Application of Combinatorial Interaction Design for DC Servomotor PID Controller Tuning

Sahib, Mouayad A.; Ahmed, Bestoun S.; Potrus, Moayad Yousif

doi:10.1155/2014/576868

Cited by 17 publications

(13 citation statements)

References 28 publications

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“…For the sake of testing the performance our strategy, it was applied to the CA construction, which represents a useful application of combinatorial optimization. CA is a mathematical model that has been used successfully in different real applications such as testing configurable software systems [45], controlling DC servo motors [46], gene expression regulation [47], advance material testing [48], and many other applications. By formulating this problem mathematically and implementing the strategy, the search process is detailed as follows:…”

Section: Fuzzy Adaptive Swarm Vs-citmentioning

confidence: 99%

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Zamli

Ahmed

Mahmoud

et al. 2018

Swarm Intelligence - Volume 3: Applications

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Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact; variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem [1]. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system [2,3,4], to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems.We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.

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Section: Fuzzy Adaptive Swarm Vs-citmentioning

confidence: 99%

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Zamli

Ahmed

Mahmoud

et al. 2018

Swarm Intelligence - Volume 3: Applications

Self Cite

View full text Add to dashboard Cite

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“…(i) Proportional-derivative (PD), proportional-integral (PI), and proportional-integral-derivative (PID) (ii) Phase-lag compensator, phase-lead compensator, and lag-lead compensator Currently, PI, PD, and PID controllers are widely used in academia and in industrial control systems because they have a simple structure and simple design procedure and they are cost-effective [1,14,[33][34][35][36]. However, the design procedure of PD, PI, and PID controllers involves determining the P, I, and D gains which are usually tuned using a trial-anderror procedure [1,36].…”

Section: Introductionmentioning

confidence: 99%

“…However, the design procedure of PD, PI, and PID controllers involves determining the P, I, and D gains which are usually tuned using a trial-anderror procedure [1,36]. Therefore, poorly tuned controller gains reduce the accuracy and the dynamic performance of the entire control system.…”

Section: Introductionmentioning

confidence: 99%

Rotor Speed Control of a Direct-Driven Permanent Magnet Synchronous Generator-Based Wind Turbine Using Phase-Lag Compensators to Optimize Wind Power Extraction

Hamatwi

Davidson

Gitau

2017

Journal of Control Science and Engineering

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Due to the intermittent nature of wind, the wind power output tends to be inconsistent, and hence maximum power point tracking (MPPT) is usually employed to optimize the power extracted from the wind resource at a wide range of wind speeds. This paper deals with the rotor speed control of a 2 MW direct-driven permanent magnet synchronous generator (PMSG) to achieve MPPT. The proportional-integral (PI), proportional-derivative (PD), and proportional-integral-derivative (PID) controllers have widely been employed in MPPT studies owing to their simple structure and simple design procedure. However, there are a number of shortcomings associated with these controllers; the trial-and-error design procedure used to determine the P, I, and D gains presents a possibility for poorly tuned controller gains, which reduces the accuracy and the dynamic performance of the entire control system. Moreover, these controllers' linear nature, constricted operating range, and their sensitivity to changes in machine parameters make them ineffective when applied to nonlinear and uncertain systems. On the other hand, phase-lag compensators are associated with a design procedure that is well defined from fundamental principles as opposed to the aforementioned trialand-error design procedure. This makes the latter controller type more accurate, although it is not well developed yet, and hence it is the focus of this paper. The simulation results demonstrated the effectiveness of the proposed MPPT controller.

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“…Borodai and Grunskii [4] used it for hardware testing and Lei et al [5] applied it for concurrency testing. Sahib et al [6] apply the pairwise method to control DC servo motors. Shasha et al [7] used it for gene expression regulation and Hoskins et al [8] used it for performance evaluation of communication systems.…”

Section: Introductionmentioning

confidence: 99%

“…In control system applications, tuning the parameters of a controller, such as the PID controller, has been proposed to represent another application of combinatorial optimization techniques [6]. In determining the optimal PID parameters, several heuristic methods have been introduced, such as genetic algorithm [16, 33], neural network [34], fuzzy based approach [35], particle swarm optimization [6, 15, 36] and chaotic ant swarm [9] techniques. Most systems arising in practice have time varying parameters which will affect the performance of the designed controller.…”

Section: Introductionmentioning

confidence: 99%

Optimum Design of PIλDμ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design

et al. 2016

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Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly.

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Application of Combinatorial Interaction Design for DC Servomotor PID Controller Tuning

Cited by 17 publications

References 28 publications

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Rotor Speed Control of a Direct-Driven Permanent Magnet Synchronous Generator-Based Wind Turbine Using Phase-Lag Compensators to Optimize Wind Power Extraction

Optimum Design of PIλDμ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design

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