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Fuzzy radial-based impedance controller design for lower limb exoskeleton robot
Abstract
The lower extremity rehabilitation exoskeleton is mainly used to help patients with movement disorders complete rehabilitation training. For the human-machine interaction problem of the lower limb rehabilitation exoskeleton, a fuzzy radial-based impedance (RBF-FVI) controller is proposed in this study. A six degree of freedom (DOF) lower extremity rehabilitation exoskeleton was developed, and the human-machine coupling dynamics model was established. To realize the compliance control of the human-machine coupling system, a novel RBF-FVI controller is designed, which includes an inner-loop fuzzy position control module and an outer-loop impedance control module. The inner-loop fuzzy position control module is mainly used to achieve the tracking control of the desired training trajectory and position adjustment amount. The outer-loop impedance control module regulates the impedance parameters and compensates for the uncertainty terms. The superiority of the proposed controller in trajectory following is verified through simulation and comparison tests. The hardware test of the human-machine coupling system was carried out, and the test results showed that the subject and the exoskeleton system could realize a coordinated and smooth movement.
... Moreover, in the case of variable impedance control, there has been a recent surge in the popularity of soft computing and deep learning techniques for enabling various levels of assistance and resistance in patient care. These techniques include fuzzy logic [59,76] and fuzzy-based radial basis function networks [71]. Zhang et al. [71] proposed a fuzzy radial-based impedance controller for the human-machine interaction problem in lower extremity rehabilitation exoskeletons. ...
... These techniques include fuzzy logic [59,76] and fuzzy-based radial basis function networks [71]. Zhang et al. [71] proposed a fuzzy radial-based impedance controller for the human-machine interaction problem in lower extremity rehabilitation exoskeletons. The proposed controller consisted of an inner-loop fuzzy position control module for trajectory tracking and position adjustment and an outer-loop impedance control module for regulating impedance parameters and compensating for uncertainties. ...
Over the last decade, lower limb exoskeletons have seen significant development, with a particular focus on improving the interaction between the subject and the exoskeleton. This has been achieved by implementing advanced control strategies that enable the safe and efficient use of the exoskeleton. In this work, the control strategies for lower limb exoskeletons are divided into upper-level control (supervisory and high-level control) and lower-level control (the servo layer). Before discussing these control strategies, a brief introduction to lower limb exoskeletons and their control schemes is provided. The control hierarchy for lower limb exoskeletons is then systematically reviewed along with an overview of the techniques used. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement is used to highlight the systematic process of identifying relevant articles with inclusion and exclusion criteria. The details of supervisory control, high-level control, and servo control strategies are presented by citing relevant state-of-the-art studies, particularly from the past five years. The targeted lower limb joint, training mode, and development stage for different control strategies are highlighted in a tabulated form to articulate the overall hierarchy level. Finally, the potential opportunities and limitations of subject-cooperative control are discussed. Overall, this work aims to provide an in-depth understanding of the control strategies used in lower limb exoskeletons, focusing on subject cooperation. This knowledge can be used to improve the safety and efficacy of lower limb exoskeletons, ultimately benefiting individuals with mobility impairments.
... As mentioned previously, the aim of this work is to evaluate the current technologies available in Egypt to start localization of manufacturing the lower limb. This evaluation depends accordingly on the TRL of each type of the prosthetic feet prosthesis illustrated in Table 2. Based on previous literature 12,14,16,24,25,26 , Table 2 presents the four types of feet according to the timeline accompanied with their descriptions in terms of their advantages and disadvantages. The TRL of each type is estimated regarding the manufacturing capabilities in the Egyptian market as shown in \* MERGEFORMAT Table 3. TRL of the first type is 7 and the technology required for the CF foot is considered "not advanced". ...
Amputation levels in Egypt and the surrounding neighborhood require a state intervention to localize the manufacturing of prosthetic feet. Amputations are mainly due to chronic diseases, accidents, and hostilities’ casualties. The prosthetic foot type is traditionally classified according to the number of axial rotational movements, and is recently classified according to the energy activeness of the foot. The localization of this industry needs a preliminary survey of the domestic technological levels with respect to the foot type. Upon the results of this survey, the energy storage response foot has appealing metrics to proceed with its manufacturing. A prototype manufacturing chain is designed and a set of these feet with a certain commercial size of 27 is manufactured. Resin impregnation technology for carbon fiber composites is followed in this work. The feet are tested according to ISO 22,675. Based on the dimensional and mechanical results, a manufacturing value chain is proposed with the prospective resin transfer molding technology. This value chain will guarantee the required localization as well as the natural growth of this value chain with all related activities like accreditation of practices as well as manpower certification.
