A simple robust compliant-motion-control technique is presented for a robot manipulator with nonlinear friction. The control technique incorporates both time-delay-estimation technique and ideal velocity feedback; the former is used to cancel out soft nonlinearities, and the latter serves to reduce the effect of hard nonlinearities, including Coulomb friction and stiction. The proposed controller has a simple structure and yet provides good online friction compensation without modeling friction. The robustness of the proposed method has been confirmed through comparisons with other controllers in 2-DOF SCARA-type industrial robot experiments.

Thanks to its simplicity and robustness, time delay control (TDC) has been recognized as a simple and yet effective alternative to robot model-based controls and/or intelligent controls. An inclusive and enhanced formulation of TDC for robust control of robot manipulators is presented in this paper. The proposed formulation consists of three intuitive terms: 1) time delay estimation (TDE), inherited from the original TDC, for cancellation of mostly continuous nonlinearities; 2) nonlinear desired error dynamics (DED) (i.e., a “mass”-“nonlinear damper”- “nonlinear spring” system) injection term; and 3) a TDE error correction term based on a nonlinear sliding surface. The proposed TDC formulation has an inclusive structure. Depending on the gain/parameter set chosen, the proposed formulation can become Hsia's formulation, Jin's formulations including a type of terminal sliding mode control (SMC), an SMC with a switching signum function, or a novel enhanced formulation. Experimental comparisons were made using a programmable universal manipulator for assembly-type robot manipulator with various parameter sets for the proposed control. Among them, the highest position tracking accuracy was obtained by using a terminal sliding DED with a terminal sliding correction term.

Arm impairments in patients post stroke involve the shoulder, elbow and wrist simultaneously. It is not very clear how patients develop spasticity and reduced range of motion (ROM) at the multiple joints and the abnormal couplings among the multiple joints and the multiple degrees-of-freedom (DOF) during passive movement. It is also not clear how they lose independent control of individual joints/DOFs and coordination among the joints/DOFs during voluntary movement. An upper limb exoskeleton robot, the IntelliArm, which can control the shoulder, elbow, and wrist, was developed, aiming to support clinicians and patients with the following integrated capabilities: 1) quantitative, objective, and comprehensive multi-joint neuromechanical pre-evaluation capabilities aiding multi-joint/DOF diagnosis for individual patients; 2) strenuous and safe passive stretching of hypertonic/deformed arm for loosening up muscles/joints based on the robot-aided diagnosis; 3) (assistive/resistive) active reaching training after passive stretching for regaining/improving motor control ability; and 4) quantitative, objective, and comprehensive neuromechanical outcome evaluation at the level of individual joints/DOFs, multiple joints, and whole arm. Feasibility of the integrated capabilities was demonstrated through experiments with stroke survivors and healthy subjects.

It has been reported that, in impedance control, there exists a dilemma between impedance accuracy and robustness against modeling error. As a solution to this dilemma, an accurate and robust impedance control technique is developed based on internal model control structure and time-delay estimation: the former injects desired impedance and corrects modeling error, the latter estimates and compensates the nonlinear dynamics of robot manipulators. Owing to the simple structure, the proposed control is designed without requiring entire model computation or complex algorithms. In 2-DOF SCARA-type robot experiments, the accuracy and robustness of the proposed control are confirmed through comparisons with other competent controllers including impedance control with disturbance observer.

Objective: This study aimed to decode shoulder, elbow and wrist dynamic movements continuously and simultaneously based on multi-channel surface electromyography signals, useful for electromyography controlled exoskeleton robots for upper-limb rehabilitation. Methods: Ten able-bodied subjects and ten stroke subjects were instructed to voluntarily move the shoulder, elbow and wrist joints back and forth in a horizontal plane with an exoskeleton robot. The shoulder, elbow and wrist movements and surface electromyography signals from six muscles crossing the joints were recorded. A set of three parallel linear-nonlinear cascade decoders was developed to continuously estimate the selected shoulder, elbow and wrist movements based on a generalized linear model using the anterior deltoid, posterior deltoid, biceps brachii, long head triceps brachii, flexor carpi radialis, and extensor carpi radialis muscle electromyography signals as the model inputs. Results: The decoder performed well for both healthy and stroke populations. As movement smoothness decreased, decoding performance decreased for the stroke population. Conclusion: The proposed method is capable of simultaneously and continuously estimating multi-joint movements of the human arm in real-time by characterizing the nonlinear mappings between muscle activity and kinematic signals based on linear regression. Significance: This may prove useful in developing myoelectric controlled exoskeletons for motor rehabilitation of neurological disorders.