Trajectory Control of Robotic Manipulator using Metaheuristic Algorithms

Abstract

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman’s ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms.

In-Depth Review of Advanced Control Strategies and Cutting-Edge Trends in Robot Manipulators: Analyzing the Latest Developments and Techniques

“…By moving to the two-link robot manipulator, Rawat et al [32] presented the use of a proportional-integral-derivative (PID) controller to control the trajectory of a robotic manipulator.…”

“…These methods seek to enhance the manipulators' performance by optimizing their control parameters. For this, a variety of metaheuristic algorithms have been employed, such as Atom Search Optimization (ASO), Spotted Hyena Optimizer (SHO), Crowd Search Optimization (CSO), Emperor Penguin Optimization (EPO), Satin Bowerbird (SB) optimization, Differential Evolution (DE) Algorithm, Teaching-Learning Based Optimization (TLBO) Algorithm, Crow search algorithm (CSA), Whale optimization algorithm (WOA), Bat Optimization Algorithm (BAT) and Particle Swarm Optimization (PSO) [32], [33], [42], [111]. These algorithms improve performance measured by performance indices like integral time absolute error (ITAE), integral absolute error (IAE), etc.…”

“…while supplying the optimal gain values for the control parameters. Using statistical analysis and ranking tests, the efficacy of these algorithms has been assessed [32]. The findings demonstrate how metaheuristic optimization methods, with certain algorithms surpassing others in terms of optimization performance, greatly enhance the control of robotic manipulators.…”