Quadcopter Attitude Control Optimization and Multi-Agent Coordination

This thesis presents a method of automated control gain tuning for a Quadcopter Unmanned Aerial Vehicle and proposes a method of coordination multiple autonomous robotic agents capable for formation aggregation. Sliding Mode Control for Quadcopter altitude and attitude stabilization is presented and tuned using Particle Swarm Optimization. Different configurations for the optimization process are compared to determine an effective and time-efficient setup to complete the control gain tuning. The multi-agent coordination scheme expands upon an existing adjustable swarm framework based on an Artificial Potential Field Sliding Mode Controller. The original leader-follower scheme is modified with the goal of producing a leaderless swarm where agents move towards specific locations to aggregate a desired formation. Analysis of the swarm control scheme pays particular attention to maintaining proper distance between agents. Using Lyapunov methods following that of the original controller analysis, stability under first order and general higher order dynamics is analyzed. Numerical simulations of the swarm controller using agents with nonlinear Quadcopter or second order point mass dynamics are presented to illustrate the capabilities of this algorithm. The automatically tuned Quadcopter controller is used in simulations when applicable. The development of an experimental test platform is discussed with the intention of validating the simulation results on physical Quadcopters

Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids

[EN] Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems' view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.Gonzales-Zurita, Ó.; Clairand, J.; Peñalvo-López, E.; Escrivá-Escrivá, G. (2020). Review on Multi-Objective Control Strategies for Distributed Generation on Inverter-Based Microgrids. Energies. 13(13):1-29. https://doi.org/10.3390/en13133483S1291313Ross, M., Abbey, C., Bouffard, F., & Joos, G. (2015). Multiobjective Optimization Dispatch for Microgrids With a High Penetration of Renewable Generation. IEEE Transactions on Sustainable Energy, 6(4), 1306-1314. doi:10.1109/tste.2015.2428676Murty, V. V. S. N., & Kumar, A. (2020). Multi-objective energy management in microgrids with hybrid energy sources and battery energy storage systems. Protection and Control of Modern Power Systems, 5(1). doi:10.1186/s41601-019-0147-zKatircioğlu, S., Abasiz, T., Sezer, S., & Katırcıoglu, S. (2019). Volatility of the alternative energy input prices and spillover effects: a VAR [MA]-MGARCH in BEKK approach for the Turkish economy. Environmental Science and Pollution Research, 26(11), 10738-10745. doi:10.1007/s11356-019-04531-5Olivares, D. E., Mehrizi-Sani, A., Etemadi, A. H., Canizares, C. A., Iravani, R., Kazerani, M., … Hatziargyriou, N. D. (2014). Trends in Microgrid Control. IEEE Transactions on Smart Grid, 5(4), 1905-1919. doi:10.1109/tsg.2013.2295514Akinyele, D., Belikov, J., & Levron, Y. (2018). Challenges of Microgrids in Remote Communities: A STEEP Model Application. Energies, 11(2), 432. doi:10.3390/en11020432Benamar, A., Travaillé, P., Clairand, J.