Abstract
This paper investigates the use of a new sliding mode control for the output voltage regulation of boost converter under parametric uncertainties of load resistance and input voltage. Owing to the fact that the proposed scheme employs the adaptation law; therefore, apriori knowledge about the upper bound value of uncertainties is not required while selecting the controller gains. Moreover, the stability analysis of the closed-loop system guarantees the finite-time convergence of output voltage to the desired value while ensuring robustness against uncertainties. The numerical simulation and hardware analysis illustrate the effective performance of the developed strategy.
1 Introduction
The last two decades have seen significant growth in the integration of renewable energy sources (RES) into the power grid to expand the worldwide penetration of such energy sources. The RES are used as alternative energy sources in the localized grid, known as microgrids, that can be detached from the main grid when required. With the use of power converters, these microgrids can take the form of AC and/or DC microgrids (; ; ; ). Clean energy, such as fuel cells, photovoltaic energy, wind, and so on, are summarily endorsed in microgrids to safeguard the Earth (). However, the electrical features of unpolluted energy are unsympathetically affected by environmental and load changes (). For this reason, a dc-dc converter is essential to provide a stable output voltage at the output stage of clean energy.
DC-DC converters (buck type, boost type, and buck/boost type) are widely utilized in industrial applications for power conversion, including uninterruptible electrical power, dc motor drives, power systems, hybrid electric cars, medical equipment, telecommunications, portable recharging devices etc. However, it is crucial to mention that the output voltage requirement varies with different application situations (; ; ). Some applications, for example, need the converter output to have a quick dynamic response and a low voltage ripple. Moreover, some application demands a constant output voltage while experiencing parameter uncertainties and load changes. Therefore, industries and academics have been focusing on developing the most effective and efficient control approach for eachapplication in recent decades (; ; ; ).
The DC microgrid incorporates a DC-DC boost converter to connect the DC bus and the DC energy source, such as photovoltaic array, fuel cell, and battery (). The DC-DC boost converter is usually employed to boost up the DC voltage to a higher level, i.e., the output voltage is greater than its input supply. Further, it is frequently used at the primary stage in a clean power system to generate more controlled DC voltage for later usage of the inverter. The mathematical model of a DC-DC boost converter is a nonlinear variable structured (), has a nonminimum phase (), and time-varying behavior (). The controller design for duty cycle generation of the converter is challenging because of disturbances caused by load changes, input voltage fluctuation, and electromagnetic interference rendered by the semiconductor switching operation (). Furthermore, adjusting the output voltage requires continuous inductor current information (). Consequently, an additional current sensor is needed (). Therefore, the boost converter requires a high-performing control system that can provide effective disturbance rejection, low steady-state error, minimal overshoot, rapid recovery time, and fast transient reaction time to achieve a good system response.
In the past, various control strategies have been developed for controlling the output voltage of boost converters, including proportional-integral (PI), proportional-integral-derivative (PID), neural network, fuzzy control, backstepping technique, sliding mode control (SMC), etc (; ; ; ). In general, PID-type or PI-type control systems are popular due to their simpler structure, ease of design, and low price. use a DSP-based controller to regulate the boost converter voltage with PI and PID designs. The findings show that PID and PI controllers are sluggish to acquire a transient response, have a significant overshoot at starting, are less stable, and are less resilient to operating point changes. The design principle of PID or PI is based on the linearization of controller system to construct its performance in the frequency domain (). These controllers are not suitable for large-signal disturbances, such as suddenly large load changes to the system based on this constraint (). Moreover, when there are system uncertainties, the stability of such control systems cannot be guaranteed, and control gains need to be repeatedly tuned to ensure favourable performance. The disturbance rejection problem for a non-minimum phase DC-DC boost converter is investigated in () using a robust PID controller. However, the drawback of PID controller is that it requires linearization to acquire a control parameter for a specific operating point. As a result, the boost converter cannot operate in all situations and operating ranges.
