Multi-objective gain optimizer for a multi-input active disturbance rejection controller: Application to series elastic actuators

Abstract Series elastic actuators (SEA) have been gaining increasing popularity as a mechanical drive in contemporary force-controlled robotic manipulators thanks to their ability to infer the applied torque from measurements of the elastic element’s deflection. Accurate deflection control is crucial to achieve a desired output torque and, therefore, unmodelled dynamics and dynamic loads can severely compromise force fidelity. Multi-input active disturbance rejection controllers (ADRC) have the ability to estimate such disturbances affecting the plant behaviour and cancel them via an appropriate feedback controller. Thus, they offer a promising control architecture for SEA. ADRC, however, can have upwards of eight tuning parameters for each controlled state. Tuning the controller becomes quite challenging, especially in the context of multi-input, multi-objective control. This paper tackles the problem of ADRC tuning as a multi-parametric and multi-objective optimization approach. An ADRC is developed to regulate the output torque of a multi-input hybrid motor-brake–clutch SEA. The controller has a total of 22 tunable parameters. Point dominance-based nondominated sorting genetic algorithm is used to find the optimal control gains, first considering nine individual control objectives, and then in the context of multi-objective. The algorithm provides a set of potential solutions that highlight the tradeoffs between the control objectives. It is up to the discretion of the designer to select the appropriate solution that best suits a given application. The approach is validated experimentally and the results are compared with a simulated model. Experimental results confirm the suitability of the proposed approach for single and multiple control objectives in a variety of experimental scenarios and show good agreement with the analytical model.

[1]  Kalyanmoy Deb,et al.  An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints , 2014, IEEE Transactions on Evolutionary Computation.

[2]  Jing Liu,et al.  Research on Autodisturbance-Rejection Control of Induction Motors Based on an Ant Colony Optimization Algorithm , 2018, IEEE Transactions on Industrial Electronics.

[3]  Qiang Gao,et al.  Passivity-Based Control for Rocket Launcher Position Servo System Based on ADRC Optimized by IPSO-BP Algorithm , 2018 .

[4]  Bao-Zhu Guo,et al.  On convergence of nonlinear active disturbance rejection control for MIMO systems , 2012, Proceedings of the 31st Chinese Control Conference.

[5]  Jingqing Han,et al.  From PID to Active Disturbance Rejection Control , 2009, IEEE Trans. Ind. Electron..

[6]  Rafal Madonski,et al.  High-gain disturbance observer tuning seen as a multicriteria optimization problem , 2013, 21st Mediterranean Conference on Control and Automation.

[7]  Jing Liu,et al.  Research on Active Disturbance Rejection Control With Parameter Autotune Mechanism for Induction Motors Based on Adaptive Particle Swarm Optimization Algorithm With Dynamic Inertia Weight , 2018, IEEE Transactions on Power Electronics.

[8]  Antonios Armaou,et al.  Study on ADRC Parameter Optimization Using CPSO for Clamping Force Control System , 2018 .

[9]  Carlos Rossa,et al.  Backlash-Compensated Active Disturbance Rejection Control of Nonlinear Multi-Input Series Elastic Actuators , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Kalyanmoy Deb,et al.  RDS-NSGA-II: a memetic algorithm for reference point based multi-objective optimization , 2017 .

[11]  Jianhua Zhang,et al.  Parameters optimization of ADRC based on adaptive CPSO algorithm and its application in main-steam temperature control system , 2018, 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA).

[12]  Chris Gerada,et al.  A Nonlinear Extended State Observer for Sensorless IPMSM Drives With Optimized Gains , 2020, IEEE Transactions on Industry Applications.

[13]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[14]  Qingfu Zhang,et al.  Multiobjective Optimization Problems With Complicated Pareto Sets, MOEA/D and NSGA-II , 2009, IEEE Transactions on Evolutionary Computation.

[15]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[16]  Hisao Ishibuchi,et al.  Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes , 2017, IEEE Transactions on Evolutionary Computation.

[17]  Chen Hua-yuan Auto-disturbances-rejection controller design and its parameter optimization for aircraft longitudinal attitude , 2010 .

[18]  Dengyan Duan,et al.  Tuning of ADRC for QTR in Transition Process Based on NBPO Hybrid Algorithm , 2019, IEEE Access.

[19]  R. K. Ursem Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[20]  Amin Nobakhti,et al.  Hardware Implementation of an ADRC Controller on a Gimbal Mechanism , 2018, IEEE Transactions on Control Systems Technology.

[21]  Slim Bechikh,et al.  A New Decomposition-Based NSGA-II for Many-Objective Optimization , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[22]  Farouk Yalaoui,et al.  A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints , 2016 .

[23]  Liu Chang-bo Unmanned Underwater Vehicle Depth ADRC Based on Genetic Algorithm Near Surface , 2013 .

[24]  S. J. Dodds,et al.  Guaranteed rates of convergence of a class of PD controllers for trajectory tracking problems of robotic manipulators with dynamic uncertainties , 1996 .

[25]  Il-Kwon Oh,et al.  Active Disturbance Rejection Control for Precise Position Tracking of Ionic Polymer–Metal Composite Actuators , 2013, IEEE/ASME Transactions on Mechatronics.

[26]  Guiying Wu,et al.  Disturbance rejection control of a fuel cell power plant in a grid-connected system , 2017 .

[27]  Brayden DeBoon,et al.  Differentially-Clutched Series Elastic Actuator for Robot-Aided Musculoskeletal Rehabilitation , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[28]  Bao-Zhu Guo,et al.  On active disturbance rejection control for nonlinear systems using time-varying gain , 2015, Eur. J. Control.

[29]  Hua Xu,et al.  An improved NSGA-III procedure for evolutionary many-objective optimization , 2014, GECCO.

[30]  Gaoxi Xiao,et al.  On Convergence Performance of Discrete-Time Optimal Control Based Tracking Differentiator , 2021, IEEE Transactions on Industrial Electronics.

[31]  Zhaoyang Ai,et al.  Parameter tuning of ADRC and its application based on CCCSA , 2014 .