Lower limb rehabilitation exoskeleton robots connect with the human body in a wearable way and control the movement of joints in the gait rehabilitation process. Among treadmill-based lower limb rehabilitation exoskeleton robots, Lokomat (Hocoma AG, Volketswil, Switzerland) has 4 actuated joints for bilateral hips and knees whereas Walkbot (P&S Mechanics, Seoul, Korea) has 6 bilateral actuated joints for bilateral hips, knees, and ankles. Lokomat and Walkbot robotic gait training systems have not been directly compared previously. The present study aimed to directly compare Lokomat and Walkbot robots in non-ambulatory chronic patients with acquired brain injury (ABI).
The authors conducted a single-center, retrospective, cross-sectional study of 62 subjects with ABI who were admitted to the rehabilitation hospital. Patients were divided into 2 groups: Lokomat (n = 28) and Walkbot (n = 34). Patients were subjected to robot-assisted gait training (RAGT) combined with conventional physical therapy for a total of 14 (8–36) median (interquartile range) sessions. Baseline characteristics, including age, sex, lag time post-injury, ABI type, paralysis type, intervention sessions, lower extremity strength, spasticity, and cognitive function were assessed. Functional ambulation category (FAC) and Berg balance scale (BBS) were used for outcome measures.
There were no significant differences in baseline characteristics between the groups. Baseline FAC score was 1 (0–2) in Lokomat and 1 (0–1) in Walkbot group. After the intervention, FAC scores improved significantly to 2 (1–3) in both groups (P < .05). Lokomat and Walkbot groups showed significantly enhanced BBS from 5 (2.75–24.25) and 15 (4–26.5) to 15 (4–26.5) and 22 (12–40), respectively (P < .05). Degree of improvements in both group were not significantly different with regard to balance (P = .56) and ambulatory ability (P = .74).
This study indicates that both Locomat and Walkbot robotic gait training combined with conventional gait-oriented physiotherapy are promising intervention for gait rehabilitation in patients with chronic stage of ABI who are not able to walk independently.
The lower limb exoskeleton investigated in this work actively supports the knee and hip and is intended to provide full motion support during gait. Parallel elastic actuators are integrated into the hip joints to improve the energy efficiency in gait. The prototype was tested in sit-to-stand and gait trials, in which the actuators were cascade-controlled with position trajectories. The compliant actuation of the hip in gait experiments proved to be more efficient; the peak torque was reduced by up to 31% and the RMS power was reduced by up to 36%.
A new adaptive impedance, augmented with backstepping control, time-delay estimation, and a disturbance observer, was designed to perform passive-assistive rehabilitation motion. This was done using a rehabilitation robot whereby humans’ musculoskeletal conditions were considered. This control scheme aimed to mimic the movement behavior of the user and to provide an accurate compensation for uncertainties and torque disturbances. Such disturbances were excited by constraints of input saturation of the robot’s actuators, friction forces and backlash, several payloads of the attached upper- limb of each patient, and time delay errors. The designed impedance control algorithm would transfer the stiffness of the human upper limb to the developed impedance model via the measured user force. In the proposed control scheme, active rejection of disturbances would be achieved through the direct connection between such disturbances from the observer’s output and the control input via the feedforward loop of the system. Furthermore, the computed control input does not require any precise knowledge of the robot’s dynamic model or any knowledge of built-in torque-sensing units to provide the desirable physiotherapy treatment. Experimental investigations performed by two subjects were exhibited to support the benefits of the designed approach
In accordance with the movement coordination principle of both lower limbs, a complete radial basis functions neural network based adaptive sliding mode control strategy (RBFVSMC) is proposed. The movement information on the non-affected side of patients is detected to drive the rehabilitation training. The nonlinear mathematical model of the rehabilitation robot system is firstly described. Based on the robotic dynamic model, a variable sliding mode control (VSMC) is proposed to stabilize the system. To reduce the buffeting problem caused by VSMC, the universal approximation of RBFNN is used to approach and compensate external disturbances and uncertainties. Besides, the buffeting phenomenon of sliding mode control is alleviated by replacing the sign function with a saturation function. The final asymptotic stability is guaranteed with Lyapunov criteria. Compared to proportional-integral-derivative (PID), radial basis functions neural network (RBFNN), continuous terminal SMC (CNTSMC), and decentralized adaptive robust controller (NDOBCTC), the effectiveness of the overall control scheme is demonstrated by co-simulation and human experiment in accordance to track following performance and disturbances rejection ability.