On the other hand, the sliding mode control (SMC) or variable structure control is a well known robust nonlinear control strategy. The SMC has a fast control action, better transient and steady state performances, and invariant against matched uncertainties and disturbances when the system dynamics are in sliding phase (; ; ; ). However, there are some problems associated with SMC, like chattering phenomena, inconstant frequency of switching, which can create excessive wear and tear on power switches, increased heat losses in the power circuit, and electromagnetic interference (EMI) problems (). The SMC has been researched to develop adjustment feedback structures for SMC based on hysteresis control in order to resolve chattering phenomena and switching frequency of higher order (). By properly regulating the hysteresis band and sliding factor, the SMC based on the hysteresis modulation scheme becomes stronger against changes in input voltage and output charge. However, the frequency of switching cannot be guaranteed to be constant (). In (), an equivalent control-based SMC is developed to try and resolve the chattering phenomena problem and the non-constant frequency of switching. Unfortunately, in these investigations, equivalent control signals are unable to address possible system uncertainties. reported an SMC-based scheme for a boost converter to deliver a constant power demand. However, the system response has a significant chattering effect due to on-off type control design. A terminal SMC strategy is proposed in (), however the finite-time convergence is not shown in the stability analysis. In (), a composite control using disturbance observer (DO) and nonsingular terminal SMC is proposed to achieve a better transient response with improved robustness. However, it employs a coordinate transformation and DO approach, making the controller design relatively complex. Then, in (), a fast terminal SMC is proposed for voltage tracking of DC-DC boost converter. Still there are few shortcomings in (). First, parameters are tuned based on trial and error basis. Then, the upper bound of disturbance is assumed to be known apriori, which is not case in practical scenario. Lastly, finite time convergence is not shown in theory. Recently, a second order-based SMC scheme is proposed in () for designing a robust decentralized control for voltage regulation in boost-based DC microgrids. The second-order SMC approach produces continuous control inputs as duty cycles for the power converters. However, higher-order SMC is mathematically intensive and requires high computational power. In addition, the reaching phase of SMC is susceptible to the effects of disturbances. That means disturbance still influences the system dynamics in the reaching stage. To solve this issue, integral SMC is developed () that guarantees the robustness from initial time t = 0.
In view of aforementioned literature, the proposed DC-DC boost converter control scheme employs an adaptive integral terminal SMC structure to achieve robustness against input and load variations. The combined integral and terminal sliding surface structure gives a better invariance throughout the operation. Also, it enables the finite-time convergence of the surface and the relative state. Further, the adaptive tuning of the controller gains helps in selecting control parameters, which further helps reduce chattering and control effort. The main contributions of this paper are as follows.
• The proposed regulation control provides the robustness against the input voltage fluctuation and load variations.
• Theoretically, it is proved that the developed scheme guarantees the finite-time convergence of output voltage to the reference value.
• Moreover, with the use of adaptive gains, the control design does not require apriori upper bound knowledge of uncertainties.
• The numerical simulations and hardware analyses validate the efficacy of the developed strategy.
2 Problem Formulation
The averaged mathematical dynamics of boost converter under continuous conduction mode with input and output uncertainties is expressed as .
where system state x1 is the average current flowing through inductor and state x2 denotes the average output voltage across the capacitor. The system parameters L, C, Rn, and Vin are the nominal inductor, capacitor, resistor, and input voltage, respectively. Further, ΔR and ΔVin denotes the variations in resistance and input voltage, respectively. The uncertainties in (1) can be clubbed together in a single term and can be rewritten as
where and .
Consider the course errors in the output voltage (ev) and inductor current (ei) as
where Vref and are the desired values of x2 and x1, respectively.
With simple mathematical operations on (3) using (2) gives the error dynamical system aswhere , , , , and .
2.1 Problem Statement
The aim of this brief is to develop a finite-time robust control scheme as duty cycle for the voltage regulation of boost converter under multiple uncertainties. In terms of mathematical expression, this can be described as:where tf represents the finite-time.
In this brief, the following assumption and lemmas are used.
Assumption 1The lumped uncertainty in the error dynamics (4) is considered to be bounded, but the knowledge of its upper bound is unknown, i.e., ‖Φ‖ ≤ ϖ, where ϖ > 0 is an unknown constant.
Lemma 1(). With γ ∈ (0, 2) and a vector , the following inequality holds:
Lemma 2(). Consider a Lyapunov function defined in an open neighborhood of the origin for a continuous function , with origin as equilibrium point. If the given inequality is satisfied for a real number a > 0 and γ ∈ (0, 1)then θ will converge to zero in finite-time with the settling time of .
