UDE-based sliding mode control of DC–DC power converters with uncertainties

The DC–DC power converter plays an important role in solar systems to provide the stable DC bus voltage, but which is easily subject to various uncertainties and disturbances in practical operation. In this paper, an uncertainty and disturbance estimator (UDE) based sliding mode control approach is applied to improve the performance of power converters. The UDE is designed for the estimation of both the matched and mismatched uncertainties. To address the mismatched uncertainties, an adaptive sliding mode function is constructed with the compensation of the estimated uncertainties, which renders a chattering-free robust control law. Simulation and experimental results illustrate that the proposed control scheme achieves good dynamic performance, strong robustness and chattering reduction in the presence of uncertainties and disturbances     

Optimization of oval–round pass design using genetic algorithm

The primary purpose of this paper is to propose a computer aided optimal design system to support a generalized oval–round pass design, which is widely used as both intermediate and final passes in the process of rod rolling. This system, which is based on a hybrid model and the genetic algorithm, is developed to improve the efficiency, to reduce the manufacturing errors, as well as to extend the useful life of rolls through uniform wear design. Generalized parametric equations are established for geometrical modeling, graphic plotting of oval–round passes, as well as calculation of the cross section area, contact area and the lengths of contact arcs along the cross section of round groove in the MATLAB programming environment. Moreover, these equations can also realize the parametric transformation between roll profile and mathematical models for the oval–round pass design and optimization. The genetic algorithm is employed for the optimal design of oval–round passes in this paper. The objective functions are formulated for minimization of power consumption in the rolling process, variances between ideal dimensions and design dimensions, as well as variances between the lengths of contact arcs. To reduce the complexity and computational burden of the system, some reliable empirical formulas for the calculations of contact area and contact arc length are applied. Finally, the proposed approach is applied to an oval–round pass design. Through simulation and comparison of results against experimental data acquired from literature, it is found that this system is reliable, effective and easier to use.     

Design of intelligent power controller for DC–DC converters using CMAC neural network

DC–DC converters are the devices which can convert a certain electrical voltage to another level of electrical voltage. They are very popularly used because of the high efficiency and small size. This paper proposes an intelligent power controller for the DC–DC converters via cerebella model articulation controller (CMAC) neural network approach. The proposed intelligent power controller is composed of a CMAC neural controller and a robust controller. The CMAC neural controller uses a CMAC neural network to online mimic an ideal controller, and the robust controller is designed to achieve L ₂ tracking performance with desired attenuation level. Finally, a comparison among a PI control, adaptive neural control and the proposed intelligent power control is made. The experimental results are provided to demonstrate the proposed intelligent power controller can cope with the input voltage and load resistance variations to ensure the stability while providing fast transient response and simple computation.     

Fault-tolerant control of wind turbines with hydrostatic transmission using Takagi–Sugeno and sliding mode techniques

In this paper, a Takagi–Sugeno Sliding Mode Observer for actuator fault diagnosis and fault-tolerant control scheme of wind turbines with hydrostatic transmission are presented. It will be shown that sliding mode techniques have the advantages that several actuator faults of the wind turbine drive train can be simultaneously reconstructed with one and the same observer and directly applied for fault compensation. Furthermore, a simple compensation approach is implemented by subtracting the reconstructed faults obtained from the (faulty) inputs. These corrected inputs act on the system as virtual actuators, such that the originally designed controller for the nominal, i.e. fault-free situation, can still be used. The fault reconstruction and fault tolerant control strategy are tested in simulations with several faults of different types.     

Design of min–max controller with predescribed sliding motion

Asymptotic stability in the sense of min–max theory is developed for a class of uncertain variable structure systems. Based on this unification between the theories, a min–max controller with predescribed sliding motion is designed. The proposed design method simplifies the one previously obtained for this purpose.     