-M., & Escrivá-Escrivá, G. (2020). Non-Linear Control of a DC Microgrid for Electric Vehicle Charging Stations. International Journal on Advanced Science, Engineering and Information Technology, 10(2), 593. doi:10.18517/ijaseit.10.2.10815Lakshmi, M., & Hemamalini, S. (2018). Nonisolated High Gain DC–DC Converter for DC Microgrids. IEEE Transactions on Industrial Electronics, 65(2), 1205-1212. doi:10.1109/tie.2017.2733463Yin, C., Wu, H., Locment, F., & Sechilariu, M. (2017). Energy management of DC microgrid based on photovoltaic combined with diesel generator and supercapacitor. Energy Conversion and Management, 132, 14-27. doi:10.1016/j.enconman.2016.11.018Chen, D., Xu, Y., & Huang, A. Q. (2017). Integration of DC Microgrids as Virtual Synchronous Machines Into the AC Grid. IEEE Transactions on Industrial Electronics, 64(9), 7455-7466. doi:10.1109/tie.2017.2674621Abhinav, S., Schizas, I. D., Ferrese, F., & Davoudi, A. (2017). Optimization-Based AC Microgrid Synchronization. IEEE Transactions on Industrial Informatics, 13(5), 2339-2349. doi:10.1109/tii.2017.2702623Liu, Z., Su, M., Sun, Y., Li, L., Han, H., Zhang, X., & Zheng, M. (2019). Optimal criterion and global/sub-optimal control schemes of decentralized economical dispatch for AC microgrid. International Journal of Electrical Power & Energy Systems, 104, 38-42. doi:10.1016/j.ijepes.2018.06.045Khatibzadeh, A., Besmi, M., Mahabadi, A., & Reza Haghifam, M. (2017). Multi-Agent-Based Controller for Voltage Enhancement in AC/DC Hybrid Microgrid Using Energy Storages. Energies, 10(2), 169. doi:10.3390/en10020169Asghar, F., Talha, M., & Kim, S. (2017). Robust Frequency and Voltage Stability Control Strategy for Standalone AC/DC Hybrid Microgrid. Energies, 10(6), 760. doi:10.3390/en10060760Lotfi, H., & Khodaei, A. (2017). Hybrid AC/DC microgrid planning. Energy, 118, 37-46. doi:10.1016/j.energy.2016.12.015Kerdphol, T., Rahman, F., & Mitani, Y. (2018). Virtual Inertia Control Application to Enhance Frequency Stability of Interconnected Power Systems with High Renewable Energy Penetration. Energies, 11(4), 981. doi:10.3390/en11040981Rodrigues, Y. R., Zambroni de Souza, A. C., & Ribeiro, P. F. (2018). An inclusive methodology for Plug-in electrical vehicle operation with G2V and V2G in smart microgrid environments. International Journal of Electrical Power & Energy Systems, 102, 312-323. doi:10.1016/j.ijepes.2018.04.037Ghosh, S., & Chattopadhyay, S. (2020). Three-Loop-Based Universal Control Architecture for Decentralized Operation of Multiple Inverters in an Autonomous Grid-Interactive Microgrid. IEEE Transactions on Industry Applications, 56(2), 1966-1979. doi:10.1109/tia.2020.2964746Mohapatra, S. R., & Agarwal, V. (2020). Model Predictive Control for Flexible Reduction of Active Power Oscillation in Grid-Tied Multilevel Inverters Under Unbalanced and Distorted Microgrid Conditions. IEEE Transactions on Industry Applications, 56(2), 1107-1115. doi:10.1109/tia.2019.2957480Ziouani, I., Boukhetala, D., Darcherif, A.-M., Amghar, B., & El Abbassi, I. (2018). Hierarchical control for flexible microgrid based on three-phase voltage source inverters operated in parallel. International Journal of Electrical Power & Energy Systems, 95, 188-201. doi:10.1016/j.ijepes.2017.08.027Golshannavaz, S., & Mortezapour, V. (2018). A generalized droop control approach for islanded DC microgrids hosting parallel-connected DERs. Sustainable Cities and Society, 36, 237-245. doi:10.1016/j.scs.2017.09.038Safa, A., Madjid Berkouk, E. L., Messlem, Y., & Gouichiche, A. (2018). A robust control algorithm for a multifunctional grid tied inverter to enhance the power quality of a microgrid under unbalanced conditions. International Journal of Electrical Power & Energy Systems, 100, 253-264. doi:10.1016/j.ijepes.2018.02.042Andishgar, M. H., Gholipour, E., & Hooshmand, R. (2017). An overview of control approaches of inverter-based microgrids in