Design and Adaptive Control for an Upper Limb Robotic Exoskeleton in Presence of Input Saturation

He, Wei; Li, Zhijun; Dong, Yiting; Zhao, Ting

doi:10.1109/tnnls.2018.2828813

Cited by 113 publications

(58 citation statements)

References 66 publications

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“…In the study of LLRE control strategies for the rehabilitation training of patients with residual muscle strength in their affected limbs, two main issues need attention. The first is how exoskeleton adaptively adjusts training tasks according to the condition of different patients' affected lower limbs [15][16][17]. The second is how to optimize the exoskeleton control strategy based on the active muscle force of the patients' lower limbs, so that they can participate in rehabilitation training as much as possible to achieve maximum exercise of the muscles.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Zhu

Song

et al. 2020

Applied Sciences

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As passive rehabilitation training with fixed trajectory ignores the active participation of patients, in order to increase the active participation of patients and improve the effect of rehabilitation training, this paper proposes an innovative adaptive sliding mode variable admittance (ASMVA) controller for the Lower Limb Rehabilitation Exoskeleton Robot. The ASMVA controller consists of an outer loop with variable admittance controller and an inner loop with an adaptive sliding mode controller. It estimates the wearer's active muscle strength and movement intention by judging the deviation between the actual and standard interaction force of the wearer's leg and the exoskeleton, thereby adaptively changing admittance controller parameters to alter training intensity. Three healthy volunteers engaged in further experimental studies, including trajectory tracking experiments with no admittance, fixed admittance, and variable admittance adjustment. The experimental results show that the proposed ASMVA control scheme has high control accuracy. Besides, the ASMVA can not only increase training intensity according to the active muscle strength of the patient during positive movement intention (so as to increase active participation of the patient), but also increase the amount of trajectory adjustment during negative movement intention to ensure the safety of the patient.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Zhu

Song

et al. 2020

Applied Sciences

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“…Some extra constraints inherent to some systems, like solution positivity in the case of biological systems or human migrations or the needed behavior robustness against parametrical changes of disturbance actions add additional complexity to the related investigations and need the use of additional mathematical or engineering tools for the research development, [5][6][7]. A large variety of modeling and design tools have to be invoked and developed in the analysis depending on the concrete systems under study and their potential applications as, for instance, the presence of internal and external delays, discretization, dynamics modeling based on fractional calculus, the existence of complex systems with interconnected subsystems, [8][9][10][11][12][13], hybrid coupled continuous/digital tandems, nonlinear systems and optimization and estimation techniques [14][15][16][17][18][19] as well as robotic and fuzzy-logic based systems, [20,21]. In particular, decentralized control is a useful tool for controlling dynamic systems by cutting some links between the dynamics coupling a set of subsystems integrated in the whole system at hand.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, a centralized controller keeps all the information on the system and coupling links as available to the control designer while decentralized control ignores some of such information or even cuts on occasions some of coupling signals between the various subsystems integrated in the whole system at hand. It can be pointed out that the stability studies are often performed trough Lyapunov theory which requires to find a Lyapunov function (see [20,21] and some references therein). It turns out that, if the neglected couplings are strong and are not taken into account by the controller, the stabilization and other properties such as the controllability can become lost.…”

Section: Introductionmentioning

confidence: 99%

Parametrical Non-Complex Tests to Evaluate Partial Decentralized Linear-Output Feedback Control Stabilization Conditions from Their Centralized Stabilization Counterparts

Sen

Ibeas

2019

Applied Sciences

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. This paper formulates sufficiency-type linear-output feedback decentralized closed-loop stabilization conditions if the continuous-time linear dynamic system can be stabilized under linear output-feedback centralized stabilization. The provided tests are simple to evaluate, while they are based on the quantification of the sufficiently smallness of the parametrical error norms between the control, output, interconnection and open-loop system dynamics matrices and the corresponding control gains in the decentralized case related to the centralized counterpart. The tolerance amounts of the various parametrical matrix errors are described by the maximum allowed tolerance upper-bound of a small positive real parameter that upper-bounds the various parametrical error norms. Such a tolerance is quantified by considering the first or second powers of such a small parameter. The results are seen to be directly extendable to quantify the allowed parametrical errors that guarantee the closed-loop linear output-feedback stabilization of a current system related to its nominal counterpart. Furthermore, several simulated examples are also discussed.

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“…Gao et al 30 selected the nondominated sorting genetic algorithm (NSGA-II) to conduct the optimizations, and better optimization results are obtained. He et al 34,35 developed a fuzzy NN learning algorithm to identify the uncertain plant model, and the NN technology is proposed to approximate the uncertain robotic dynamics. A series of compromise solutions can be obtained by optimum design method of multi-objective function, and we can choose the appropriate optimal solution according to the different needs of users and different emphasis.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al 33 adopted the neural network (NN) to approximate the uncertain dynamics, and NNs are considered to be one of the effective methods in many control systems. He et al 34,35 developed a fuzzy NN learning algorithm to identify the uncertain plant model, and the NN technology is proposed to approximate the uncertain robotic dynamics.…”

Section: Introductionmentioning

confidence: 99%

Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

Sun

Fan

et al. 2019

Energy Science & Engineering

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As a new type of energy, tidal current energy is increasingly gaining attention worldwide. However, the tidal current energy development field faces a key technical problem of how to improve the generation efficiency and service life of tidal current generators. In this study, an innovative optimized design method of the horizontal axis tidal turbine was applied to the rotor blade design. To be specific, blade cavitation was considered as a factor for optimization and then was improved using a modern optimization method that combined artificial neural networks and genetic algorithms, solving for a numerical solution. In the optimization program, the annual energy output and the superficial area of the rotor blade served as the optimization goals, and the chord length and the twist angle served as the optimization variables. The cavitation constraint was different from the traditional method in that the judgment criterion of occurrence of cavitation was no longer the minimum pressure coefficient but the maximum stress coefficient. The combination of the artificial neural network and the genetic algorithm not only realized high optimization accuracy but also greatly saved computation time. To verify the validity of the optimization method, a 1 MW turbine was designed, and the optimal solution was chosen from the formed Pareto optimal solution set for modeling, and the new model was compared with the model quoted in the reference. The simulation result shows that the turbine designed using the optimization method not only reduces the surface area of the rotor blade and improves the annual energy output, but also remarkably improves the blades' cavitation resistance. The optimization analysis and comparison using different cavitation judgment criteria shows that the cavitation resistance of blades obtained using the maximum stress coefficient as the judgment criterion is more advantageous.

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Design and Adaptive Control for an Upper Limb Robotic Exoskeleton in Presence of Input Saturation

Cited by 113 publications

References 66 publications

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

An Adaptive Sliding Mode Variable Admittance Control Method for Lower Limb Rehabilitation Exoskeleton Robot

Parametrical Non-Complex Tests to Evaluate Partial Decentralized Linear-Output Feedback Control Stabilization Conditions from Their Centralized Stabilization Counterparts

Prediction and multi‐objective optimization of tidal current turbines considering cavitation based on GA‐ANN methods

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