Hybrid design of PID controller for four DoF lower limb exoskeleton

Abstract In this paper, a method of tuning a proportional-integral-derivative controller for a four degree-of-freedom lower limb exoskeleton using hybrid of genetic algorithm and particle swarm optimization is presented. Transfer function of each link of the lower limb exoskeleton acquired from a pendulum model, was used in a closed-loop proportional-integral-derivative control system, while each link was assumed as one degree-of-freedom linkage. In the control system, the hybrid algorithm was applied to acquire the parameters of the controller for each joint for minimizing the error. The algorithm started with genetic algorithm and continued via particle swarm optimization. Furthermore, a 3-dimensional model of the lower limb exoskeleton was simulated to validate the proposed controller. The trajectory of the control system with optimized proportional-integral-derivative controller via hybrid precisely follows the input signal of the desired. The result of the hybrid optimized controller was compared with genetic algorithm and particle swarm optimization based on statistics. The average error of the proposed algorithm showed the optimized results in comparison with genetic algorithm and particle swarm optimization. Furthermore, the advantages of the hybrid algorithm have been indicated by numerical analysis.

[1]  Adolfo Rodríguez Tsouroukdissian,et al.  ros_control: A generic and simple control framework for ROS , 2017, J. Open Source Softw..

[2]  Arturo Y. Jaen-Cuellar,et al.  PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems , 2013 .

[3]  Michael Goldfarb,et al.  A Controller for Guiding Leg Movement During Overground Walking With a Lower Limb Exoskeleton , 2018, IEEE Transactions on Robotics.

[4]  Zhang Huaqing,et al.  PID Controller Optimization by GA and Its Performances on the Electro-hydraulic Servo Control System , 2008 .

[5]  Xin Song,et al.  A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization , 2019, Math. Comput. Simul..

[6]  Prakash Kumar Hota,et al.  Controller parameters tuning of differential evolution algorithm and its application to load frequency control of multi-source power system , 2014 .

[7]  Chao Li,et al.  Optimization of a heliostat field layout using hybrid PSO-GA algorithm , 2018 .

[8]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[9]  Wei-Hsin Liao,et al.  HIP-KNEE control for gait assistance with Powered Knee Orthosis , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[10]  Lei Ma,et al.  Neural network control of lower limb rehabilitation exoskeleton with repetitive motion , 2017, 2017 36th Chinese Control Conference (CCC).

[11]  Kyung-Hun Kim,et al.  Progressive treadmill cognitive dual-task gait training on the gait ability in patients with chronic stroke , 2018, Journal of exercise rehabilitation.

[12]  Adnan Masood,et al.  Kinematic and Dynamic Analysis of a Lower Limb Exoskeleton , 2012 .

[13]  Eduardo Vázquez-Fernández,et al.  A genetic algorithm with a mutation mechanism based on a Gaussian and uniform distribution to minimize addition chains for small exponents , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[14]  Songran Liu,et al.  A modified genetic algorithm for community detection in complex networks , 2017, 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET).

[15]  Shahid Hussain,et al.  Adaptive Impedance Control of a Robotic Orthosis for Gait Rehabilitation , 2013, IEEE Transactions on Cybernetics.

[16]  Dumitru Baleanu,et al.  A new hybrid algorithm for continuous optimization problem , 2018 .

[17]  Yang Tian,et al.  Model-free based adaptive nonsingular fast terminal sliding mode control with time-delay estimation for a 12 DOF multi-functional lower limb exoskeleton , 2018, Adv. Eng. Softw..

[18]  Jian Huang,et al.  Data-Driven Human-Robot Coordination Based Walking State Monitoring With Cane-Type Robot , 2018, IEEE Access.

[19]  Weihua Su,et al.  PID controllers: Design and tuning methods , 2014, 2014 9th IEEE Conference on Industrial Electronics and Applications.

[20]  Paulo Félix,et al.  Electronic design and validation of Powered Knee Orthosis system embedded with wearable sensors , 2017, 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).

[21]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[22]  Rong Song,et al.  The design and control of a 3DOF lower limb rehabilitation robot , 2016 .

[23]  J. Edward Colgate,et al.  Design of an active one-degree-of-freedom lower-limb exoskeleton with inertia compensation , 2011, Int. J. Robotics Res..

[24]  M. Munlin,et al.  New social-based radius particle swarm optimization , 2017, 2017 12th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[25]  Xiaodong Zhang,et al.  Modeling and design on control system of lower limb rehabilitation exoskeleton robot , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[26]  Shuzhi Sam Ge,et al.  Neural Network Control of a Rehabilitation Robot by State and Output Feedback , 2015, J. Intell. Robotic Syst..

[27]  Oscar Castillo,et al.  Type-1 and Type-2 fuzzy logic controller design using a Hybrid PSO-GA optimization method , 2014, Inf. Sci..

[28]  Mohamad Rasekh,et al.  Using PSO-GA algorithm for training artificial neural network to forecast solar space heating system parameters , 2019, Applied Thermal Engineering.

[29]  Adel Akbarimajd,et al.  Intelligent Control Method of a 6-DOF parallel robot Used for Rehabilitation Treatment in lower limbs , 2016 .

[30]  Xiaobo Zhang,et al.  Design and Control of a Powered Hip Exoskeleton for Walking Assistance , 2015 .

[31]  S. Kanmani,et al.  A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid) , 2017, Swarm Evol. Comput..