Abstract
In this study, an improved particle swarm optimization (IPSO)-based neural network controller (NNC) is proposed for solving a real unstable control problem. The proposed IPSO automatically determines an NNC structure by a hierarchical approach and optimizes the parameters of the NNC by chaos particle swarm optimization. The proposed NNC based on an IPSO learning algorithm is used for controlling a practical planetary train-type inverted pendulum system. Experimental results show that the robustness and effectiveness of the proposed NNC based on IPSO are superior to those of other methods.
Highlights
The multilayer neural network is one of the most popular methods for solving real-time signal processing, system control, and signal prediction [1,2,3] problems
The mathematical model of the derived planetary train inverted pendulum system [16] is based on initial conditions and input signals to predict the dynamic behavior of the system
Respectively, and θ0 and θ0 denote the actual speed of the pendulum and the referred speed of the controller, particle swarm optimization (PSO)-based neural network controller (NNC), and genetic algorithms (GAs)-based NNC
Summary
The multilayer neural network is one of the most popular methods for solving real-time signal processing, system control, and signal prediction [1,2,3] problems. Several researchers [12,13,14,15] have used neural networks and evolutionary computation methods for the inverted pendulum control. An efficient improved particle swarm optimization (IPSO)-based neural network controller (NNC) was proposed for controlling a planetary train-type inverted pendulum system. IPSO learning algorithm is used to automatically determine the neural network structure and adjust the NNC parameters to avoid over-fitting and to enhance global search capabilities. The experimental results show that the proposed IPSO-based NNC has a better control performance of the set-point and periodic square commands than other methods. The proposed improved particle swarm optimization can automatically determine the neural network structure and adjust the parameters of a neural network controller.
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