The lower limb exoskeleton provides assistance by following the lower limb joints’ desired motion trajectory. However, angle control is not enough to meet the requirements in some special circumstances such as encountering obstacles. In the swing phase of the attached leg with the exoskeleton, there is a different contact force between the sole and the road surface in different road conditions. Therefore, it is particularly important to control the joint angle and contact force simultaneously, that is, it is not only necessary to follow the desired angle but also to minimize the influence of external contact force. In this article, a novel scheme is proposed to adjust the trajectory dynamically in the swing phase. First of all, the physical model is streamlined and the Lagrangian principle is carried out to dynamic analysis and established a model of lower limb exoskeleton in the swing phase. Furthermore, the angle dynamics equation is transformed into a Cartesian coordinate system to calculate the end contact force for the impedance model. Finally, the impedance control strategy together with a disturbance observer is designed which is suitable for nonlinear and strong coupling characteristics. The simulation result shows that the control system can follow the angle accurately in the condition of minimizing external constraints. Hardware experiment shows that lower extremity exoskeleton can adjust motion trajectory actively when encountering obstacles and complete the movement trajectory tracking at the same time.
In this paper, an obstacle-surmounting-enabled lower limb exoskeleton with novel linkage joints that perfectly mimicked human motions was proposed. Currently, most lower exoskeletons that use linear actuators have a direct connection between the wearer and the controlled part. Compared to the existing joints, the novel linkage joint not only fitted better into compact chasis, but also provided greater torque when the joint was at a large bend angle. As a result, it extended the angle range of joint peak torque output. With any given power, torque was prioritized over rotational speed, because instead of rotational speed, sufficiency of torque is the premise for most joint actions. With insufficient torque, the exoskeleton will be a burden instead of enhancement to its wearer. With optimized distribution of torque among the joints, the novel linkage method may contribute to easier exoskeleton movements.
The aim of this review was to provide an overview of assistive exoskeletons that have specifically been developed for industrial purposes and to assess the potential effect of these exoskeletons on reduction of physical loading on the body. The search resulted in 40 papers describing 26 different industrial exoskeletons, of which 19 were active (actuated) and 7 were passive (non-actuated). For 13 exoskeletons, the effect on physical loading has been evaluated, mainly in terms of muscle activity. All passive exoskeletons retrieved were aimed to support the low back. Ten-forty per cent reductions in back muscle activity during dynamic lifting and static holding have been reported. Both lower body, trunk and upper body regions could benefit from active exoskeletons. Muscle activity reductions up to 80% have been reported as an effect of active exoskeletons. Exoskeletons have the potential to considerably reduce the underlying factors associated with work-related musculoskeletal injury. Practitioner Summary: Worldwide, a significant interest in industrial exoskeletons does exist, but a lack of specific safety standards and several technical issues hinder mainstay practical use of exoskeletons in industry. Specific issues include discomfort (for passive and active exoskeletons), weight of device, alignment with human anatomy and kinematics, and detection of human intention to enable smooth movement (for active exoskeletons).
Few comparisons have been performed across torque controllers for exoskeletons, and differences among devices have made interpretation difficult. In this study, we designed, developed and compared the torque-tracking performance of nine control methods, including variations on classical feedback control, model-based control, adaptive control and iterative learning. Each was tested with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyography. Controllers were implemented on a tethered ankle exoskeleton with series elastic actuation. Measurements were taken while one human subject walked on a treadmill at 1.25 m·s-1 for one hundred steady-state steps. The combination of proportional control with damping injection and iterative learning resulted in the lowest errors for all high-level controllers. With time-based desired torque, root-mean-squared errors were 0.6 N·m (1.3% of peak desired torque) step by step, and 0.1 N·m (0.2%) on average. These results indicate that model-free, integration-free feedback control is suited to the uncertain dynamics of the human-robot system, while iterative learning is effective in the cyclic task of walking.
