Optimal Augmented Linear and Nonlinear PD Control Design  for Parallel Robot Based on PSO Tuner

Humaidi, Amjad J.; Oglah, Ahmed A.; Abbas, Saad Jabbar; Ibraheem, Ibraheem K.

doi:10.15866/iremos.v12i5.16298

Cited by 13 publications

(9 citation statements)

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“…The optimization algorithm based on PSO [32][33][34][35][36][37][38][39][40] results in optimal design parameters for SMC and finding the optimal values of (𝑐 = 0.1), (𝐾 = 40) for ( 15) and (𝐾 1 = 6.93), (𝐾 2 = 600.59) for (17). The values of (𝐾 𝑝 ), (𝐾 𝑑 ), ( 𝛽 1 ), ( 𝛽 2 ) and (𝛽 3 ) are calculated according to (7) and (14) when choosing (𝑤 𝑐 = 24.5 𝑟𝑎𝑑/𝑠𝑒𝑐) and (𝑤 𝑜 = 4𝑤 𝑐 ).…”

Section: Numerical Simulation and Discussionmentioning

confidence: 99%

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Alawad¹,

Humaidi²,

Al-Araji³

2022

Journal of Robotics and Control

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Sliding mode (SM) has been selected as the controlling technique, and the state observer (SO) design is used as a component of active disturbance rejection control (ADRC) to reduce the knee position trajectory for therapeutic purposes. The suggested controller will improve the needed position performances for the Exoskeleton system when compared to the proportional-derivative controller (PD) and SMC as feed-forward in the ADRC approach, as shown theoretically and through computer simulations. Simulink tool is used in this comparison to analyze the nominal case and several disruption cases. The results of mathematical modeling and simulation studies demonstrated that SMC with a disturbance observer strategy performs better than the PD control system and SMC in feed-forward with a greater capacity to reject disturbances and significantly better than these controllers. Performance indices are used for numerical comparison to demonstrate the superiority of these controllers.

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Section: Numerical Simulation and Discussionmentioning

confidence: 99%

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Alawad¹,

Humaidi²,

Al-Araji³

2022

Journal of Robotics and Control

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“…A unique communication among the variant dimensions of the search space is provided by the objective functions. Experimental research showed that the PSO algorithm is a successful optimization tool [36][37][38].…”

Section: Particles Swarm Optimizationmentioning

confidence: 99%

Optimum of fractional order fuzzy logic controller with several evolutionary optimization algorithms for inverted pendulum

Ghaleb

Oglah

Humaidi

et al. 2023

IRASE

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This paper compared the performance between Integer Order Fuzzy PID (IOFPID) and Fractional Order Fuzzy PID (FOFPID) controllers for inverted pendulum system as a controlling plant. The parameters of each controller were tuned with four evolutionary optimization algorithms (Social Spider Optimization (SSO), Swarm Optimization (PSO), Genetic Algorithm (GA), and Particle Ant Colony Optimization (ACO)). The comparisons were carried out between the two controllers IOFPID and FOFPID, as well as among the four optimization algorithms for the two controllers. The results of comparisons proved that the FOFPID controller with SSO has achieved the best time response characteristics and the least tuning time.

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“…To provide a robust controller in AQM routers, an ANFIS like-PID controller is constructed as an intelligent congestion avoidance controller. Figure 7 illustrates the structure of an ANFIS like-PID controller [24], [25]. The PID equation is described in (15).…”

Section: Anfis Like-pid Controllermentioning

confidence: 99%

Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Berbek

Oglah

2022

IJEECS

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Routers are vital during network congestion. All routers have input and output packet buffers. V<span lang="EN-US">Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.</span>

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Optimal Augmented Linear and Nonlinear PD Control Design for Parallel Robot Based on PSO Tuner

Cited by 13 publications

References 0 publications

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Observer Sliding Mode Control Design for lower Exoskeleton system: Rehabilitation Case

Optimum of fractional order fuzzy logic controller with several evolutionary optimization algorithms for inverted pendulum

Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

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