This article presents a locomotion controller for lower limb exoskeletons so as to enable the combined robot and user system to exhibit compliant walking characteristics when interacting with the environment. This is of critical importance to reduce the excessive ground reaction forces during the walking task execution with the aim of improved environmental interaction capabilities. In robot-aided walking support for paraplegics, the user has to actively use his/her upper limbs via crutches to ensure overall balance. By virtue of this requisite, several issues may particularly arise during touchdown instants, e.g., upper body orientation fluctuates, shoulder joints are subject to excessive loading, and arms may need to exert extra forces to counterbalance these effects. In order to reduce the upper body effort via compliant locomotion, the controller is designed to manage the force/position tradeoff by using an admittance controller in each joint. For proof of concept, a series of exoskeleton-aided walking experiments were conducted with the participation of nine healthy volunteers, four of whom additionally walked on an irregular surface for further performance evaluation. The results suggest that the proposed locomotion controller is advantageous over conventional high-gain position tracking in decreasing undesired oscillatory torso motion and total arm force, adequately reducing the required upper body effort.

This paper addresses the problem of assist-as-needed (AAN) control for rehabilitation robots. To achieve a motion which is not explicitly a function of time, the velocity field control is considered in this paper. The proposed new controller consists of a proportional-like feedback term and a neural network (NN) term, where the later is exploited to compensate for the dynamic uncertainties of the system. The AAN property is facilitated by means of a dead-zone function in the feedback control term and a forgetting factor in the adaptation law of NN component. The designed controller guarantees the stability of the system with a bounded control command. The performance of the proposed AAN scheme is validated through the simulation and experiment conducted on a lower-limb exoskeleton.

Due to ill-defined and poorly understood dynamics of flapping wing flying robots, acquiring high-fidelity aero dynamical and control models for such aerial robots are challenging and often infeasible. In this work, we propose using a non-linear, model-free approach that eliminates errors arising from the mis-modeling or under-modeling of flapping dynamics. These procedures require no prior knowledge or model of any of the dynamical aspects of the flying robot and assume zero statistical knowledge about noise affecting sensor measurements. Intelligent PID (iPID) controllers are used to provide an abstracted treatment of all unknowns and uncertainties in the robot by encapsulating them together into a single term. The problem then devolves into the estimation of this term by either direct or indirect means which is more practical than obtaining high-fidelity models. For indirect estimation, numerical differentiation is required and we propose using a Haar-wavelet differentiator that is capable of smoothing and differentiating signals irrespective of the multivariate noise corrupting the signal. The direct estimation method uses an algebraic and non-asymptotic method. Pitch control and altitude control experiments are successfully performed on a real, bird-like flapping wing flying robot using all different approaches; results obtained reveal that indirect estimation using the Haar-wavelet differentiator provides slightly better performance.

Recognizing the human gait is an edge research in robotics and rehabilitation. It has been popular to recognize the human gait from kinematic data. However, the recognition from muscle activities, the input of the movement, has not been widely approached. In this paper, we propose a framework to recognize the human walking and running movements by investigating muscle activities through electromyography (EMG). The framework is a Hidden Markov Model (HMM) topology utilizing Locally Linear Embedding (LLE) technique to extract feature vectors. We show that: (1) the high-dimensional EMG data can be embedded into a lower-dimensional space by using a manifold learning algorithm (LLE), primitive components which give meaningful representation of the EMG can be extracted, and (2) our proposed HMM topology whose input are the extracted vectors from EMG can recognize the gait movement at an accuracy rate of over 80%.

This paper presents a wearable upper body exoskeleton system with a model-based compensation control framework to support robot-aided shoulder-elbow rehabilitation and power assistance tasks. To eliminate the need for EMG and force sensors, we exploit off-the-shelf compensation techniques developed for robot manipulators. Thus, target rehabilitation tasks are addressed by using only encoder readings. A proof-of-concept evaluation was conducted with live able-bodied participants. The patient-active rehabilitation task was realized via observer-based user torque estimation, in which resistive forces were adjusted using virtual impedance. In the patient-passive rehabilitation task, the proposed controller enabled precise joint tracking with a maximum positioning error of 0.25°. In the power assistance task, the users' muscular activities were reduced up to 85% while exercising with a 5 kg dumbbell. Therefore, the exoskeleton system was regarded as being useful for the target tasks, indicating that it has a potential to promote robot-aided therapy protocols.