islanding mode of operation. Renewable and Sustainable Energy Reviews, 80, 1043-1060. doi:10.1016/j.rser.2017.05.267Li, Z., Zang, C., Zeng, P., Yu, H., Li, S., & Bian, J. (2017). Control of a Grid-Forming Inverter Based on Sliding-Mode and Mixed ${H_2}/{H_\infty }$ Control. IEEE Transactions on Industrial Electronics, 64(5), 3862-3872. doi:10.1109/tie.2016.2636798Hossain, M. A., Pota, H. R., Squartini, S., & Abdou, A. F. (2019). Modified PSO algorithm for real-time energy management in grid-connected microgrids. Renewable Energy, 136, 746-757. doi:10.1016/j.renene.2019.01.005Shokoohi, S., Golshannavaz, S., Khezri, R., & Bevrani, H. (2018). Intelligent secondary control in smart microgrids: an on-line approach for islanded operations. Optimization and Engineering, 19(4), 917-936. doi:10.1007/s11081-018-9382-9Safari, A., Babaei, F., & Farrokhifar, M. (2019). A load frequency control using a PSO-based ANN for micro-grids in the presence of electric vehicles. International Journal of Ambient Energy, 42(6), 688-700. doi:10.1080/01430750.2018.1563811Miveh, M. R., Rahmat, M. F., Ghadimi, A. A., & Mustafa, M. W. (2016). Control techniques for three-phase four-leg voltage source inverters in autonomous microgrids: A review. Renewable and Sustainable Energy Reviews, 54, 1592-1610. doi:10.1016/j.rser.2015.10.079Rokrok, E., Shafie-khah, M., & Catalão, J. P. S. (2018). Review of primary voltage and frequency control methods for inverter-based islanded microgrids with distributed generation. Renewable and Sustainable Energy Reviews, 82, 3225-3235. doi:10.1016/j.rser.2017.10.022Bouzid, A. M., Guerrero, J. M., Cheriti, A., Bouhamida, M., Sicard, P., & Benghanem, M. (2015). A survey on control of electric power distributed generation systems for microgrid applications. Renewable and Sustainable Energy Reviews, 44, 751-766. doi:10.1016/j.rser.2015.01.016Vásquez, V., Ortega, L. M., Romero, D., Ortega, R., Carranza, O., & Rodríguez, J. J. (2017). Comparison of methods for controllers design of single phase inverter operating in island mode in a microgrid: Review. Renewable and Sustainable Energy Reviews, 76, 256-267. doi:10.1016/j.rser.2017.03.060Shen, X., Wang, H., Li, J., Su, Q., & Gao, L. (2019). Distributed Secondary Voltage Control of Islanded Microgrids Based on RBF-Neural-Network Sliding-Mode Technique. IEEE Access, 7, 65616-65623. doi:10.1109/access.2019.2915509Arbab-Zavar, B., Palacios-Garcia, E., Vasquez, J., & Guerrero, J. (2019). Smart Inverters for Microgrid Applications: A Review. Energies, 12(5), 840. doi:10.3390/en12050840Bullich-Massagué, E., Díaz-González, F., Aragüés-Peñalba, M., Girbau-Llistuella, F., Olivella-Rosell, P., & Sumper, A. (2018). Microgrid clustering architectures. Applied Energy, 212, 340-361. doi:10.1016/j.apenergy.2017.12.048Kerdphol, T., Rahman, F., Mitani, Y., Hongesombut, K., & Küfeoğlu, S. (2017). Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration. Sustainability, 9(5), 773. doi:10.3390/su9050773Hajiakbari Fini, M., & Hamedani Golshan, M. E. (2018). Determining optimal virtual inertia and frequency control parameters to preserve the frequency stability in islanded microgrids with high penetration of renewables. Electric Power Systems Research, 154, 13-22. doi:10.1016/j.epsr.2017.08.007Jung, J., & Villaran, M. (2017). Optimal planning and design of hybrid renewable energy systems for microgrids. Renewable and Sustainable Energy Reviews, 75, 180-191. doi:10.1016/j.rser.2016.10.061Baharizadeh, M., Karshenas, H. R., & Guerrero, J. M. (2018). An improved power control strategy for