Decentralized Intelligent PID based controller tuned by Evolutionary Algorithm for Double Link Flexible Robotic Manipulator with Experimental Validation

In this paper, a development of decentralized intelligent proportional–integral–derivative (PID) controller for multi input multi output (MIMO) controller of double link flexible robotics manipulator is presented. Simultaneous optimization method is implemented in optimizing the parameters. The controllers are incorporated with optimization algorithm that is PSO to find out the parameters of the PID controllers. Numerical simulation was carried out in MATLAB/Simulink to evaluate the system in term of tracking capability and vibration suppression for both links. The optimal values of PID controller parameters that were achieved via off-line tuning using PSO were tested experimentally on the DLFRM experimental test rig. Experimental results show that the proposed control algorithm managed to control the system to reach desired angle for both hub at lower overshoot. Meanwhile, the vibration reduction shows improvement for both link 1 and 2. This signifies that, the PSO algorithm is very effective in optimizing the PID parameters for double link flexible robotics manipulator

Intelligent proportional-integral-derivate controller using metaheuristic approach via crow search algorithm for vibration suppression of flexible plate structure

Proportional-integral-derivate (PID) controller has gained popularity since the advancement of smart devices especially in suppressing the vibration on flexible structures using different approaches. Such structures required accurate and reliable responses to prevent system failures. Swarm intelligence algorithm (SIA) is one of the optimization methods based on nature that managed to solve real-world problems. Crow search is a well-known algorithm from the SIA group that can discover optimum solutions in both local and global searches by utilizing fewer tuning parameters compared to other methods. Hence, this study aimed to simulate a PID controller tuned by SIA via crow search for vibration cancellation of horizontal flexible plate structures. Prior to that, an accurate model structure is developed as a prerequisite for PID controller development. After the best model is achieved, the proportional-integral-derivative-crow-search (PID-CS) performance was compared to a traditional tuning approach known as the Ziegler Nichols (ZN) to validate its robustness. The result revealed the PID-CS outperformed the proportional-integral-derivative-Ziegler Nichols (PID-ZN) with attenuation values of 44.75 and 42.74 dB in the first mode of vibration for single sinusoidal and real disturbances, respectively. In addition, the value of mean squared error (MSE) for PID-ZN and PID-CS for single sinusoidal disturbance are 0.0167 and 0.0081, respectively. Meanwhile, PID-ZN and PID-CS achieved 2.3981 × 10−4 and 2.3737 × 10−4 when they were exerted with real disturbance. This proves that the PID-CS is more accurate compared to the PID-ZN as it achieved the lowest MSE value

Intelligent modeling of double link flexible robotic manipulator using artificial neural network

The paper investigates the application of the Artificial Neural Network (ANN) in modeling of double-link flexible robotic manipulator (DLFRM). The system was categorized under multi-input multi-output. In this research, the dynamic models of DLFRM were separated into single-input single-output in the modeling stage. Thus, the characteristics of DLFRM were defined separately in each model and the coupling effect was assumed to be minimized. There are four discrete SISO model of double link flexible manipulator were developed from torque input to the hub angle and from torque input to the end point accelerations of each link. An experimental work was established to collect the input-output data pairs and used in developing the system model. Since the system is highly nonlinear, NARX model was chosen as the model structure because of its simplicity. The nonlinear characteristic of the system was estimated using the ANN whereby multi-layer perceptron (MLP) and ELMAN neural network (ENN) structure were utilized. The implementation of the ANN and its’ effectiveness in developing the model of DLFRM was emphasized. The performance of the MLP was compared to ENN based on the validation of the mean-squared error (MSE) and correlation tests of the developed models. The results indicated that the identification of the DLFRM system using the MLP outperformed the ENN with lower mean squared prediction error and unbiased results for all the models. Thus, the MLP provides a good approximation of the DLFRM dynamic model compared to the ENN