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. Index Terms-Fuzzy systems, parameterized linear matrix inequality (PLMI).

No abstract

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. Index Terms-Fuzzy control, linear matrix inequality (LMI), output feedback.Manuscript

No abstract

Abstract-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 5 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 degrees. 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.

No abstract

No abstract