hybrid AC-DC microgrids. International Journal of Electrical Power & Energy Systems, 95, 364-373. doi:10.1016/j.ijepes.2017.08.036Serban, I., & Ion, C. P. (2017). Microgrid control based on a grid-forming inverter operating as virtual synchronous generator with enhanced dynamic response capability. International Journal of Electrical Power & Energy Systems, 89, 94-105. doi:10.1016/j.ijepes.2017.01.009Tavakoli, M., Shokridehaki, F., Marzband, M., Godina, R., & Pouresmaeil, E. (2018). A two stage hierarchical control approach for the optimal energy management in commercial building microgrids based on local wind power and PEVs. Sustainable Cities and Society, 41, 332-340. doi:10.1016/j.scs.2018.05.035Cagnano, A., De Tuglie, E., & Cicognani, L. (2017). Prince — Electrical Energy Systems Lab. Electric Power Systems Research, 148, 10-17. doi:10.1016/j.epsr.2017.03.011Zhang, H., Meng, W., Qi, J., Wang, X., & Zheng, W. X. (2019). Distributed Load Sharing Under False Data Injection Attack in an Inverter-Based Microgrid. IEEE Transactions on Industrial Electronics, 66(2), 1543-1551. doi:10.1109/tie.2018.2793241Yang, L., Hu, Z., Xie, S., Kong, S., & Lin, W. (2019). Adjustable virtual inertia control of supercapacitors in PV-based AC microgrid cluster. Electric Power Systems Research, 173, 71-85. doi:10.1016/j.epsr.2019.04.011Rahman, F. S., Kerdphol, T., Watanabe, M., & Mitani, Y. (2019). Optimization of virtual inertia considering system frequency protection scheme. Electric Power Systems Research, 170, 294-302. doi:10.1016/j.epsr.2019.01.025Farrokhabadi, M., Canizares, C. A., Simpson-Porco, J. W., Nasr, E., Fan, L., Mendoza-Araya, P. A., … Reilly, J. (2020). Microgrid Stability Definitions, Analysis, and Examples. IEEE Transactions on Power Systems, 35(1), 13-29. doi:10.1109/tpwrs.2019.2925703Yoldaş, Y., Önen, A., Muyeen, S. M., Vasilakos, A. V., & Alan, İ. (2017). Enhancing smart grid with microgrids: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 72, 205-214. doi:10.1016/j.rser.2017.01.064Rajesh, K. S., Dash, S. S., Rajagopal, R., & Sridhar, R. (2017). A review on control of ac microgrid. Renewable and Sustainable Energy Reviews, 71, 814-819. doi:10.1016/j.rser.2016.12.106Marzal, S., Salas, R., González-Medina, R., Garcerá, G., & Figueres, E. (2018). Current challenges and future trends in the field of communication architectures for microgrids. Renewable and Sustainable Energy Reviews, 82, 3610-3622. doi:10.1016/j.rser.2017.10.101Singh, A., & Suhag, S. (2018). Trends in Islanded Microgrid Frequency Regulation – A Review. Smart Science, 7(2), 91-115. doi:10.1080/23080477.2018.1540380Hou, X., Sun, Y., Lu, J., Zhang, X., Koh, L. H., Su, M., & Guerrero, J. M. (2018). Distributed Hierarchical Control of AC Microgrid Operating in Grid-Connected, Islanded and Their Transition Modes. IEEE Access, 6, 77388-77401. doi:10.1109/access.2018.2882678SHI, R., ZHANG, X., HU, C., XU, H., GU, J., & CAO, W. (2017). Self-tuning virtual synchronous generator control for improving frequency stability in autonomous photovoltaic-diesel microgrids. Journal of Modern Power Systems and Clean Energy, 6(3), 482-494. doi:10.1007/s40565-017-0347-3Toub, M., Bijaieh, M. M., Weaver, W. W., III, R. D. R., Maaroufi, M., & Aniba, G. (2019). Droop Control in DQ Coordinates for Fixed Frequency Inverter-Based AC Microgrids. Electronics, 8(10), 1168. doi:10.3390/electronics8101168Shuai, Z., Fang, J., Ning, F., & Shen, Z. J. (2018). Hierarchical structure and bus voltage control of DC microgrid. Renewable and Sustainable Energy Reviews, 82, 