There is an increasing trend in using robots for medical purposes. One specific area is the rehabilitation. There are some
commercial exercise machines used for rehabilitation purposes. However, these machines have limited use because of their insufficient
motion freedom. In addition, these types of machines are not actively controlled and therefore can not accommodate complicated
exercises required during rehabilitation. In this study, a rule based intelligent control methodology is proposed to imitate
the faculties of an experienced physiotherapist. These involve interpretation of patient reactions, storing the information
received, acting according to the available data, and learning from the previous experiences. Robot manipulator is driven
by a servo motor and controlled by a computer using force/torque and position sensor information. Impedance control technique
is selected for the force control.
Because recent preliminary evidence points to the use of Error augmentation (EA) for motor learning enhancements, we visually enhanced deviations from a straight line path while subjects practiced a sensorimotor reversal task, similar to laparoscopic surgery. Our study asked 10 healthy subjects in two groups to perform targeted reaching in a simulated virtual reality environment, where the transformation of the hand position matrix was a complete reversal--rotated 180 degrees about an arbitrary axis (hence 2 of the 3 coordinates are reversed). Our data showed that after 500 practice trials, error-augmented-trained subjects reached the desired targets more quickly and with lower error (differences of 0.4 seconds and 0.5 cm Maximum Perpendicular Trajectory deviation) when compared to the control group. Furthermore, the manner in which subjects practiced was influenced by the error augmentation, resulting in more continuous motions for this group and smaller errors. Even with the extreme sensory discordance of a reversal, these data further support that distorted reality can promote more complete adaptation/learning when compared to regular training. Lastly, upon removing the flip all subjects quickly returned to baseline rapidly within 6 trials.
Firstly, the application requirements of lower extremity exoskeletons in rehabilitation, industry as well as military fields are introduced briefly, and the pros and cons, research problems and applicable population of rigid and soft lower extremity exoskeletons (RLEEX/SLEEX) are analyzed and compared. Subsequently, the research process and progress of SLEEX at home and abroad are illustrated in great detail, especially in mechanisms and actuation, sensors layout, development of control strategies as well as assessment of the assistance performance. Finally, the key technologies related to the SLEEX, including soft structure, human intention recognition, control strategies and assistance assessment, are summarized, and the future research direction is prospected.
Many previous works of soft wearable exoskeletons
(exosuit) target at improving the human locomotion assistance,
without considering the impedance adaption to interact with the
unpredictable dynamics and external environment, preferably
outside the laboratory environments. This paper proposes a novel
hierarchical human-in-the-loop paradigm that aims to produce
suitable assistance powers for cable-driven lower limb exosuits
to aid the ankle joint in pushing off the ground. It includes
two primary loop layers: impedance learning in the external
loop and human-in-the-loop adaptive management in the inner
loop. Considering unknown terrains, its impedance model can
be transferred to a quadratic programming (QP) problem with
specified constraints, which a designed primal-dual optimization
prototype then solves. Then, the presented impedance learning
strategy is introduced to regulate the impedance model with
the adaptive assistant powers for humans on different terrains.
An adaptive controller is designed in the inner loop to balance
the non-linearities and compliance existing in the human-exosuit
coexistence while the robust mechanism compensates for disturbances
to facilitate trajectory management without employing the
general regressor. The advantage of the proposed technique over
conventional solutions with fixed impedance parameters is that it
can improve human walking performance over different terrains.
Experiments demonstrate the significance of the approach.
Efficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.
In order to assist patients with lower limb disabilities in normal walking, a new trajectory learning scheme of limb exoskeleton robot based on dynamic movement primitives (DMP) combined with reinforcement learning (RL) was proposed. The developed exoskeleton robot has six degrees of freedom (DOFs). The hip and knee of each artificial leg can provide two electric-powered DOFs for flexion/extension. And two passive-installed DOFs of the ankle were used to achieve the motion of inversion/eversion and plantarflexion/dorsiflexion. The five-point segmented gait planning strategy is proposed to generate gait trajectories. The gait Zero Moment Point stability margin is used as a parameter to construct a stability criteria to ensure the stability of human-exoskeleton system. Based on the segmented gait trajectory planning formation strategy, the multiple-DMP sequences were proposed to model the generation trajectories. Meanwhile, in order to eliminate the effect of uncertainties in joint space, the RL was adopted to learn the trajectories. The experiment demonstrated that the proposed scheme can effectively remove interferences and uncertainties.