3 Proposed Controller and Stability Analysis
The proposed sliding surface is defined aswhere α1 > 0 and α2 > 0 are the scalar constants, , and ρ ∈ (0, 1). The derivative of σ with respect to time is expressed asThe Proposed adaptive based terminal sliding mode control scheme is given aswhere B+ is the left pseudo-inverse of B, i.e., (), , are the estimation of controller gains k1 and k2, respectively with and . These controller gains are governed by the following adaptive lawswhere ϕ1 > 0 and ϕ2 > 0 decides the rate of adaptation and ϵ > 0 is significantly small design parameter.
3.1 Stability Analysis
Consider the error dynamics of boost converter (4) under Assumption 1. The proposed control algorithm (8) will guarantee the convergence of sliding manifold (6) and system error to zero within finite-time.
Proof. Closed-loop stability analysis of the above theorem is proved using the Lyapunov theory (; ). Therefore, considering a positive Lyapunov function V1 aswhere , , θ1 > 0, and θ2 > 0.
Using (4, 7) in the time derivative of V1 yieldsAfter substituting u from (8) and adaptive law from (9) in above equation results inAdding and subtracting k2α1‖σ‖ in (11) and since k1 > 0, so k1α1‖σ‖2 can be introduced in (11) asSince and , so and . Thus, absolute operators with negative sign has been incorporated in (12) (). Further simplification of Eq. 12 results inIncorporating the definition of 2-norm, i.e., ‖σ‖2 = σTσ, into (13) yieldsUsing Lemma 1, Eq. 14 can be rewritten aswhere Ψmin = min (Ψ0, Ψ1, Ψ2) > 0, , , , , and . Eq. 15 satisfies the inequality condition of Lemma 2. Therefore, the sliding manifold σ will converge to zero in finite-time. As σ = 0, the following equation can be written from (6) as
Now, to show the convergence of the error e, consider another Lyapunov function V2 = (1/2)eTe. Substituting (16) in the time derivative of V2 to obtainEq. 17 also agrees with Lemma 2. Therefore, the vector e will also converge to zero in finite-time. Hence, Theorem 1 is proved.
4 Results
This section presents multiple numerical and experimental results using the proposed adaptive ISMC for the uncertain dc-dc boost converter. For numerical simulation, the average mathematical model of dc-dc boost converter (1) is employed on Matlab/Simulink software. On the other hand, the hardware is realized on the setup given in Figure 1, where the control algorithm is implemented via a digital signal processor and controller (DSC) C2000 by Texas Instruments. The control input (duty) and corresponding PWM are evaluated in Simulink on the computer (laptop) and then fed to the DSC for physical signal generation. The generated electrical signal is used for switching MOSFET using the driver circuit. The DSC constructs the proposed controller output based on the feedback signals and creates the PWM signal directly. The DSC modifies the duty cycle of the PWM signal to regulate the boost converter’s output voltage. The analog feedback signals, i.e., output voltage and inductor current, are sensed using voltage and current sensors.
FIGURE 1
Besides, the nominal parameters of the boost converter is selected as follows: Vin = 15 V, Rn = 90 Ω, C = 900 μF, and L = 870 μH. The switching frequency of PWM is selected as 20 kHz and the sampling time is taken as 0.5 ms. Moreover, the gain parameters of the proposed controller are chosen as: ϕ1 = 2.6, ϕ2 = 0.2, α1 = 6.5, α2 = 1.8, ρ = 0.2, and ϵ = 0.01.
The performance of the proposed scheme is tested under three conditions as shown in Table 1. Accordingly, in condition 1, reference voltage (Vref) is varying while the other two variables, i.e., Vin and R is fixed. Whereas, load is varying in condition 2 and input voltage is changing in condition 3.
TABLE 1
| Condition | Vref | Variation of Input Voltage | Variation of Load Resistance |
|---|---|---|---|
| 1 | Vref: 55 V → 60 V | No variation; Vin = 15 V | No variation; R = 90 Ω |
| 2 | Vref = 55 V | No variation; Vin = 15 V | R: 90 Ω → 120 Ω |
| 3 | Vref = 55 V | Vin: 15 V → 20 V | No variation; R = 90 Ω |
Cases examined under the proposed scheme.
4.1 Simulation Analysis
The numerical simulation is performed on MATLAB/Simulink software to analyze the proposed control strategy under different conditions. Figure 2 shows the output voltage response, proposed control duty, and the time-varying adaptive gain response for all three cases.