3670-3682. doi:10.1016/j.rser.2017.10.096Agundis-Tinajero, G., Segundo-Ramírez, J., Visairo-Cruz, N., Savaghebi, M., Guerrero, J. M., & Barocio, E. (2019). Power flow modeling of islanded AC microgrids with hierarchical control. International Journal of Electrical Power & Energy Systems, 105, 28-36. doi:10.1016/j.ijepes.2018.08.002Ali, A., Li, W., Hussain, R., He, X., Williams, B., & Memon, A. (2017). Overview of Current Microgrid Policies, Incentives and Barriers in the European Union, United States and China. Sustainability, 9(7), 1146. doi:10.3390/su9071146Cui, Y., Geng, Z., Zhu, Q., & Han, Y. (2017). Review: Multi-objective optimization methods and application in energy saving. Energy, 125, 681-704. doi:10.1016/j.energy.2017.02.174Yazdi, F., & Hosseinian, S. H. (2019). A novel «Smart Branch» for power quality improvement in microgrids. International Journal of Electrical Power & Energy Systems, 110, 161-170. doi:10.1016/j.ijepes.2019.02.026Bassey, O., Butler-Purry, K. L., & Chen, B. (2020). Dynamic Modeling of Sequential Service Restoration in Islanded Single Master Microgrids. IEEE Transactions on Power Systems, 35(1), 202-214. doi:10.1109/tpwrs.2019.2929268Chang, E.-C. (2018). Study and Application of Intelligent Sliding Mode Control for Voltage Source Inverters. Energies, 11(10), 2544. doi:10.3390/en11102544Das, D., Gurrala, G., & Shenoy, U. J. (2018). Linear Quadratic Regulator-Based Bumpless Transfer in Microgrids. IEEE Transactions on Smart Grid, 9(1), 416-425. doi:10.1109/tsg.2016.2580159Nguyen, H. K., Khodaei, A., & Han, Z. (2018). Incentive Mechanism Design for Integrated Microgrids in Peak Ramp Minimization Problem. IEEE Transactions on Smart Grid, 9(6), 5774-5785. doi:10.1109/tsg.2017.2696903Xiao, Z., Guerrero, J. M., Shuang, J., Sera, D., Schaltz, E., & Vásquez, J. C. (2018). Flat tie-line power scheduling control of grid-connected hybrid microgrids. Applied Energy, 210, 786-799. doi:10.1016/j.apenergy.2017.07.066Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2018). A Decentralized Robust Mixed $H_{{2}}/ H_{{{\infty }}}$ Voltage Control Scheme to Improve Small/Large-Signal Stability and FRT Capability of Islanded Multi-DER Microgrid Considering Load Disturbances. IEEE Systems Journal, 12(3), 2610-2621. doi:10.1109/jsyst.2017.2716351Panda, S. K., & Ghosh, A. (2020). A Computational Analysis of Interfacing Converters with Advanced Control Methodologies for Microgrid Application. Technology and Economics of Smart Grids and Sustainable Energy, 5(1). doi:10.1007/s40866-020-0077-xZhang, L., Chen, K., Lyu, L., & Cai, G. (2019). Research on the Operation Control Strategy of a Low-Voltage Direct Current Microgrid Based on a Disturbance Observer and Neural Network Adaptive Control Algorithm. Energies, 12(6), 1162. doi:10.3390/en12061162Zhu, K., Sun, P., Zhou, L., Du, X., & Luo, Q. (2020). Frequency-Division Virtual Impedance Shaping Control Method for Grid-Connected Inverters in a Weak and Distorted Grid. IEEE Transactions on Power Electronics, 35(8), 8116-8129. doi:10.1109/tpel.2019.2963345Samavati, E., & Mohammadi, H. R. (2019). Simultaneous voltage and current harmonics compensation in islanded/grid-connected microgrids using virtual impedance concept. Sustainable Energy, Grids and Networks, 20, 100258. doi:10.1016/j.segan.2019.100258Shi, K., Ye, H., Song, W., & Zhou, G. (2018). Virtual Inertia Control Strategy in Microgrid Based on Virtual Synchronous Generator Technology. IEEE Access, 6, 27949-27957. doi:10.1109/access.2018.2839737Fathi, A., Shafiee, Q., & Bevrani, H. (2018). Robust Frequency