scholarly journals Active Disturbance Rejection Control Design Using the Optimization Algorithm for a Hydraulic Quadruped Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yuqi Fan ◽  
Junpeng Shao ◽  
Guitao Sun ◽  
Xuan Shao
Keyword(s):  
Disturbance Rejection ◽  
Search Algorithm ◽  
Parameter Tuning ◽  
Quadruped Robot ◽  
Search Problem ◽  
Lévy Flight ◽  
Control Performance ◽  
External Interference ◽  
Levy Flight ◽  
Active Disturbance Rejection

To increase the robustness and control precision of a hydraulic quadruped robot and simultaneously enhance the dynamic and steady characteristic of the hydraulic system, an active disturbance rejection controller (ADRC) tuned using the Lévy-flight beetle antennae search algorithm (LBAS) was proposed. Moreover, the designed controller was used in the hydraulic quadruped robot to enhance the control performance and restrain the disturbances. The use of the Lévy-flight trajectory in the advanced algorithm can help increase the search speed and iteration accuracy. In the LBAS-ADRC, the parameter tuning method is adopted to develop the active disturbance rejection controller enhanced using the beetle antennae search algorithm. When implemented in the hydraulic quadruped robot, the LBAS-ADRC can ensure satisfactory dynamic characteristics and stability in the presence of external interference. In particular, in the proposed method, the ADRC parameter search problem is transformed to a sixteen-dimensional search problem, the solution of which is identified using the Lévy-flight beetle antennae search algorithm. Moreover, three different algorithms are implemented in the active disturbance rejection controller tuning problem to demonstrate the control performance of the proposed controller. The analysis results show that the proposed controller can achieve a small amplitude overshoot under complex and changeable environments.

Active disturbance rejection control for small unmanned helicopters via Levy flight-based pigeon-inspired optimization

2017 ◽  
Vol 89 (6) ◽  
pp. 946-952 ◽  
Author(s):  
Daifeng Zhang ◽  
Haibin Duan ◽  
Yijun Yang
Keyword(s):  
Swarm Intelligence ◽  
Attitude Control ◽  
Disturbance Rejection ◽  
Optimization Problems ◽  
Active Disturbance Rejection Control ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control

Purpose The purpose of this paper is to propose a control approach for small unmanned helicopters, and a novel swarm intelligence algorithm is used to optimize the parameters of the proposed controller. Design/methodology/approach Small unmanned helicopters have many advantages over other unmanned aerial vehicles. However, the manual operation process is difficult because the model is always instable and coupling. In this paper, a novel optimized active disturbance rejection control (ADRC) approach is presented for small unmanned helicopters. First, a linear attitude model is built in hovering condition according to small perturbation linearization. To realize decoupling, this model is divided into two parts, and each part is equipped with an ADRC controller. Finally, a novel Levy flight-based pigeon-inspired optimization (LFPIO) algorithm is developed to find the optimal ADRC parameters and enhance the performance of controller. Findings This paper applies ADRC method to the attitude control of small unmanned helicopters so that it can be implemented in practical flight under complex environments. Besides, a novel LFPIO algorithm is proposed to optimize the parameters of ADRC and is proved to be more efficient than other homogenous methods. Research limitations/implications The model of proposed controller is built in the hovering action, whereas it cannot be used in other flight modes. Practical implications The optimized ADRC method can be implemented in actual flight, and the proposed LFPIO algorithm can be developed in other practical optimization problems. Originality/value ADRC method can enhance the response and robustness of unmanned helicopters which make it valuable in actual environments. The proposed LFPIO algorithm is proved to be an effective swarm intelligence optimizer, and it is convenient and valuable to apply it in other optimized systems.

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

scholarly journals A gravitational search algorithm based on levy flight

2021 ◽  
Vol 1865 (4) ◽  
pp. 042006
Author(s):  
Jing Zhao ◽  
Haidong Zhu ◽  
Yinhua Hu ◽  
Enjun Hu ◽  
Baole Huang ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Lévy Flight ◽  
Levy Flight ◽  
Gravitational Search

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

scholarly journals Attitude Active Disturbance Rejection Control of the Quadrotor and Its Parameter Tuning

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Suiyuan Shen ◽  
Jinfa Xu
Keyword(s):  
Flight Control ◽  
Disturbance Rejection ◽  
Sliding Mode ◽  
Dynamic Performance ◽  
External Disturbance ◽  
Parameter Tuning ◽  
Adaptive Genetic Algorithm ◽  
Active Disturbance Rejection ◽  
Disturbance Rejection Control ◽  
Better Than

The internal uncertainty and external disturbance of the quadrotor will have a significant impact on flight control. Therefore, to improve the control system’s dynamic performance and robustness, the attitude active disturbance rejection controller (ADRC) of the quadrotor is established. Simultaneously, an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) is used to optimize the controller parameters to solve the problem that the controller parameters are difficult to tune. The performance of the proposed ADRC is compared with that of the sliding mode controller (SMC). The simulations revealed that the dynamic performance and robustness of the ADRC is better than that of the SMC.

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