FIGURE 2
It can be seen from Figure 2A that the references values are changing, i.e., till 1.1 s, Vref = 55 V, and after that Vref = 60 V. Accordingly, the proposed controller effectively regulates the output voltage to the two different desired values in the same simulation. Note that the output voltage response is fast and without any overshoot or oscillations in its transient response. Moreover, the control effort in terms of duty value is generated according to the proposed law (8), whose response is shown in Figure 2B. The value of u automatically changes at time t = 1.1s when the reference value increases to 60 V. The auto-increment in u is possible due to the self-tuning of controller gains. These gains are tuned using the proposed adaptation laws, and their responses are illustrated in Figure 2C. It is evident from Figure 2C that the adaptive gains are self-adapting to a new value after 1.1 s when Vref command changes to 60 V.
The system response under resistive load variation is demonstrated in Figures 2D–F. In this condition, loading (at 0.8 s) and unloading (at 1.1 s) of load resistance occur. However, the output voltage response has no significant changes due to this load variation, as shown in Figure 2D. Thus, the proposed controller effectively tackles the load uncertainty while maintaining the desired output voltage level. The control duty and the adaptive gain responses are shown in Figures 2E,F, respectively. The slight fluctuation in the control response and small increment in the adaptive gains is due to the sudden change in load resistance value.
Similarly, the third condition of input voltage variation is tested and illustrated in Figures 2G–I. Similarly, the third condition of input voltage variation is tested and illustrated in Figure 2G. Here, the input voltage is varied from 15 to 20 V (at 0.8 s) and 20–15 V (at 1.1 s) to check the robustness performance of the proposed controller under system parameter variation. The sudden rise and the fall in the input voltage have a small effect on the output voltage response for a short duration, as shown in Figure 2G. However, once the adaptive gains tune to an appropriate value after 0.2 s from the time of variation, the output response again converges to the desired value. Figure 2H shows the change in the control duty cycle value when there are changes in the input parameter. The adaptive gain parameter is also changing at the time of input variation (see Figure 2I) to provide a suitable switching gain for nullifying the effect of input fluctuations. In summary, the closed-loop response rapidly recovers to its desired value thanks to the self-tuning property of the proposed robust control law.
It is important to note that the chattering effect in all control responses is considerably relieved due to employing the boundary layer technique in simulation (; ).
4.2 Experimental Results
The hardware performances of the proposed scheme are also illustrated here to validate its effectiveness. Figure 3 presents four sets of actual snapshots of the output voltage and inductor current waveforms on the experimental setup using DSO. Figures 3A,B illustrate the results of the boost converter under condition 1. In Figure 3A, the output voltage initially starts from the biasing voltage. But, once the processor C2000 implements the proposed control algorithm to the hardware, the output voltage effectively reaches the reference value (i.e., 55 V). Likewise, the given controller satisfactorily forces the output voltage to a new desired value of 60 V in Figure 3B.
FIGURE 3
The effect of load variation on the system performance is shown in Figure 3C, which indicates that the proposed controller perfectly tackles the change in resistive load without any significant change in the output voltage. Furthermore, Figure 3D gives a snapshot of output voltage response under the influence of input voltage fluctuation. Here, there is a small dip in the output voltage at the instant of voltage variation, but the controller effectively regulates the output to the desired voltage value quickly.
5 Conclusion
This paper presents a robust regulation control of boost converter to regulate its output voltage under various uncertainties. The proposed control scheme is designed by integrating the terminal sliding manifold with adaptive controller gains. The terminal SMC guarantees the finite-time convergence of error between the output and reference voltage. The adaptive laws enable the controller design to dynamically tune its gains without knowing the upper bound values of uncertainties. A detailed theoretical stability analysis of the closed-loop system is also proved by the Lyapunov theory. The proposed control strategy is validated through numerical and hardware analyses under various uncertainty conditions. The closed-loop system performance is found to be fast, efficient, and robust against different uncertainties. The possible extension of this research could be along the lines of implementing the proposed algorithm at a higher power level with inductance and capacitance uncertainties.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
SA contributed to conception, methodology, and design of the study. SA and SW carried out the formal analysis. SA and JA organized the database and software coding. SA performed the mathematical analysis and theoretical investigation. SA and JA realized the hardware results. SA wrote the first draft of the manuscript. SA, AS, JA, SW, and ASS wrote sections of the manuscript. MN and AS supervised this work. MN and AS provide the laboratory resources. ASS arranged the funding acquisition. All authors contributed to manuscript revision, read, and approved the submitted version.
Funding
The Deanship of Scientific Research at King Khalid University funded the APC of this work through Research Groups Program under grant number (RGP.2/81/43).