This paper introduces a position-based compliance control algorithm that can be implemented in a lower extremity exoskeleton-supported paraplegia walking task, in which upper body has to be utilized to maintain the overall balance. In order to reduce the upper body effort required during the task, the controller is designated to be capable of managing the position/force trade-off in conjunction with an active admittance regulator scheme. In the case of no force errors, the controller prioritizes position tracking in a way to achieve walking support. Once the force error increases (e.g., ground reaction force peaks, unexpected disturbances, stepping on an object, etc.) the position reference is updated in accordance with the force constraints and active admittance characteristics. By the virtue of this strategy, the human-robot system exhibits enhanced environmental interaction capabilities; therefore, the subject can maintain the overall balance with relatively less upper body effort while walking. Implementing the proposed method, we conducted robot-assisted walking experiments on 4 able-bodied subjects with different body mass index levels and genders. Subjects were instructed to be in passive mode. In addition, walking with severe obstacles was also experimented on a single able-bodied subject. In conclusion, the proposed method enabled us to yield enhanced walking performance comparing to classical rigid position control scheme; indicating that it could potentially introduce a compliant locomotion control alternative for the paraplegia walking support task with a comparatively less amount of upper body effort requirements.

Parallel manipulators are in demand for high-speed industrial applications due to low inertia and high stiffness. However, vibration may still occur that limits the utilization of these manipulators for the operations at high speed. In this study, we cope with the residual vibration of the 3P(S)4parallel mechanism that occurs at the end of high-speed motion. First, by using Lagrangian formulation, inverse dynamics of the mechanism is analyzed to linearize the system. To achieve robust control against residual vibration at high speed, Quantitative Feedback Theory is applied that utilizes templates and offers design transparency. As the challenge in this study is vibration, this frequency-domain robust control design approach has a potential to solve it. Verification of the applied method is conducted through high-speed motion experiments with PID and QFT methods. Settling time with QFT is 72% smaller compare to PID control.

This paper is authored to describe a control framework that is designated for realizing cyclic, actively-compliant and dynamically-balanced jumping and trotting quadruped locomotion over rough terrain. In order to succeed in exhibiting such locomotion abilities, two controllers are synthesized: i) Active Compliance Control via force feedback, ii) Angular Momentum Control via gyro sensing. The first controller computes the joint displacements that are associated with ground reaction force errors, using Jacobian transpose and admittance blocks. Together with position constraints, these joint displacements are simultaneously fed-back to local servo controllers; allowing the robot to perform the given locomotion task in an actively-compliant manner. The second controller, in the meantime, evaluates gyro sensor information to calculate the required compensation torque about center of mass, which is necessary to regulate upper torso rotational motion. Afterwards, it updates the orientation input in accordance with this compensation torque. Using the proposed framework, the overall control performance is tested via cyclic jumping and trotting motion experiments, conducted over rough terrain with a stiff-by-nature quadruped robot. Results turn out to be positive; the robot demonstrates successful jumping and trotting cycles in a repetitive, actively-compliant and dynamically-balanced fashion.

This paper is aimed at presenting a framework that consists of a pattern generator and a controller, which are combined together to realize continuous and dynamically equilibrated running motion on a 4-link 3-DoF one-legged robot with no passively compliant elements. Initially, we make use of a pattern generator to synthesize dynamically-consistent running trajectories in which the rotational inertia and the associated angular momentum term are characterized. As for the controller, ground reaction force constraints are imposed to the system indirectly. For this purpose, joint torque values that are corresponding to horizontal and vertical force errors are computed. Subsequently, they are inserted to an admittance filter block to obtain the associated joint displacements. These joint displacements are then fed-back to local servo controllers to implement indirect force feedback control in an actively compliant manner. Additionally, friction compensation and foot orientation controller blocks are added to enhance the system performance. In order to validate the method, running experiments are conducted on the actual one-legged robot. As the result, we satisfactorily obtained continuous, dynamically equilibrated and repetitive running cycles.

A new fuzzy modeling based on fuzzy linear fractional transformations model is introduced. This new representation is shown to be a flexible tool for handling complicated nonlinear models. Particularly, the new fuzzy model provides an efficient and tractable way to handle the output feedback parallel distributed compensation problem. We demonstrate that this problem can be given a linear matrix inequality characterization and hence is immediately solvable through available semidefinite programming codes. The capabilities of the new fuzzy modeling is illustrated through numerical examples.

This paper proposes different parameterized linear matrix inequality (PLMI) characterizations for fuzzy control systems. These PLMI characterizations are, in turn, relaxed into pure LMI programs, which provides tractable and effective techniques for the design of suboptimal fuzzy control systems. The advantages of the proposed methods over earlier ones are then discussed and illustrated through numerical examples and simulations.

In this paper, a simple control methodology for macro/micro manipulator system is proposed. The macro/micro manipulator system consists of a multi-link rigid body manipulator as a macro manipulator and a machining unit mounted on the end-effector of the macro manipulator as a micro manipulator. It is shown that the state equations of the proposed macro/micro manipulator system are identical with the singular perturbed system. A simple method of control system design of this system can be derived from the singular perturbation theory. Simulation results show that the proposed control scheme perfectly compensates the tracking errors. An experimental verification of the proposed control algorithm is shown by applications to electrodischarge machining.>