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Stabilization and tracking control of an x-z type inverted pendulum system using Lightning Search Algorithm tuned nonlinear PID controller. ROBOTICA 2021. [DOI: 10.1017/s0263574721001727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Abstract
Inverted pendulum systems (IPSs) are mostly used to demonstrate the control rules for keeping the pendulum at a balanced upright position due to a slight force applied to the cart system. This paper presents an application for nonlinear control of an x-z type IPS by using a proportional-integral-derivative (PID) controller with newly established evolutionary tuning method Lightning Search Algorithm (LSA). A single, double and triple PID controller system is tested with the conventional and the self-tuning controllers to get a better understanding of the performance of the given system. The mathematical modelling of the x-z type IPS, the theoretical explanation of the LSA and the simulation analysis of the x-z type IPS is put forward entirely. The LSA algorithm solves the optimization problem of PID controller in an evolutionary way. The most effective way of making comparisons is evaluating the performance results with a well-known optimization technique or with the previous accepted results. We have compared the system’s performance with particle swarm optimization and with a classical control study in the literature. According to the simulation results, LSA-tuned PID controller has the ability to decrease the overshoot better than the conventional-tuned controllers. Finally, it can be concluded that the LSA-supported PID can better stabilize the pendulum angle and track the reference better for non-disturbed and the slightly disturbed systems.
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Control Synthesis as Machine Learning Control by Symbolic Regression Methods. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.
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