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Design of intelligent optimal controller for hybrid position/force control of constrained reconfigurable manipulators

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Abstract

This paper is concerned with an intelligent optimal control approach for position/force control of constrained reconfigurable manipulators in the presence of uncertainties. Reconfigurable manipulator has strong coupling, nonlinearity, uncertainties in the system and the environmental contact make the dynamical model much more complex. This necessitates the design of intelligent control scheme to achieve the control objectives as possible. Therefore to handle this complex system, firstly, the dynamic model is reduced into state space form describing the constrained and unconstrained state variables separately. Then the state-space form of error dynamics is utilized for quadratic optimization where the optimal control, the explicit solution of Hamilton Jacobi Bellman equation, is obtained by solving an algebraic Riccati equation. The proposed scheme integrates the linear optimal control, radial basis function (RBF) neural network and adaptive robust term to overcome the uncertainties of the complex system and to achieve the desired performance. The linear optimal controller optimizes the system whereas the nonlinearities and uncertainties of the system are compensated using RBF neural network and adaptive compensator. The asymptotic stability of the system is demonstrated using the optimal control theory and Lyapunov stability analysis. Finally, the proposed control approach is verified in comparative manner through simulation results with 2 DOF reconfigurable manipulators to perform various tasks.

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Authors and Affiliations

  1. National Institute of Technology Kurukshetra, Kurukshetra, Haryana, India

    Komal Rani & Naveen Kumar

  2. Babu Anant Ram Janta College, Kaul, Kaithal, Haryana, India

    Komal Rani

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  1. Komal Rani
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  2. Naveen Kumar
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Correspondence to Naveen Kumar.

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Rani, K., Kumar, N. Design of intelligent optimal controller for hybrid position/force control of constrained reconfigurable manipulators. J Ambient Intell Human Comput 14, 13421–13432 (2023). https://doi.org/10.1007/s12652-022-03797-x

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  • DOI: https://doi.org/10.1007/s12652-022-03797-x

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