Digital sliding mode controller design for multiple time-delay continuous-time transfer function matrices with a long input–output delay

This paper extends the dominant eigenvector-based sliding mode control (SMC) design methodology, which was originally developed for delay-free continuous-time processes with known parameters, to the case of multiple time-delay continuous-time processes with known/unknown parameters. In addition, this paper presents a new prediction-based Chebyshev quadrature digital redesign methodology for indirect design of the digital counterpart of the analog sliding mode controller (ASMC) for multiple time-delay continuous-time transfer function matrices with either a long input delay or a long output delay. An approximated discrete-time model and its corresponding continuous-time model are constructed for multiple time-delay continuous-time stable/unstable dynamical processes with known/unknown parameters, using first the conventional observer/Kalman filter identification (OKID) method. Then, an optimal ASMC is developed using the linear quadratic regulator (LQR) approach, in which the corresponding sliding surface is designed using the user-specified eigenvectors and the scalar sign function. For digital implementation of the proposed non-augmented low-dimensional ASMC, a digital counterpart is designed based on the existing prediction-based digital redesign method and the newly developed prediction-based Chebyshev quadrature digital redesign method. Finally, a non-augmented low dimensional digital observer with a long input or output dead time is constructed for the implementation of the digitally redesigned sliding mode controller, to improve the performances of multiple time-delay dynamical processes. The effectiveness of the proposed method has been verified by means of two illustrative examples.     

Detection of idea plagiarism using syntax–Semantic concept extractions with genetic algorithm

Plagiarism is increasingly becoming a major issue in the academic and educational domains. Automated and effective plagiarism detection systems are direly required to curtail this information breach, especially in tackling idea plagiarism. The proposed approach is aimed to detect such plagiarism cases, where the idea of a third party is adopted and presented intelligently so that at the surface level, plagiarism cannot be unmasked. The reported work aims to explore syntax-semantic concept extractions with genetic algorithm in detecting cases of idea plagiarism. The work mainly focuses on idea plagiarism where the source ideas are plagiarized and represented in a summarized form. Plagiarism detection is employed at both the document and passage levels by exploiting the document concepts at various structural levels. Initially, the idea embedded within the given source document is captured using sentence level concept extraction with genetic algorithm. Document level detection is facilitated with word-level concepts where syntactic information is extracted and the non-plagiarized documents are pruned. A combined similarity metric that utilizes the semantic level concept extraction is then employed for passage level detection. The proposed approach is tested on PAN13-14¹plagiarism corpus for summary obfuscation data, which represents a challenging case of idea plagiarism. The performance of the current approach and its variations are evaluated both at the document and passage levels, using information retrieval and PAN plagiarism measures respectively. The results are also compared against six top ranked plagiarism detection systems submitted as a part of PAN13-14 competition. The results obtained are found to exhibit significant improvement over the compared systems and hence reflects the potency of the proposed syntax-semantic based concept extractions in detecting idea plagiarism.     

A type-2 fuzzy logic controller design for buck and boost DC–DC converters

Conventional (type-1) fuzzy logic controllers have been commonly used in various power converter applications. Generally, in these controllers, the experience and knowledge of human experts are needed to decide parameters associated with the rule base and membership functions. The rule base and the membership function parameters may often mean different things to different experts. This may cause rule uncertainty problems. Consequently, the performance of the controlled system, which is controlled with type-1 fuzzy logic controller, is undesirably affected. In this study, a type-2 fuzzy logic controller is proposed for the control of buck and boost DC–DC converters. To examine and analysis the effects of the proposed controller on the system performance, both converters are also controlled using the PI controller and conventional fuzzy logic controller. The settling time, the overshoot, the steady state error and the transient response of the converters under the load and input voltage changes are used as the performance criteria for the evaluation of the controller performance. Simulation results show that buck and boost converters controlled by type-2 fuzzy logic controller have better performance than the buck and boost converters controlled by type-1 fuzzy logic controller and PI controller.     

Automated exploration of datapath and unrolling factor during power–performance tradeoff in architectural synthesis using multi-dimensional PSO algorithm

