Fractional order based trajectory tracking control of an ankle rehabilitation robot

Abstract

Human–robot interaction is inherently available and used actively in ankle rehabilitation robots. This interaction causes disturbances to be counteracted on the rehabilitation robots in order to reduce the side effects. This paper presents a fractional order proportional–integral–derivative controller to improve the trajectory tracking ability of a developed 2-degree of freedom parallel ankle rehabilitation robot subject to external disturbances. The parameters of the controller are optimally tuned by using both the cuckoo search algorithm and the particle swarm optimization algorithm. A traditional proportional–integral–derivative controller, which is also tuned using both of the algorithms, is designed to test the performance of the fractional order proportional–integral–derivative controller. The experimental results show that the optimally tuned FOPID controller improves the tracking performance of the ankle rehabilitation robot subject to external disturbances significantly and decreases the steady-state tracking errors compared to the optimally tuned PID controller.

Designing and implementing a plug-in type repetitive controller for a redundantly actuated ankle rehabilitation robot

“…However, the detailed explanations about the developed ankle rehabilitation robot and the required control schemes for the ankle rehabilitation process can be found in our previous studies. 23,24 It should be noted that the frequently used ROM movements of the ankle, that is, inversion/eversion and plantarflexion/dorsiflexion, during the ankle rehabilitation process are carried out in the sagittal and frontal planes, 35,36 although ROM exercises for ankle rehabilitation are performed in the transverse, frontal, and sagittal planes. Therefore, at least a 2-DOF mechanism capable of providing the requirements in terms of ROM is required to perform these common movements.…”

“…As the reference trajectory of the ankle rehabilitation robot can be transformed to the reference lengths of each leg of the parallel mechanism using inverse kinematic analysis, the transfer function model of the linear electrical actuator corresponding to each leg is obtained. First, a PID controller, the parameters of which are optimally tuned using Cuckoo search algorithm (CSA), 23 is implemented for each actuator separately. Then, a plug-in RC is designed and implemented as a cascade to a PID controller in order to improve the tracking performance.…”

Research on an Ankle Joint Auxiliary Rehabilitation Robot with a Rigid-Flexible Hybrid Drive Based on a 2-S′PS′ Mechanism

“…Zhang et al [16] proposed a computational ankle model for use in robot-assisted therapy estimating the passive ankle torque. Ayas et al [17] designed a fractional-order controller for a developed 2-DOF parallel ankle rehabilitation robot subject to external disturbance to improve the trajectory tracking performance.…”

Parameter effect analysis of particle swarm optimization algorithm in PID controller design

“…Because these algorithms have been agreed as an alternative method of solving deterministic optimization problems or stochastic programming whose solution is not feasible in most cases even though optimality is proven. Genetic algorithm (GA) [1,2], ant colony optimization (ACO) [3], cuckoo search algorithm (CSA) [4], flower pollination algorithm (FPA) [5], differential evolution [6], artificial bee colony algorithm (ABC) [8], and particle swarm optimization (PSO) [7,[9][10][11] are some of meta-heuristic optimization algorithms used in controller design process in control system area.…”