An adaptive PID like controller using mix locally recurrent neural network for robotic manipulator with variable payload. (May 2016)

• Record Type: Journal Article
• Authors: Sharma, Richa; Kumar, Vikas; Gaur, Prerna; Mittal, A.P.
• Is Part Of: ISA transactions. Volume 62(2016:May)
• Page Start: 258
• Page End: 267
• Publication Date: 2016-05
• Subjects: Robotic manipulator -- Cuckoo search algorithm -- Artificial neural networks -- Recurrent neural networks -- On-line learning; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2016.01.016
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Being complex, non-linear and coupled system, the robotic manipulator cannot be effectively controlled using classical proportional-integral-derivative (PID) controller. To enhance the effectiveness of the conventional PID controller for the nonlinear and uncertain systems, gains of the PID controller should be conservatively tuned and should adapt to the process parameter variations. In this work, a mix locally recurrent neural network (MLRNN) architecture is investigated to mimic a conventional PID controller which consists of at most three hidden nodes which act as proportional, integral and derivative node. The gains of the mix locally recurrent neural network based PID (MLRNNPID) controller scheme are initialized with a newly developed cuckoo search algorithm (CSA) based optimization method rather than assuming randomly. A sequential learning based least square algorithm is then investigated for the on-line adaptation of the gains of MLRNNPID controller. The performance of the proposed controller scheme is tested against the plant parameters uncertainties and external disturbances for both links of the two link robotic manipulator with variable payload (TL-RMWVP). The stability of the proposed controller is analyzed using Lyapunov stability criteria. A performance comparison is carried out among MLRNNPID controller, CSA optimized NNPID (OPTNNPID) controller and CSA optimized conventional PID (OPTPID) controller in order to establish the effectiveness of the MLRNNPID controller. Highlights: An adaptive PID controller is created using mix locally recurrent neural networks. A sequential learning mechanism is derived for on line adaptation of the gains. Fast, stable and accurate training algorithm compared to conventional gradient based methods. The implemented controller is robust due to its on line learning capabilities.