On Stability Analysis of Particle Swarm Optimization Algorithm

Gopal, Anshul; Sultani, Mohammad Mahdi; Bansal, Jagdish Chand

doi:10.1007/s13369-019-03991-8

Cited by 35 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, this converged point may or may not be optimum. Note that the convergence to the local optimum has been tackled via different methods, including the Lyapunov stability method [58] and the von Neumann stability criterion [59]. Due to the above two reasons, the stability of the controller can be well ensured.…”

Section: Discussionmentioning

confidence: 99%

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

2021

View full text Add to dashboard Cite

Robotic exoskeletons have great potential in the medical rehabilitation and augmentation of human performance in a variety of tasks. Proposing effective and adaptive control strategies is one of the most challenging issues for exoskeleton systems to work interactively with the user in dynamic environments and variable tasks. This research, therefore, aims to advance the state of the art of the exoskeleton adaptive control by integrating the excellent search capability of metaheuristic algorithms with the PID feedback mechanism. Specifically, this paper proposes an online adaptive PID controller for a multi-joint lower extremity exoskeleton system by making use of the particle swarm optimization (PSO) algorithm. Significant improvements, including a ‘leaving and re-searching mechanism’, are introduced into the PSO algorithm for better and faster update of the solution and to prevent premature convergence. In this research, a 9-DOF lower extremity exoskeleton with seven controllable joints is adopted as a test-bench, whose first-principle dynamic model is developed, which includes as many uncertain factors as possible for generality, including human–exoskeleton interactions, environmental forces, and joint unilateral constraint forces. Based upon this, to illustrate the effectiveness of the proposed controller, the human–exoskeleton coupled system is simulated in four characteristic scenarios, in which the following factors are considered: exoskeleton parameter perturbations, human effects, walking terrain switches, and walking speed variations. The results indicate that the proposed controller is superior to the standard PSO algorithm and the conventional PID controller in achieving rapid convergence, suppressing the undesired chattering of PID gains, adaptively adjusting PID coefficients when internal or external disturbances are encountered, and improving tracking accuracy in both position and velocity. We also demonstrate that the proposed controller could be used to switch the working mode of the exoskeleton for either performance or an energy-saving consideration. Overall, aiming at a multi-joint lower extremity exoskeleton system, this research proposes a PSO-based online adaptive PID controller that can be easily implemented in applications. Through rich and practical case studies, the excellent anti-interference capability and environment/task adaptivity of the controller are exemplified.

show abstract

Section: Discussionmentioning

confidence: 99%

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

2021

View full text Add to dashboard Cite

show abstract

“…where Iter j  is the food location that is the best situation that a crow could achieve so far. In (44),…”

Section: B Ccs Optimizationmentioning

confidence: 99%

“…In this table, the total operation cost in scenario III is solved for 20 trails using the proposed stochastic framework and the results of the best solution, the worst solution, standard deviation and CPU time are provided. To have better comparison, the results of the genetic algorithm (GA) [42], [43] particle swarm optimization (PSO) [44], [45] and original CCS algorithm are provided in the table. According to these results, the proposed modified CCS could get to a more optimal solution which is not attained by any other algorithms.…”

Section: B Ccs Optimizationmentioning

confidence: 99%

A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids

Yin¹,

Hajjiah

Jermsittiparsert³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Optimal scheduling of reconfigurable interconnected microgrids is a precious and critical task for the residential consumers especially with the integration of renewable energy sources, dispatchable units and energy storage systems. In this regard, not only the optimal scheduling of the microgrids in a realistic and correlated environment is a necessity, but also the guarantied security and the prevention of cyber-attacks are mandatory tasks for the operators. This article first addresses these issues by developing a novel framework based on blockchain for secured data transaction from the individual microgrids' components to the central control unit and then tries to find the optimal scheduling plan using stochastic programming based on point estimate method (PEM). Through such a hybrid PEM-blockchain based framework, the interconnected microgrids can supply the residential loads in a fully reliable, economic and secured structure. We also consider a social-economic framework to not only minimize the total operating cost of the microgrids, but also benefit the customers by enhancing the social factors through the optimal switching. Considering the complex and nonlinear nature of the problem, an effective corrected crow search (CCS) algorithm is deployed to find the most optimal operating point for the microgrids. The quality and capabilities of the proposed model are investigated using a practical residential interconnected microgrid. The results show that the optimal switching could reduce the total operation cost from $22,716 to $21,935 (3.56% reduction). Also, the average energy not supplied (AENS) has reduced from 1.4115 to 1.352 kWh/customer.yr (4.40% reduction), which are notable values. The results advocate the quality and functionality of the proposed framework.

show abstract

“…It is known that the quality of PSO is closely related to inertia weight where the local search ability of the algorithm is higher with smaller inertial weight and global search capability is stronger with larger inertial weight. To enable the algorithm maintaining higher search ability during the entire operation process, many methods have been proposed to adjust the inertia weight (Li et al, 2019a,b;Gopal et al, 2020;Wang et al, 2020;Wang, 2021;Zhang et al, 2021). However, the current inertia weight improvement methods have a close relationship with the iteration number and cannot adapt to the nonlinear variations well.…”

Section: A New Adaptive Inertial Weightmentioning

confidence: 99%

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Cheng

Zheng³

et al. 2021

Front. Neurorobot.

View full text Add to dashboard Cite

The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.

show abstract

On Stability Analysis of Particle Swarm Optimization Algorithm

Cited by 35 publications

References 29 publications

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

Online Adaptive PID Control for a Multi-Joint Lower Extremity Exoskeleton System Using Improved Particle Swarm Optimization

A Secured Social-Economic Framework Based on PEM-Blockchain for Optimal Scheduling of Reconfigurable Interconnected Microgrids

An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

Contact Info

Product

Resources

About