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

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.

Performance Evaluation and Implementation of FPGA Based SGSF in Smart Diagnostic Applications.

The main objective of the paper is to implement Savitzky Golay Smoothing Filter (SGSF) so as to apply in pre-processing of real time smart medical diagnostic systems. As very important information of EEG and ECG waveforms lies in the peak of the signal, hence it becomes absolutely necessary to filter noise and artifacts from the signal. The implemented filter should be able to reject the noise efficiently along with the least distortion from the original signal. The shape preserving characteristics of the filter are determined by introducing different noise levels in the signal. The designed filter is tested on synthetic signals of EEG and ECG by adding different types of noise and the performance is analysed on various parameters, i.e., SNR, SSNR, SNRI, MSE, COR and signal distortion of the final output. The smoothing performance comparison of SGSF with the most commonly used Moving Average Filter (MAF) proves that SGSF is more efficient. Hence it is suggested that MAF can be replaced by SGSF. For real time issues, it is further implemented on reconfigurable architectures so as to achieve high speed, low cost, low power consumption and less area. Therefore SGSF is realized on FPGA platform to combine the advantages of both. Real time EEG and ECG signals are also considered for experimentation. The experimental results show that the proposed methodology (FPGA-SGSF) significantly reduces the processing time and preserves the actual features of the signal.

Performance analysis of two-degree of freedom fractional order PID controllers for robotic manipulator with payload.

The robotic manipulators are multi-input multi-output (MIMO), coupled and highly nonlinear systems. The presence of external disturbances and time-varying parameters adversely affects the performance of these systems. Therefore, the controller designed for these systems should effectively deal with such complexities, and it is an intriguing task for control engineers. This paper presents two-degree of freedom fractional order proportional-integral-derivative (2-DOF FOPID) controller scheme for a two-link planar rigid robotic manipulator with payload for trajectory tracking task. The tuning of all controller parameters is done using cuckoo search algorithm (CSA). The performance of proposed 2-DOF FOPID controllers is compared with those of their integer order designs, i.e., 2-DOF PID controllers, and with the traditional PID controllers. In order to show effectiveness of proposed scheme, the robustness testing is carried out for model uncertainties, payload variations with time, external disturbance and random noise. Numerical simulation results indicate that the 2-DOF FOPID controllers are superior to their integer order counterparts and the traditional PID controllers.