A novel algorithm for automated simultaneous exploration of datapath and Unrolling Factor (UF) during power–performance tradeoff in High Level Synthesis (HLS) using multi-dimensional particle swarm optimization (PSO) (termed as ‘M-PSO’) for control and data flow graphs (CDFGs) is presented. The major contributions of the proposed algorithm are as follows: (a) simultaneous exploration of datapath and loop UF through an integrated multi-dimensional particle encoding process using swarm intelligence; (b) an estimation model for computation of execution delay of a loop unrolled CDFG (based on a resource configuration visited) without requiring to tediously unroll the entire CDFG for the specified loop value in most cases; (c) balancing the tradeoff between power–performance metrics as well as control states and execution delay during loop unrolling; (d) sensitivity analysis of PSO parameter such as swarm size on the impact of exploration time and Quality of Results (QoR) of the proposed design space exploration (DSE) process. This analysis presented would assist the designer in pre-tuning the PSO parameters to an optimum value for achieving efficient exploration results within a quick runtime; (e) analysis of design metrics such as power, execution time and number of control steps of the global best particle found in every iteration with respect to increase/decrease in unrolling factor.The proposed approach when tested on a variety of data flow graphs (DFGs) and CDFGs indicated an average improvement in QoR of >28% and reduction in runtime of >94% compared to recent works.     

Analysis and design of a high efficiency bidirectional DC–DC converter for battery and ultracapacitor applications

This paper presents a high efficiency non-isolated bidirectional converter which can be employed as an interface circuit between ultracapacitors or batteries and DC bus voltage. All semiconductor devices in the proposed converter are soft switched while the control circuit remains PWM. So, the energy conversion through the converter is highly efficient. The proposed converter acts as a zero-voltage transition (ZVT) buck to charge an ultracapacitor or battery and acts as a ZVT boost to discharge an ultracapacitor or battery. The performance of the proposed converter with respect to abrupt load and operating mode change is shown through computer simulation results. The results confirm the aforementioned advantages and features of the proposed converter.     

Design of power controller in CDMA system with power and SIR error minimization

In this paper, an uplink power control problem is considered for code division multiple access (CDMA) systems. A distributed algorithm is proposed based on linear quadratic optimal control theory. The proposed scheme minimizes the sum of the power and the error of signal-to-interference ratio (SIR). A power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving a Kalman filter problem.     

esign of power controller in CDMA system with power and SIR error minimization

In this paper, an uplink power control problem is considered for code division multiple access （CDMA） systems. A distributed algorithm is proposed based on linear quadratic optimal control theory. The proposed scheme minimizes the sum of the power and the error of signal-to-interference ratio （SIR）. A power controller is designed by constructing an optimization problem of a stochastic linear quadratic type in Krein space and solving a Kalman filter problem.     

Maximum power tracking of a generic photovoltaic system via a fuzzy controller and a two-stage DC–DC converter

This study presents a new two-stage DC–DC converter for maximum power point tracking (MPPT) and a voltage boost of a generic photovoltaic (PV) system. An intelligent MPPT of PV system based on fuzzy logic control (FLC) is presented to adaptively design the proposed fuzzy controlled MPPT controller (FC-MPPTC) while a voltage boost controller (VBC) is used to fix the output voltage to a voltage level that is higher than the required operating voltage to the back-end grid impedance. Modeling and simulation on the PV system and the DC–DC converter circuit are achieved by state-space and the software Powersim. The PV string considered has the rated power around 600?VA under varied partial shadings. The FC-MPPTC and VBC are designed and realized by a DSP module (TMS320F2812) to adjust the duty cycle in the two-stage DC–DC converter. A special FLC algorithm is forged to render an MPPT faster and more accurate than conventional MPPT technique, perturb and observe (P&O). The simulations are intended to validate the performance of the proposed FC-MPPTC. Experiments are conducted and results show that MPPT can be achieved in a fast pace and the efficiency reaches over 90?%, even up to 96?%. It is also found that the optimized tracking speed of the proposed FC-MPPTC is in fact more stable and faster than the general P&O method with the boost voltage capable of offering a stable DC output.     

Automated fault detection in power distribution networks using a hybrid fuzzy–genetic algorithm approach

This paper describes the development of an intelligent technique based on artificial intelligence for automatically detecting incidents on power distribution networks. A hybrid combination of fuzzy logic and genetic algorithms (GAs) has been applied to detect faults in these networks. The robust nature of a fuzzy controller allows it to model functions of arbitrary complexity, while the maximising capabilities of GAs allow optimisation of the fuzzy design parameters to achieve optimal performance. The hybrid approach used in this paper builds on these individual strengths and seeks to blend fuzzy set and GAs techniques to compensate for their inadequacies. The technique for fault detection is described and verified with experiments on a 33 kV test system containing 12 busbars, eight transformers and eight line sections. The results obtained from the test data file of 500 test cases contain only one undetected case (0.2%), 458 correctly detected cases (91.6%) of actual faults and 41 cases (8.2%) where the protection system components either had not operated or had malfunctioned but were correctly identified by the incident detection system.     