Control of Microgrids Using an Extended Virtual Synchronous Generator. IEEE Transactions on Power Systems, 33(6), 6289-6297. doi:10.1109/tpwrs.2018.2850880Amoateng, D. O., Al Hosani, M., Elmoursi, M. S., Turitsyn, K., & Kirtley, J. L. (2018). Adaptive Voltage and Frequency Control of Islanded Multi-Microgrids. IEEE Transactions on Power Systems, 33(4), 4454-4465. doi:10.1109/tpwrs.2017.2780986Sopinka, A., & Pitt, L. (2013). British Columbia Electricity Supply Gap Strategy: A Redefinition of Self-Sufficiency. The Electricity Journal, 26(3), 81-88. doi:10.1016/j.tej.2013.03.003Baghaee, H. R., Mirsalim, M., Gharehpetian, G. B., & Talebi, H. A. (2018). Decentralized Sliding Mode Control of WG/PV/FC Microgrids Under Unbalanced and Nonlinear Load Conditions for On- and Off-Grid Modes. IEEE Systems Journal, 12(4), 3108-3119. doi:10.1109/jsyst.2017.2761792Gholami, S., Saha, S., & Aldeen, M. (2018). Robust multiobjective control method for power sharing among distributed energy resources in islanded microgrids with unbalanced and nonlinear loads. International Journal of Electrical Power & Energy Systems, 94, 321-338. doi:10.1016/j.ijepes.2017.07.012Mousazadeh Mousavi, S. Y., Jalilian, A., Savaghebi, M., & Guerrero, J. M. (2018). Autonomous Control of Current- and Voltage-Controlled DG Interface Inverters for Reactive Power Sharing and Harmonics Compensation in Islanded Microgrids. IEEE Transactions on Power Electronics, 33(11), 9375-9386. doi:10.1109/tpel.2018.2792780Fani, B., Zandi, F., & Karami-Horestani, A. (2018). An enhanced decentralized reactive power sharing strategy for inverter-based microgrid. International Journal of Electrical Power & Energy Systems, 98, 531-542. doi:10.1016/j.ijepes.2017.12.023Khayat, Y., Naderi, M., Shafiee, Q., Batmani, Y., Fathi, M., Guerrero, J. M., & Bevrani, H. (2019). Decentralized Optimal Frequency Control in Autonomous Microgrids. IEEE Transactions on Power Systems, 34(3), 2345-2353. doi:10.1109/tpwrs.2018.2889671Arcos-Aviles, D., Pascual, J., Marroyo, L., Sanchis, P., & Guinjoan, F. (2018). Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids. IEEE Transactions on Smart Grid, 9(2), 530-543. doi:10.1109/tsg.2016.2555245Alyazidi, N. M., Mahmoud, M. S., & Abouheaf, M. I. (2018). Adaptive critics based cooperative control scheme for islanded Microgrids. Neurocomputing, 272, 532-541. doi:10.1016/j.neucom.2017.07.027Buduma, P., & Panda, G. (2018). Robust nested loop control scheme for LCL‐filtered inverter‐based DG unit in grid‐connected and islanded modes. IET Renewable Power Generation, 12(11), 1269-1285. doi:10.1049/iet-rpg.2017.0803Batiyah, S., Sharma, R., Abdelwahed, S., & Zohrabi, N. (2020). An MPC-based power management of standalone DC microgrid with energy storage. International Journal of Electrical Power & Energy Systems, 120, 105949. doi:10.1016/j.ijepes.2020.105949Baghaee, H. R., Mirsalim, M., Gharehpetan, G. B., & Talebi, H. A. (2018). Nonlinear Load Sharing and Voltage Compensation of Microgrids Based on Harmonic Power-Flow Calculations Using Radial Basis Function Neural Networks. IEEE Systems Journal, 12(3), 2749-2759. doi:10.1109/jsyst.2016.2645165Benhalima, S., Miloud, R., & Chandra, A. (2018). Real-Time Implementation of Robust Control Strategies Based on Sliding Mode Control for Standalone Microgrids Supplying Non-Linear Loads. Energies, 11(10), 2590. doi:10.3390/en11102590California Carbon Market Watch: A Comprehensive Analysis of the Golden State’s Cap-and-Trade Program, Year One—2012–2013. 