The research and development of quadruped robots is grown steadily in during the last two decades. Quadruped robots present major advantages when compared with tracked and wheeled robots, because they allow locomotion in terrains inaccessible. However, the design controller is a major problem in quadruped robots because of they have complex structure. This paper presents the optimization of two PID controllers for a quadruped robot to ensure single footstep control in a desired trajectory using a bio-inspired meta-heuristic soft computing method which is name the Grey Wolf Optimizer (GWO) algorithm. The main objective of this paper is the optimization of KP, KI and KD gains with GWO algorithm in order to obtain more effective PID controllers for the quadruped robot leg. The importance to this work is that GWO is used first time as a diversity method for a quadruped robot to tune PID controller. Moreover, to investigate the performance of GWO, it is compared with widespread search algorithms. Firstly, the computer aided design (CAD) of the system are built using SolidWorks and exported to MATLAB/SimMechanics. After that, PID controllers are designed in MATLAB/Simulink and tuned gains using the newly introduced GWO technique. Also, to show the efficacy of GWO algorithm technique, the proposed technique has been compared by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. The system is simulated in MATLAB and the simulation results are presented in graphical forms to investigate the controller's performance.
The inverted pendulum system is a challenging control problem in the control theory, which continually moves away from a stable state. The paper presents the design of a Proportional-Integral-Derivative (PID) controller for a single-input multi-output (SIMO) inverted pendulum system and using the Bees Algorithm (BA) to obtain optimal gains for PID controllers. The Bees Algorithm optimizes the gains so that the controller can move the cart to a desired position with the minimum amount of the change in the pendulum’s angle from the vertically upright position during the movement. The tuning aim is to minimize the control responses of the cart’s position and the pendulum’s angle in time domain. MATLAB/Simulink simulation has been performed to demonstrate that the effects on the system performance of PID controllers with optimal gains. The obtained results show that the tuning method by using the Bees Algorithm produced PID controllers successfully within the controller design criteria. Following a description of the inverted pendulum system and the Bees Algorithm, the paper gives the obtained simulation results for the system demonstrating the efficiency of the design.
The paper presents the design of a Linear Quadratic Regulator (LQR) controller for an Inverted Pendulum (IP) system using The Bees Algorithm (BA) to provide optimal parameters of LQR. Inverted Pendulum is a typical highly nonlinear and unstable system and widely used as a benchmark for testing different control techniques in control theory. LQR is an optimal control method that can achieve the closed loop control of multivariable dynamical systems with minimum control effort. In LQR controller design, state (Q) and control (R) weighting matrices are main design parameters which are defined by designer using trial and error method in general. Automatic tuning of the weighting matrices with an optimization algorithm ensure expected efficiency from LQR controller. Also the technique consider to design of time domain specifications like overshoot, rise time, settling time, and steady state error. In this paper, The Bees Algorithm optimizes the weighting matrices of LQR controller be able to move the cart in reference input with the minimum deflection of the pendulum's angular position. The tuning aim is to minimize the objective function which consists of time domain responses of system in MATLAB/Simulink. The paper gives the simulation results obtained for the system demonstrating the efficiency and robustness of the proposed design method of LQR controller. Index Terms-the bees algorithm, LQR controller tuning, optimal control, inverted pendulumAs show in Fig. 1, the free body diagram of the system. From the free body diagram, the following linearized (about =π, represents a small angle from the vertical upward direction) dynamic equations in the 384
Stabilizing of an inverted pendulum (IP) system is a main problem for researchers working on control theory. Balancing of an inverted pendulum system is one of the major benchmark problems in the control system community. This paper presents optimal tuning of linear quadratic regulator (LQR) controller with The Bees Algorithm (BA) for a linear inverted pendulum. In this paper, a metaheuristic approach which is a nature-inspired search method that mimics the foraging behavior of honey bees is used for design of LQR controller to obtain optimal performance. In LQR controller design, state (Q) and control (R) weighting matrices are basic parameters of LQR which are tuning by designer using trial and error method in usually. The Bees Algorithm optimizes the weighting matrices of the LQR controller so that it can move the cart to a desired position with the minimum change in pendulum's angle from vertically upright position during the movement. The tuned LQR controller is benchmarked on the linear inverted pendulum experimental device (IP02) that is manufactured by QUANSER Company. After description of the system and The Bees Algorithm, the paper gives the experimental results obtained from the IP02 system to demonstrating the efficiency of the tuning of the LQR controller. Simulation and experimental results are given graphically to show the success of controller. As a result of the paper, the performance of LQR controller shows the effectiveness of The Bees Algorithm which is a diversity method for provide an efficient solution to conventional trial and error design approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.