Acknowledgments
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Groups Program under grant number (RGP.2/81/43).
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AmrrS. M.AlamM. S.AsgharM. S. J.AhmadF. (2018). Low Cost Residential Microgrid System Based Home to Grid (H2G) Back up Power Management. Sustain. Cities Soc.36, 204–214. 10.1016/j.scs.2017.10.016
2
AmrrS. M.AlturkiA. (2021). Robust Control Design for an Active Magnetic Bearing System Using Advanced Adaptive SMC Technique. IEEE Access9, 155662–155672. 10.1109/access.2021.3129140
3
AmrrS. M.ShemamiM. S.IrfanH. K. M.AsgharM. S. J. (2021). “Design and Operation of a Low-Cost Microgrid-Integrated EV for Developing Countries,” in Electric Vehicle Integration in a Smart Microgrid Environment (CRC Press), 335–357. 10.1201/9780367423926-15
4
BalogR. S.WeaverW. W.KreinP. T. (2012). The Load as an Energy Asset in a Distributed Dc Smartgrid Architecture. IEEE Trans. Smart Grid3, 253–260. 10.1109/tsg.2011.2167722
5
BhatS. P.BernsteinD. S. (2000). Finite-time Stability of Continuous Autonomous Systems. SIAM J. Control Optim.38, 751–766. 10.1137/s0363012997321358
6
BoikoI. M. (2013). Chattering in Sliding Mode Control Systems with Boundary Layer Approximation of Discontinuous Control. Int. J. Syst. Sci.44, 1126–1133. 10.1080/00207721.2011.652233
7
BouchekaraH. R. E.-H.JavaidM. S.ShaabanY. A.ShahriarM. S.RamliM. A. M.LatrecheY. (2021). Decomposition Based Multiobjective Evolutionary Algorithm for Pv/wind/diesel Hybrid Microgrid System Design Considering Load Uncertainty. Energy Rep.7, 52–69. 10.1016/j.egyr.2020.11.102
8
CucuzzellaM.LazzariR.TripS.RostiS.SandroniC.FerraraA. (2018). Sliding Mode Voltage Control of Boost Converters in Dc Microgrids. Control Eng. Pract.73, 161–170. 10.1016/j.conengprac.2018.01.009
9
FarrellJ. A.PolycarpouM. M. (2006). Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches, 48. New Jersey): John Wiley & Sons.
10
GuoL.AbdulN. M. (2021). Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System under Partial Shading Conditions. Front. Energy Res.9. 10.3389/fenrg.2021.712175
11
GuoL.HungJ. Y.NelmsR. M. (2011). Comparative Evaluation of Sliding Mode Fuzzy Controller and Pid Controller for a Boost Converter. Electr. Power Syst. Res.81, 99–106. 10.1016/j.epsr.2010.07.018
12
Hernández-MárquezE.Avila-ReaC. A.García-SánchezJ. R.Silva-OrtigozaR.Marciano-MelchorM.Marcelino-ArandaM. (2019). New “Full-bridge Buck Inverter–Dc Motor” System: Steady-State and Dynamic Analysis and Experimental Validation. Electronics8, 1216. 10.3390/electronics8111216
13
Hernández-MárquezE.Avila-ReaC. A.García-SánchezJ. R.Silva-OrtigozaR.Silva-OrtigozaG.TaudH.et al (2018). Robust Tracking Controller for a Dc/dc Buck-Boost Converter–Inverter–Dc Motor System. Energies11, 2500. 10.3390/en11102500
14
ImdadullahAmrrS. M.IqbalA.AsgharM. J. (2021). Comprehensive Performance Analysis of Flexible Asynchronous Ac Link under Various Unbalanced Grid Voltage Conditions. Energy Rep.7, 750–761. 10.1016/j.egyr.2021.01.028
15