In lower limb exoskeletons, control performance and system stability of human–robot coordinated movement are often hampered by some model parametric uncertainties. To address this problem, Neighborhood Field Optimization (NFO) is proposed to identify the unknown model parameters of an exoskeleton for the model-based controller design. The excitation trajectory is designed by the NFO algorithm with motion constraints to improve the model identification accuracy. Meanwhile, the Huber fitness function is adopted to suppress the influence of the disturbance points in sampled dataset. Then an adaptive backstepping control scheme is constructed to improve the dynamic tracking performance of human–robot training mode in the presence of the identification error. Via Lyapunov technique and backstepping iteration, all the system state errors of the exoskeleton are bound and converge to zero neighborhood based on the assumption of bounded identified parameter error. Finally, the model identification results and comparative tracking performance of the proposed scheme are verified by an experimental platform of Two-degrees of freedom (DOF) lower limb exoskeleton with human–robot cooperative motion.
Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.
As an important movement of the daily living activities, sit-to-stand (STS) movement is usually a difficult task facing elderly and dependent people. In this article, a novel impedance modulation strategy of a lower-limb exoskeleton is proposed to provide appropriate power and balance assistance during STS movements while preserving the wearer’s control priority. The impedance modulation control strategy ensures adaptation of the mechanical impedance of the human–exoskeleton system toward a desired one requiring less wearer’s effect while reinforcing the wearer’s balance control ability during STS movements. A human joint torque observer is designed to estimate the joint torques developed by the wearer using joint position kinematics instead of electromyography or force sensors; a time-varying desired impedance model is proposed according to the wearer’s lower-limb motion ability. A virtual environmental force is designed for balance reinforcement control. Stability and robustness of the proposed method are theoretically analyzed. Simulations are implemented to illustrate the characteristics and performance of the proposed approach. Experiments with four healthy subjects are carried out to evaluate the effectiveness of the proposed method and show satisfactory results in terms of appropriate power assist and balance reinforcement.
Based on the equivalent mechanics model of wire rope, a kinetics modeling method of rope-driven unit for robot rotary joint is proposed, and the kinetics equation of rope-driven unit is derived. The impact resistance of rope-driven unit is analyzed theoretically, including passive compliance and active compliance. The active compliance control is introduced on the basis of tension feedback and position closed-loop controller of joint. According to the signals of tension sensor to judge the impact force, the change of output joint angle of rope-driven unit which the impact force may cause is calculated. Then, it will be substituted into the system kinetics equation to obtain the compensation angle needed by motor, so that rope-driven unit makes the corresponding retrograde motion to achieve the effect of unloading force when it is impacted by the external. Taking FDU-II rope-driven unit developed independently by our laboratory as an example, the system kinetics modeling and impact experimental research are carried out by using the above method. The results show that the active–passive compliance control can effectively improve the impact resistance of FDU-II rope-driven unit. At present, FDU-II rope-driven unit can withstand 3 times of the impact force of the load gravity and achieve 20% of the buffer ability. Compared with the impact experimental results only relying on passive compliance, it has greatly improved and also effectively reduced the period of shock caused by the impact, which provides a theoretical and experimental basis for the future research of biped robot stable walking experiment.
In this paper, a novel hybrid optimal Adaptive High-Order Super Twisting Sliding Mode (AHOSTWSM) impedance control is proposed for a lower limb exoskeleton robot with 7 active joints, which takes the interaction forces between the robot and the user into account. First, the dynamic model of the robot is extracted using Lagrange approach. Then, an adaptive super twisting sliding mode controller is designed based on Lyapanov theory. Moreover, the interaction forces are obtained in every time-step and applied to the robot as a compensating torque to guard against disturbances. The desired trajectory of the upper limb joint has been extracted so that the stability of the robot is achieved based on Zero Moment Point (ZMP) criterion at any moment. Moreover, to achieve optimal performance (maximum stability, minimum tracking error, reduced interacting forces, and optimal energy consumption), parameters of the proposed controller, the upper limb desired trajectory, and the impedance system are optimized using Harmony Search Algorithm (HSA). Finally, in order to demonstrate the effectiveness of the proposed controller, optimal sliding mode controller (SMC) is designed and performance of the AHOSTWSM is compared with SMC. The simulation results reveal the superiority of the controller in comparison with SMC.
In this paper, hybrid control of central pattern generators (CPGs), along with an adaptive supper-twisting sliding mode (ASTSM) control based on supper-twisting state observer, is proposed to guard against disturbances and uncertainties. Rhythmic and coordinated signals are generated using CPGs. In addition, to overcome the chattering of conventional sliding mode, supper-twisting sliding mode has been applied. The ASTSM method triggers sliding variables, and its derivatives tend to zero continuously in the presence of the uncertainties. Moreover, to acquire maximum stability, the desired trajectory of the upper limb based on zero moment point criterion is designed.
