results show that the system performance has been much improved when compared with a conventional PID controller, and it has a good control precision and robustness

The objective of this study was to understand how people adapt to errors when using a myoelectric control interface. We compared adaptation across 1) non-amputee subjects using joint angle, joint torque, and myoelectric control interfaces, and 2) amputee subjects using myoelectric control interfaces with residual and intact limbs (five total control interface conditions). We measured trial-by-trial adaptation to self-generated errors and random perturbations during a virtual, single degree-of-freedom task with two levels of feedback uncertainty, and evaluated adaptation by fitting a hierarchical Kalman filter model. We have two main results. First, adaptation to random perturbations was similar across all control interfaces, whereas adaptation to self-generated errors differed. These patterns matched predictions of our model, which was fit to each control interface by changing the process noise parameter that represented system variability. Second, in amputee subjects, we found similar adaptation rates and error levels between residual and intact limbs. These results link prosthesis control to broader areas of motor learning and adaptation and provide a useful model of adaptation with myoelectric control. The model of adaptation will help us understand and solve prosthesis control challenges, such as providing additional sensory feedback.

We propose a probe scanning support system based on force/visual servoing control for robotic echography. First, we have designed and formulated its inverse kinematics the construction of mechanism. Next, we have developed a scanning method of the ultrasound probe on body surface to construct visual servo system based on acquired echogram by the standalone medical robot to move the ultrasound probe on patient abdomen in three-dimension. The visual servo system detects local change of brightness in time series echogram, which is stabilized the position of the probe by conventional force servo system in the robot, to compensate not only periodical respiration motion but also body motion. Then we integrated control method of the visual servo with the force servo as a hybrid control in both of position and force. To confirm the ability to apply for actual abdomen, we experimented the total system to follow the gallbladder as a moving target to keep its position in the echogram by minimizing variation of reaction force on abdomen. As the result, the system has a potential to be applied to automatic detection of human internal organ.

The best exoskeleton control strategy depends on the task, motivating the development and analysis of an exoskeleton capable of position, force, and impedance control. In this paper, we implement and compare the three controller types via sliding mode control on a custom-built pneumatic cylinder ankle exoskeleton. To evaluate each controller's performance, the exoskeleton was tested on the benchtop and with human subject experiments. With the position controller, the exoskeleton achieved an root-mean-square error (RMSE) of under 5 deg for both the benchtop and human tests. It had a bandwidth of approximately 12 rad/s. The force controller tracked sinusoidal trajectories acceptably well at low frequencies (15% of force range at 6 rad/s), with a bandwidth of approximately 24 rad/s. The error was significantly higher (RMSE of 24% of force range) for the more biologically relevant trajectory. The impedance controller demonstrated the desired spring-like behavior, with position RMSE generally under 5 deg compared to expected position. All three controllers worked equally well for benchtop and human tests. Thus, a pneumatically powered ankle exoskeleton with sliding mode control is capable of accurate position and impedance control and is capable of marginally acceptable force control.

In this paper, model-based precision force and position control of an ionic polymer metal composite (IPMC) is presented. A 23.8 mm × 3.4 mm × 0.16 mm IPMC strip was used as an actuator in a cantilever configuration. Open-loop force and position responses of an IPMC are not repeatable, and hence closed-loop precision control is of critical importance to ensure proper functioning, repeat-ability and reliability. After feedback controllers were designed and implemented with empirically obtained fourth-order plant transfer functions, the overshoot decreased from 460 to 2.8 per cent and the settling time was reduced from 37.5 to 3.22 s in force control. In position control the overshoot decreased from 333 to 20.3 per cent and the settling time was reduced from 21.5 to 2.56 s. Microscale precision force and position control capabilities of the IPMC actuator were also demonstrated experimentally. An 8 μN force resolution was achieved with a force noise of 0.5μN r.m.s., and the position resolution was 6 μm with a position noise of 2.5 μm r.m.s. The maximum force and tip displacement achieved with the IPMC actuator under closed-loop control were 2 mN and 5 mm respectively. The IPMC actuator could follow various commanded force and position trajectories such as sinusoidal and trapezoidal position profiles, and a velocity profile with a 3 mm/s maximum velocity. A novel hybrid force and position control strategy demonstrated its utility in practical micromanipulalion applications where the actuator force must be limited to prevent damaging micro-objects. High-precision control of the IPMC at low force level proved its potential for micromanufacturing and micromanipulation applications such as robotic and biomedical microgrippers.

Joint position control is a dominant paradigm in industrial robots. While it has been successful in various industrial tasks, joint position control is severely limited in performing advanced robotic tasks, especially in unstructured dynamic environments. This paper presents the concept of torque-to-position transformer designed to allow the implementation of joint torque control techniques on joint position-controlled robots. Robot torque control is essentially accomplished by converting desired joint torques into instantaneous increments of joint position inputs. For each joint, the transformer is based on the knowledge of the joint position servo controller and the closed-loop frequency response of that joint. This transformer can be implemented as a software unit and applied to any conventional position-controlled robot so that torque command to the robot becomes available. This approach has been experimentally implemented on the Honda ASIMO robot arm. The paper presents the results of this implementation which demonstrate the effectiveness of this approach.