KhanT.SundareswaranK. (2014). “Voltage Regulation Enhancement in a Buck Type Dc-Dc Converter Using Queen Bee Evolution Based Genetic Algorithm,” in 2014 IEEE 6th India International Conference on Power Electronics (IICPE) (IEEE), 1–6. 10.1109/iicpe.2014.7115836
16
KobakuT.JeyasenthilR.SahooS.DragicevicT. (2021). Experimental Verification of Robust Pid Controller under Feedforward Framework for a Nonminimum Phase Dc–Dc Boost Converter. IEEE J. Emerg. Sel. Top. Power Electron.9, 3373–3383. 10.1109/jestpe.2020.2999649
17
LeonJ. I.VazquezS.FranqueloL. G. (2017). Multilevel Converters: Control and Modulation Techniques for Their Operation and Industrial Applications. Proc. IEEE105, 2066–2081. 10.1109/jproc.2017.2726583
18
LiuL.ZhaoY.ChangD.XieJ.MaZ.SunQ.et al (2018). Prediction of Short-Term Pv Power Output and Uncertainty Analysis. Appl. energy228, 700–711. 10.1016/j.apenergy.2018.06.112
19
Martínez-TreviñoB. A.JammesR.El AroudiA.Martínez-SalameroL. (2017). Sliding-mode Control of a Boost Converter Supplying a Constant Power Load. IFAC-PapersOnLine50, 7807–7812. 10.1016/j.ifacol.2017.08.1055
20
MuktiadjiR. F.RamliM. A.BouchekaraH. R.SeedahmedM.BudimanF. N. (2022). Control of Boost Converter Using Observer-Based Backstepping Sliding Mode Control for Dc Microgrid. Front. Energy Res.10, 152. 10.3389/fenrg.2022.828978
21
MummadiV. (2011). Design of Robust Digital Pid Controller for H-Bridge Soft-Switching Boost Converter. IEEE Trans. Industrial Electron.58, 2883–2897. 10.1109/tie.2010.2077615
22
NizamiT. K.ChakravartyA. (2020). Neural Network Integrated Adaptive Backstepping Control of Dc-Dc Boost Converter. IFAC-PapersOnLine53, 549–554. 10.1016/j.ifacol.2020.06.092
23
ParkH.-H.ChoG.-H. (2014). A Dc–Dc Converter for a Fully Integrated Pid Compensator with a Single Capacitor. IEEE Trans. Circuits Syst. II Express Briefs61, 629–633. 10.1109/tcsii.2014.2327351
24
SahaS.AmrrS. M.NabiM. U.IqbalA. (2019). Reduced Order Modeling and Sliding Mode Control of Active Magnetic Bearing. IEEE Access7, 113324–113334. 10.1109/access.2019.2935541
25
SahaS.AmrrS. M.SaidiA. S.BanerjeeA.NabiM. (2021). Finite-time Adaptive Higher-Order Smc for the Nonlinear Five Dof Active Magnetic Bearing System. Electronics10, 1333. 10.3390/electronics10111333
26
SaidiA. S. (2022). Impact of Grid-Tied Photovoltaic Systems on Voltage Stability of Tunisian Distribution Networks Using Dynamic Reactive Power Control. Ain Shams Eng. J.13, 101537. 10.1016/j.asej.2021.06.023
27
ShenH.TaoP.LyuR.RenP.GeX.WangF. (2021). Risk-constrained Optimal Bidding and Scheduling for Load Aggregators Jointly Considering Customer Responsiveness and Pv Output Uncertainty. Energy Rep.7, 4722–4732. 10.1016/j.egyr.2021.07.021
28
SinghR.AmrrS. M.AsgharM. J. (2021). Supervisory Control Strategy for the Effective Solar Energy Utilization in a Residential Microgrid System Using a Cost-Effective Controller. Int. J. Electr. Power & Energy Syst.132, 107170. 10.1016/j.ijepes.2021.107170
29
SlotineJ.-J. E.LiW. (1991). Applied Nonlinear Control, 199. Englewood Cliffs, NJ): Prentice-Hall.