The powered exoskeleton promises substantial improvements
on daily activities of the people who need robots
provide assistance. In order to achieve flexible and stable control
of a powered lower limb exoskeleton, in this paper, a hybrid
control that combines a brain-computer interface (BCI) based on
motor imagery (MI) with surface electromyogram (EMG) signals
has been developed. We utilized the common spatial pattern
(CSP) method to extract the variance of electroencephalogram
(EEG) signals and back propagation (BP) neural network to
recognize the imagery tasks. Moreover, we have used the strength
of EMG signals obtained from upper forearms of subjects to
adjust the height and width of stairs so that the gait of subjects
can be adjusted flexibly. The recognized results of EEG and the
strength of EMG are used to drive the powered exoskeleton to
help subjects climb the stairs by the designed gait synthesis which
satisfies the environmental constraint and kinematic constraint.
The developed hybrid control strategy has been verified by three
healthy subjects, and all subjects can successfully fulfill steadily
climbing the stairs, assisted by the powered exoskeleton. The
results of the experiment have demonstrated the developed hybrid
brain/muscle signals powered robot can effectively enhance
human mobility.
This paper presents the control of a lower-limb exoskeleton in real time, to assist a pathological gait, taking into account only electromyography (EMG) events and a characteristic angular pattern of a normal gait. The algorithm consists of an impedance control scheme that uses EMG signals to modify the characteristic angular pattern of a normal gait. EMG signals were characterized using the simple square integral (SSI) index and this information was used to identify a characteristic EMG pattern in the semitendinosus and gastrocnemius muscles. Finally, a series of experimental tests were conducted with a volunteer to validate the adequate performance of the system.
In this paper, the position and force tracking control problem of cooperative robot manipulator system handling a common rigid object with unknown dynamical models and unknown external disturbances is investigated. The universal approximation properties of fuzzy logic systems are employed to estimate the unknown system dynamics. On the other hand, by defining new state variables based on the integral and differential of position and orientation errors of the grasped object, the error system of coordinated robot manipulators is constructed. Subsequently by defining the appropriate change of coordinates and using the backstepping design strategy, an adaptive fuzzy backstepping position tracking control scheme is proposed for multi-robot manipulator systems. By utilizing the properties of internal forces, extra terms are also added to the control signals to consider the force tracking problem. Moreover, it is shown that the proposed adaptive fuzzy backstepping position/force control approach ensures all the signals of the closed loop system uniformly ultimately bounded and tracking errors of both positions and forces can converge to small desired values by proper selection of the design parameters. Finally, the theoretic achievements are tested on the two three-link planar robot manipulators cooperatively handling a common object to illustrate the effectiveness of the proposed approach.
Purpose
– This article aims to provide details of recent robotic exoskeleton developments and applications.
Design/methodology/approach
– Following an introduction, this article first considers some of the technological issues associated with an exoskeleton design. It then discusses military developments, industrial load-carrying applications and uses in healthcare. Progress in thought-controlled exoskeletons is discussed briefly, and finally, concluding comments are drawn.
Findings
– This article shows that, while military interests continue, the dominant application is to restore or enhance mobility to individuals suffering from disabilities or injuries. An emerging use is to increase the strength and endurance of industrial workers. The majority are lower-limb devices, although some full-body exoskeletons have been developed, and most rely on battery-powered electric motors to create motion. Reflecting the anticipated growth in applications, exoskeletons are now available from, or under development by, a growing number of commercial organisations.
Originality/value
– This provides an insight into the latest developments in robotic exoskeletons and their applications.
The AERObot autopilot for small and medium size unmanned aerial vehicles (UAVs) developed at Óbuda Universtiy has multiple control systems. It is possible to switch between the control systems even during in flight. The purpose of the current research was to examine the applicability of full fuzzy flight control (takeoff, cruise and land) in unmanned aerial vehicles instead of partial or hybrid control [1, 2, 3]. The easy tuning of the parameters and features is quite important because one autopilot should control different sized and designed planes. Since the safety of the flight, life and value is the most important, real test flights should always be performed after many hours of computer simulation. For this purpose Hardware In the Loop simulation was used. The advantage of this simulation is that the very same environment can be created as in real flights (e.g. atmospheric, aerodynamic, propulsion model, different weather conditions). After successful simulations the real test flights can be started.
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