On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach

Belkadi, Adel; Oulhadj, Hamouche; Touati, Youcef; Khan, Safdar Abbas; Daachi, B.

doi:10.1016/j.asoc.2017.06.012

Cited by 59 publications

(24 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Sanz-Merodio et al [28] combined the use of different gains and passive elements to reduce the energy consumption while applying a static optimization. Additionally, Belkadi et al [29] proposed a PID adaptive controller based on modified particle swarm optimization algorithm, where the controller is initialized with the desired position and velocity instead of EMG signals, as made in this work. Experimental studies, as the one carried out with eight subjects by Song et al [30], showed improvements in the upper limb functions with the use of a controlled robotic system with one degree-of-freedom.…”

Section: Resultsmentioning

confidence: 99%

Modeling of Motorized Orthosis Control

et al. 2019

View full text Add to dashboard Cite

Orthotic devices are defined as externally applied devices that are used to modify the structural and functional characteristics of the neuro-muscular and skeletal systems. The aim of the current study is to improve the control and movement of a robotic arm orthosis by means of an intelligent optimization system. Firstly, the control problem settlement is defined with the muscle, brain, and arm model. Subsequently, the optimization control, which based on a differential evolution algorithm, is developed to calculate the optimum gain values. Additionally, a cost function is defined in order to control and minimize the effort that is made by the subject and to assure that the algorithm follows as close as possible the defined setpoint value. The results show that, with the optimization algorithm, the necessary development force of the muscles is close to zero and the neural excitation level of biceps and triceps signal values are getting lower with a gain increase. Furthermore, the necessary development force of the biceps muscle to overcome a load added to the orthosis control system is practically the half of the one that is necessary without the optimization algorithm.

show abstract

Section: Resultsmentioning

confidence: 99%

Modeling of Motorized Orthosis Control

et al. 2019

View full text Add to dashboard Cite

show abstract

“…• Based on the usage of unused resources in the field, each initial seed grows to a flowering plant and produce new seeds on the fitness value of each plant. The number of seeds (S) produced by each plant in reproduction process is calculated by using the equation (23). The goodness of this algorithm is that it allows all generated weeds to contribute towards reproduction process.…”

Section: Modified Chaotic Invasive Weed Optimization Algorithmmentioning

confidence: 99%

“…Moreover, Geetha et al [22] implemented a virtual feedback controller to control the state variables of the continuously stirred tank chemical reactor and used extended kalman filter in the feedback mechanism. They used GA, PSO [23] and ACO algorithms for determining the optimal PID parameters. It was found that the PSO algorithm was seen to perform better than the other methods.…”

Section: Introductionmentioning

confidence: 99%

Implementation of modified chaotic invasive weed optimization algorithm for optimizing the PID controller of the biped robot

Mandava

Vundavilli

2018

Sādhanā

View full text Add to dashboard Cite

The present research work involves the implementation of Modified Chaotic Invasive Weed Optimization (MCIWO) algorithm for optimizing the gains of torque based proportional integral and derivative (PID) controller used to control the motors of the biped robot while walking on flat surface. While designing the controller, the dynamics of the biped robot has been derived using the well-known Lagrange-Euler (L-E) formulation. Subsequently, manual tuning procedure is employed to find the ranges of the gains of PID controller used in the developed algorithm. Once it is optimized, the effectiveness of the proposed algorithm is then compared with the Differential Evolution (DE) algorithm, in terms of variation of error, torque required, zero moment point (ZMP) and dynamic balance margin (DBM) of the biped robot. It has been observed that the MCIWO algorithm tuned PID controller is found to perform better than DE tuned controller. Further, the optimal gait obtained through the developed algorithm is validated by executing it on the real robot. It has been observed that the robot has successfully negotiated the flat terrain with the gaits obtained by the optimal PID controller.

show abstract

“…As a result, the trajectory tracking control of lower limb rehabilitation robots is more difficult compared with robotic manipulators 8,9 . Therefore, design methods combining traditional control with adaptive control, robust control, sliding mode control and intelligent control are proposed to ensure the stability, robustness and dynamic performance of lower limb rehabilitation robots in complex environment 10,11 . Although the aforementioned design methods improve the tracking performance of lower limb rehabilitation robots, the application scope of them is restricted and the calculation of designed controllers is complicated.…”

Section: Introductionmentioning

confidence: 99%

Tracking control for lower limb rehabilitation robots based on polynomial nonlinear uncertain models

Zeng

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper aims to propose a novel idea for the modeling and trajectory tracking control of a lower limb rehabilitation robot. A polynomial nonlinear uncertain model is established to deal with the difficulty of accurate modeling for the lower limb rehabilitation robot with complex dynamic characteristics. A high‐order nonlinear disturbance observer (HONDO) is utilized to estimate the lumped disturbance with fast time‐varying and the corresponding observer gain matrix is obtained by H∞ performance. Then an HONDO‐based composite tracking controller is designed to achieve good tracking performance of the lower limb rehabilitation robot. In addition, all solvability conditions can be directly handled by sum of squares programming, effectively avoiding the calculation problem. Finally, numerical simulations are conducted to verify the validity of the proposed method.

show abstract

On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach

Cited by 59 publications

References 23 publications

Modeling of Motorized Orthosis Control

Modeling of Motorized Orthosis Control

Implementation of modified chaotic invasive weed optimization algorithm for optimizing the PID controller of the biped robot

Tracking control for lower limb rehabilitation robots based on polynomial nonlinear uncertain models

Contact Info

Product

Resources

About