Reduction of large scale linear dynamic SISO and MIMO systems using modified particle swarm optimization algorithm

Abdullah, Hadeel N.

doi:10.1109/iciea.2016.7603571

Cited by 3 publications

(6 citation statements)

References 22 publications

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“…Consider Gn(s) be the SISO transfer function of the linear time-invariant system of order 'n' represented by the form [2]:…”

Section: Problem Formulation 21 Reduced-order Modelmentioning

confidence: 99%

“…When the particle swarm evolves to the next generation, each particle updates itself by tracking the two "optimal solutions" pbest and gbest. The update formula is as follows [2]: Where t represents the t-th generation; w is the inertia weight factors, which is the proportional coefficient of the current speed V (t), taking a random number between [0,1]; r1 and r2 are both random between [0,1] Number, c1 and c2 are learning factors that affect how fast the particle swarm follows the optimal solution. X (t + 1) represents the position of the next generation, which is obtained by adding the current position X (t) to the next generation speed V(t+1).…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…5. A comparison of the fitness value, using the same number of iteration, for the techniques proposed in this work indicated by the blue line for MCPSO and in the red line for bacterial foraging-particle swarm optimization (BF-PSO) techniques proposed in [15], and in the green line for the modified Particle swarm optimization (MPSO) techniques proposed in [2]. It can be seen from the figure that as the evolutionary algebra progresses, the fitness of the optimal global solution continues to decrease, and it stabilizes at the minimum value of 0.297 after ten generations for MCPSO.…”

Section: Examplementioning

confidence: 99%

“…To reduce cost and time in design and simplify implementation, reduced-order models (ROM) are very suitable for engineers in analysis, synthesis, and simulation. Different techniques are useful in the literature for MOR [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

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An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Abdullah¹

2021

IJIES

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The diverse engineering and scientific applications are stated through complex and high-order systems. The significant difficulties of these systems are the complications of modeling, analyzing, and controlling. It is easier to examine simpler models for more physical insights than more complex models and result in lower-ordering controllers that are easier to implement. The model order reduction (MOR) was used to simplify the computational difficulty of such complications and was later developed intensively for use with increasingly CDS. In this paper, a new Modified Chaos Particle Swarm Optimization (MCPSO) technique is employed to get a reduced-order model of a large scale system and design a Linear Quadratic Regulator (LQR) based controller. The mod uses the combination of advantages of basic PSO algorithms and chaotic algorithms. It becomes an excellent algorithm with fast convergence, few control parameters, simple execution, and avoidance of local extremes. In addition to combining the chaotic algorithm, CPSO also improved the weight parameter w, adjusting it to the dynamic attenuation direction. First, efficient reduced-order model parameters are obtained for original higher-order systems based on the MCPSO. Then linear quadratic regulator (LQR (controller parameters optimized for the reduced-order model. The goodness of the proposed method is evaluated through a numerical example. The experimental results indicate that the proposed technique’s reduced order model provides an excellent close approximation to the original system.

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“…Consider Gn(s) be the SISO transfer function of the linear time-invariant system of order 'n' represented by the form [2]:…”

Section: Problem Formulation 21 Reduced-order Modelmentioning

confidence: 99%

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

Abdullah¹

2021

IJIES

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show abstract

“…PSO is a populace grounded streamlining tool in which the framework is set through various arbitrarypossibility elements famous as particles. Every particle takes a position ( ) i X t and speed (V ) i t , which are refreshed by the accompanying equations: Different approaches are beneficial in texts for adjusting [16]- [19]. The proposed MPSO described as follows:…”

Section: Mpso Algorithmmentioning

confidence: 99%

A hybrid bacterial foraging and modified particle swarm optimization for model order reduction

Abdullah

2019

IJECE

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This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.

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Low Correlation Interference OFDM-NLFM Waveform Design for MIMO Radar Based on Alternating Optimization

Liu

Sun

et al. 2021

Sensors

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The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp waveform, a new OFDM-NLFM waveform with low peak auto-correlation sidelobe ratio (PASR) and peak cross-correlation ratio (PCCR) is obtained. IN-OFDM is the OFDM-NLFM waveform set currently with the lowest PASR and PCCR. Here we construct the optimization model of the OFDM-NLFM waveform set with the objective function being the maximum of the PASR and PCCR. Further, this paper proposes an OFDM-NLFM waveform set design algorithm inspired by alternating optimization. We implement the proposed algorithm by the alternate execution of two sub-algorithms. First, we keep both the sub-chirp sequence code matrix and sub-chirp rate plus and minus (PM) code matrix unchanged and use the particle swarm optimization (PSO) algorithm to obtain the optimal parameters of the NLFM signal’s time-frequency structure (NLFM parameters). Next, we keep current optimal NLFM parameters unchanged, and optimize the sub-chirp sequence code matrix and sub-chirp rate PM code matrix using the block coordinate descent (BCD) algorithm. The above two sub-algorithms are alternately executed until the objective function converges to the optimal solution. The results show that the PASR and PCCR of the obtained OFDM-NLFM waveform set are about 5 dB lower than that of the IN-OFDM.

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Reduction of large scale linear dynamic SISO and MIMO systems using modified particle swarm optimization algorithm

Cited by 3 publications

References 22 publications

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

An Improvement in LQR Controller Design based on Modified Chaotic Particle Swarm Optimization and Model Order Reduction

A hybrid bacterial foraging and modified particle swarm optimization for model order reduction

Low Correlation Interference OFDM-NLFM Waveform Design for MIMO Radar Based on Alternating Optimization

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