Strategic iniitialization of a hybrid particle swarm optimization-simullated annealing algorithm (HPSOSA) for PID controller design for a nonlinear system

Tharmalingam, Mathiruban; Raahemifar, Kaamran

doi:10.1109/ccece.2012.6334942

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The first PSO algorithm was used to model the movements of a flock of birds searching for food. The PSO algorithm has been used to solve many engineering problems including optimization of PID controller gains for complicated nonlinear systems [28][29][30][31]. The PSO algorithm is shown in Algorithm 5-1. ii.…”

Section: Parameter Optimization Using Particle Swarm Optimizationmentioning

confidence: 99%

Dictionaries and algorithms for sparsity constrained image reconstruction

Tharmalingam¹

2021

Preprint

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There has been a growing interest in the different types of dictionaries that can be used in image processing applications. We propose a hybrid dictionary composed of transform based atoms and additional nonlinear atoms generated using the polynomial, rectangular and exponential functions. The additional nonlinear atoms improve signal reconstruction quality for both transient and smooth signals. To further improve signal reconstruction quality, we optimize the hybrid dictionary using training samples from the signal. We also propose a signal coding algorithm that generates additional atoms by performing a circular shift on the provided dictionary prior to coding the signal. We have evaluated the proposed methods against existing predefined dictionaries by visually examining the reconstructed images as well as evaluating the peak signal to noise ratio of the reconstructed signal. All methods proposed in this thesis improved signal reconstruction quality however; we require an in-depth cost analysis study to evaluate its limitations.

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Section: Parameter Optimization Using Particle Swarm Optimizationmentioning

confidence: 99%

Dictionaries and algorithms for sparsity constrained image reconstruction

Tharmalingam¹

2021

Preprint

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“…Zhang and Hu (2008) investigated different PID controllers tuned with SA for motion control systems that have constant disturbance. A hybrid algorithm of modified PSO and SA is used to design a PID controller for the inverted pendulum problem (Tharmalingam and Raahemifar, 2012).…”

Section: Introductionmentioning

confidence: 99%

Tuning a digital multivariable controller for a lab-scale helicopter system via simulated annealing and evolutionary algorithms

Moness

Moustafa

2014

Transactions of the Institute of Measurement and Control

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Helicopter systems are considered a complex and challenging control problem due to strong couplings and high non-linearities. In this paper, simulated annealing (SA), as one of the leading methods in search and optimization, is applied to tune a multivariable controller of a lab-scale helicopter system. The lab-scale helicopter system is a multivariable experimental aerodynamic test rig that resembles the behaviour of a real helicopter. The control objectives are quickly to reach a desired position or track a trajectory. A centralized cross-coupled PID controller is used to achieve these objectives. First, SA optimizations are carried out with 24 different initial configurations. Then, the best results of these SA configurations are compared with other controllers obtained with evolutionary algorithms (EAs) of genetic algorithms (GAs), modified particle swarm optimization (MPSO) and differential evolution (DE). The comparisons are based on statistical measures of 20 independent trials, non-linear computer simulations of different input signals and real-time measurements for various commands of positions or trajectories. Results show that SA obtained the best performance index and acceptable time-domain performance on reaching hovering point, following a step command and tracking a sine trajectory compared with the investigated EAs.

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