30
TahriA.El FadilH.RachidA.EricM.GiriF. (2019). A Nonlinear Controller Based on a High Gain Observer for a Cascade Boost Converter in a Fuel Cell Distributed Power Supply System. IFAC-PapersOnLine52, 91–96. 10.1016/j.ifacol.2019.12.627
31
TalebM.PlestanF.BououlidB. (2015). An Adaptive Solution for Robust Control Based on Integral High-Order Sliding Mode Concept. Int. J. Robust Nonlinear Control25, 1201–1213. 10.1002/rnc.3135
32
TanS.-C.LaiY.-M.CheungM. K.TseC. K. (2005). On the Practical Design of a Sliding Mode Voltage Controlled Buck Converter. IEEE Trans. power Electron.20, 425–437. 10.1109/tpel.2004.842977
33
TanS.-C.LaiY.-M.ChiK. T.Martinez-SalameroL.WuC.-K. (2007). A Fast-Response Sliding-Mode Controller for Boost-type Converters with a Wide Range of Operating Conditions. IEEE Tr Ind Ele54, 3276–3286. 10.1109/tie.2007.905969
34
TanS.-C.LaiY.TseC. K.CheungM. K. (2006). Adaptive Feedforward and Feedback Control Schemes for Sliding Mode Controlled Power Converters. IEEE Tr Power Ele21, 182–192. 10.1109/tpel.2005.861191
35
UtkinV.PoznyakA.OrlovY.PolyakovA. (2020). Conventional and High Order Sliding Mode Control. J. Frankl. Inst.357, 10244–10261. 10.1016/j.jfranklin.2020.06.018
36
UtkinV.ShiJ. (1996). “Integral Sliding Mode in Systems Operating under Uncertainty Conditions,” in Proceedings of 35th IEEE conference on decision and control (IEEE)4, 4591–4596.
37
UtkinV. (1977). Variable Structure Systems with Sliding Modes. IEEE Trans. Automatic Control22, 212–222. 10.1109/tac.1977.1101446
38
WaiR.-J.ShihL.-C. (2011). Design of Voltage Tracking Control for Dc–Dc Boost Converter via Total Sliding-Mode Technique. IEEE Tr Indust Elect58, 2502–2511. 10.1109/tie.2010.2066539
39
WangH.ManZ.KongH.ZhaoY.YuM.CaoZ.et al (2016). Design and Implementation of Adaptive Terminal Sliding-Mode Control on a Steer-By-Wire Equipped Road Vehicle. IEEE Trans. Industrial Electron.63, 5774–5785. 10.1109/tie.2016.2573239
40
WangZ.LiS.LiQ. (2020). Continuous Nonsingular Terminal Sliding Mode Control of Dc-Dc Boost Converters Subject to Time-Varying Disturbances. IEEE Trans. Circuits Syst. II Express Briefs67, 2552–2556. 10.1109/tcsii.2019.2955711
41
WeiM.LinS.ZhaoY.WangH.LiuQ. (2021). An Adaptive Sliding Mode Control Based on Disturbance Observer for Lfc. Front. Energy Res.555. 10.3389/fenrg.2021.733910
42
YaziciI.YaylaciE. K. (2016). Fast and Robust Voltage Control of Dc–Dc Boost Converter by Using Fast Terminal Sliding Mode Controller. IET Power Electron.9, 120–125. 10.1049/iet-pel.2015.0008
43
YinY.LiuJ.MarquezA.LinX.LeonJ. I.VazquezS.et al (2020). Advanced Control Strategies for Dc–Dc Buck Converters with Parametric Uncertainties via Experimental Evaluation. IEEE Trans. Circuits Syst. I Regul. Pap.67, 5257–5267. 10.1109/tcsi.2020.3009168
44
YuS.YuX.ShirinzadehB.ManZ. (2005). Continuous Finite-Time Control for Robotic Manipulators with Terminal Sliding Mode. Automatica41, 1957–1964. 10.1016/j.automatica.2005.07.001
45
ZhangM.LiX.LiuJ.SuH. (2017). Dual-mode Lqr-Feedforward Optimal Control for Non-minimum Phase Boost Converter. IET Power Electron.10, 92–102. 10.1049/iet-pel.2016.0234
Summary
Keywords
boost converter, uncertain system, finite-time theory, Lyapunov stability analysis, adaptive control, sliding mode control
Citation
Amrr SM, Ahmad J, Waheed SA, Sarwar A, Saidi AS and Nabi M (2022) Finite-Time Adaptive Sliding Mode Control of a Power Converter Under Multiple Uncertainties. Front. Energy Res. 10:901606. doi: 10.3389/fenrg.2022.901606
Received
22 March 2022
Accepted
13 April 2022
Published
03 May 2022
Volume
10 - 2022
Edited by
Marif Daula Siddique, Virginia Tech, United States
Reviewed by
Asaad Mohammad, Auckland University of Technology, New Zealand
Zeeshan Ahmad Khan, Volkswagen Group, Germany
Mahetab Alam, IIT Ropar, India
Updates
Copyright
© 2022 Amrr, Ahmad, Waheed, Sarwar, Saidi and Nabi.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Syed Muhammad Amrr, syedamrr@gmail.com
This article was submitted to Smart Grids, a section of the journal Frontiers in Energy Research
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.