Abstract
The control of a robotic manipulators is a complicated task due to its high model uncertainties and disturbances. In this research, we study two neural network architectures to control robotic manipulators. The first method consists of using an optimized PD controller combined with a multi-layer perceptron neural network (MLPNN). The second method relies on a combination of MLPNN and a feedforward radial basis function neural network (BBFNN) compensator. Heuristic methods such as genetic optimization and pattern search optimization are used to find the optimal weights for these architectures. Extensive simulations are conducted in MATLAB environment to evaluate the feasibility of the optimized neural controllers in trajectory tracking.
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Appendix
Appendix
In this section, we present the optimal weight values found using both GA and PS optimization techniques. These weights were used to generate plots in Figs. 3, 4, 5 and 6. First, we present the optimized weights for the MLPNN and RBFNN structure obtained through Pattern Search Optimization. Below, \({W}_{1}\), \({W}_{2}\) and \({W}_{3}\) represent the weights of the first, second and third layers of the MLPNN.
Furthermore, below are the weights, \(W\), for the RBFNN feedforward compensator
We now present the optimized weights for the MLPNN and RBFNN structure obtained through Genetic optimization.
The optimized gains and weights for the Optimized PD based MLPNN structure are presented below. We first however present the GA optimized gains of the PD controller. These were defined as:
After optimizing the weights of the MLPNN through Pattern Search, we define the weights as:
Lastly, after optimizing the weights of the MLPNN through GA, we define the weights as
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Cherfouh, K., Gu, J., Farooq, U., Asad, M.U., Qureshi, K.K. (2023). Control of Robotic Manipulator Using Optimized Neural Networks. In: Balas, V.E., Jain, L.C., Balas, M.M., Baleanu, D. (eds) Soft Computing Applications. SOFA 2020. Advances in Intelligent Systems and Computing, vol 1438. Springer, Cham. https://doi.org/10.1007/978-3-031-23636-5_2
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