Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

Abstract

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 𝑒<sup>-6</sup> and (PSO)=3.4948 𝑒<sup>-4</sup>.

Static and Dynamic Path Planning Algorithms Design for a Wheeled Mobile Robot Based on a Hybrid Technique

“…The speed and position update methods for the i th particle at the (𝑘 + 1 ) iteration are represented in Eqs. ( 4) and ( 5), respectively [26][27][28][29][30].…”

Efficient time-series forecasting of nuclear reactions using swarm intelligence algorithms

“…Figure 1. The chemical structure of nuclear reaction with the help of radio biological efficiency [19] In recent years' cancer therapy with heavier ions (C) has been developed and is now being used on humans two places in the world (GSI, Germany and China, Japan). Nuclear reactions are used since it is the optimum ion for killing most cells in the tumor and least in the healthy tissue surrounding the tumor.…”

Single phase second order sliding mode controller for complex interconnected systems with extended disturbances and unknown time-varying delays

“…Theorem 2. Study the interconnected system (1) with the proposed controller (14) where the switching manifold is demarcated by (2). Then, the state variables of (1) forward to the switching manifold (2) in finite time and stay on sliding mode under the controller (14) when scalar gains filling the following settings:…”

“…, where 𝑠 𝑖 (𝑦 𝑖 (𝑡), 𝑡) is switching manifold as (2). By differentiating 𝑉 𝑖 (𝑠 𝑖 ) and using the (13), we get (16).…”

“…5, October 2022: 4852-4860 4858where the mismatched uncertainties in state matrix and delayed state matrix are respectively 𝛥𝐴 1 (𝑡) = [0 0 1] 𝑇 𝛴 1 (𝑥 1 (𝑡), 𝑡)[1 1 0] with 𝛴 1 (𝑥 1 (𝑡), 𝑡) = 0.14 𝑠𝑖𝑛( 0.1𝑡) and 𝛥𝐴 1𝑑 (𝑡) = [0 1 0] 𝑇 𝛴 1𝑑 (𝑥 1 (𝑡), 𝑥 1𝑑 , 𝑡) × [1 1 0] with 𝛴 1𝑑 (𝑥 1 (𝑡), 𝑥 1𝑑 , 𝑡) = 0.22 𝑠𝑖𝑛( 0.1𝑡). The external perturbation input is assumed to be ‖𝜓 ̇1(𝑡)‖ ≤ 𝜐 01 + 𝜐 11 (‖𝑥 1 ‖) + 𝜐 21 (‖𝑥 1 ‖)2 with 𝜐 01 = 0.12, 𝜐 11 = 0.32, and 𝜐 21 = 0.55.Subsystem II: 𝑛 2 = 3, 𝑚 2 = 2, 𝑖 = 2, 𝑗 = 1, and the dynamics are specified as(23): uncertainties in state matrix and delayed state matrix are respectively 𝛥𝐴 2 (𝑡) = [0 0 1] 𝑇 𝛴 2 (𝑥 2 (𝑡), 𝑡)[1 1 0] with 𝛴 2 (𝑥 2 (𝑡), 𝑡) = 0.28 𝑠𝑖𝑛( 0.1𝑡) and 𝛥𝐴 2𝑑 (𝑡) = [0 1 0] 𝑇 𝛴 2𝑑 (𝑥 2 (𝑡), 𝑥 2𝑑 , 𝑡) × [1 1 0]with 𝛴 2𝑑 (𝑥 2 (𝑡), 𝑥 2𝑑 , 𝑡) = 0.41 𝑠𝑖𝑛( 0.1𝑡). The external disturbance input is assumed to be ‖𝜓 ̇2(𝑡)‖ ≤ 𝜐 02 + 𝜐 12 (‖𝑥 2 ‖) + 𝜐 22 (‖𝑥 2 ‖)2 with 𝜐 02 = 0.12, 𝜐 12 = 0.22, and 𝜐 22 = 0.34.…”