Simplifying ADRC design with error‑based framework: case study of a DC–DC buck power converter

In this work, the problem of designing a robust control algorithm for a DC-DC buck power converter is investigated. The applied solution is based on a recently proposed error-based version of the active disturbance rejection control (ADRC) scheme, in which the unknown higher-order terms of the reference signal are treated as additional components of the system “total disturbance”. The motivation here is to provide a practical following of a reference voltage trajectory for the buck converter in specifc cases where neither the analytical form of the desired signal nor its future values are known a’priori, hence cannot be directly used for control synthesis. In this work, the application of the error-based ADRC results in a practically appealing control technique, with compact structure, simplifed control rule, and intuitive tuning (inherited from the conventional output-based ADRC scheme). Theoretical, numerical, and experimental results are shown to validate the efcacy of the error-based ADRC in buck converter control, followed by a discussion about the revealed theoretical and practical limitations of this approach.     

Optimisation of the weighting functions of an H ∞ controller using genetic algorithms and structured genetic algorithms

In this article, the optimisation of the weighting functions for an H _∞ controller using genetic algorithms and structured genetic algorithms is considered. The choice of the weighting functions is one of the key steps in the design of an H _∞ controller. The performance of the controller depends on these weighting functions since poorly chosen weighting functions will provide a poor controller. One approach that can solve this problem is the use of evolutionary techniques to tune the weighting parameters. The article presents the improved performance of structured genetic algorithms over conventional genetic algorithms and how this technique can assist with the identification of appropriate weighting functions’ orders.     

Second-order sliding mode attitude controller design of a small-scale helicopter基于二阶滑模的小型直升机姿态控制器设计

In this paper, the attitude control of a small-scale helicopter is investigated. The main rotor flapping dynamics is explicitly explored to improve the control performance. A two-layer control architecture is adopted: the inner loop controller is designed combining second-order sliding mode control with extended state observer to control the angular rates and yield good robustness properties with respect to model uncertainties; the outer loop controller is used to control the attitude. Experimental results show that the proposed controller yields excellent performance and robustness.     

Robust flight control system design using H∞ loop-shaping and recessive trait crossover genetic algorithm

A proposed approach to robust controller design is introduced. This approach combines the recessive trait crossover genetic algorithm with the loop-shaping design procedure using H_∞ synthesis. The requirements, design and simulation of a flight control system for precision tracking task are considered. The proposed method is applied to design a control system for the F-16 fighter aircraft model. The flight simulations reveal that the desired performance objectives are achieved and that the controller provides acceptable performance in spite of modeling errors and plant parameter variations.     

Application of permutation genetic algorithm for sequential model building–model validation design of experiments

The work presented in this paper is motivated by a complex multivariate engineering problem associated with engine mapping experiments, which require efficient design of experiments (DoE) strategies to minimise expensive testing. The paper describes the development and evaluation of a Permutation Genetic Algorithm (PermGA) to enable an exploration-based sequential DoE strategy for complex real-life engineering problems. A known PermGA was implemented to generate uniform OLH DoEs, and substantially extended to support generation of model building–model validation (MB–MV) sequences, by generating optimal infill sets of test points as OLH DoEs that preserve good space-filling and projection properties for the merged MB + MV test plan. The algorithm was further extended to address issues with non-orthogonal design spaces, which is a common problem in engineering applications. The effectiveness of the PermGA algorithm for the MB–MV OLH DoE sequence was evaluated through a theoretical benchmark problem based on the Six-Hump-Camel-Back function, as well as the Gasoline Direct Injection engine steady-state engine mapping problem that motivated this research. The case studies show that the algorithm is effective in delivering quasi-orthogonal space-filling DoEs with good properties even after several MB–MV iterations, while the improvement in model adequacy and accuracy can be monitored by the engineering analyst. The practical importance of this work, demonstrated through the engine case study, is that significant reduction in the effort and cost of testing can be achieved.