2014https://www.issuelab.org/resource/california-carbon-market-watch-a-comprehensive-analysis-of-the-golden-state-s-cap-and-trade-program-year-one-2012-2013.htmlExploring the Best Possible Trade-Off between Competing Objectives: Identifying the Pareto Fronthttps://pythonhealthcare.org/2018/09/27/93-exploring-the-best-possible-trade-off-between-competing-objectives-identifying-the-pTeekaraman, Y., Kuppusamy, R., & Nikolovski, S. (2019). Solution for Voltage and Frequency Regulation in Standalone Microgrid using Hybrid Multiobjective Symbiotic Organism Search Algorithm. Energies, 12(14), 2812. doi:10.3390/en12142812Zeng, Z., Li, H., Tang, S., Yang, H., & Zhao, R. (2016). Multi‐objective control of multi‐functional grid‐connected inverter for renewable energy integration and power quality service. IET Power Electronics, 9(4), 761-770. doi:10.1049/iet-pel.2015.0317Wu, Y., Guerrero, J. M., Vasquez, J. C., & Wu, Y. (2019). Bumpless Optimal Control over Multi-Objective Microgrids with Mode-Dependent Controllers. Energies, 12(19), 3619. doi:10.3390/en12193619Sedighizadeh, M., Esmaili, M., & Eisapour-Moarref, A. (2017). Voltage and frequency r

Modeling and Contour Control of Multi-Axis Linear Driven Machine Tools

In modern manufacturing industries, many applications require precision motion control of multi-agent systems, like multi-joint robot arms and multi-axis machine tools. Cutter (end effector) should stay as close as possible to the reference trajectory to ensure the quality of the final products. In conventional computer numerical control (CNC), the control unit of each axis is independently designed to achieve the best individual tracking performance. However, this becomes less effective when dealing with multi-axis contour following tasks because of the lack of coordination among axes. This dissertation studies the control of multi-axis machine tools with focus on reducing the contour error. The proposed research explicitly addresses the minimization of contour error and treats the multi-axis machine tool as a multi-input-multi-output (MIMO) system instead of several decoupled single-input-single-output (SISO) systems. New control schemes are developed to achieve superior contour following performance even in the presence of disturbances. This study also extends the applications of the proposed control system from plane contours to regular contours in R3. The effectiveness of the developed control systems is experimentally verified on a micro milling machine

Robust pole placement using firefly algorithm

In this paper, the new automatic tool that is based on the firefly algorithm whose purpose is optimization of pole location in the control of state feedback has been presented. The aim is satisfying specifications of performance like settling and rise time, steady state as well as overshoot error. Utilization of Firefly algorithm has demonstrated the benefits of controllers based on this kind of time domain over controllers based on the frequency domain like Proportional-Integral Derivative (PID). The presented method is more particular for the multi-input multi-output (MIMO) systems that have substantial state numbers. The simulation results indicated that the proposed method had superior performance in providing solution to the problems that involved stabilization of helicopter under the Rationalized Model of helicopter/ Moreover, it demonstrates the Firefly algorithm effectiveness with regards to, the state observer design and feedback controller and auto-tuning

Particle swarm optimization based proportional-derivative parameters for unmanned tilt-rotor flight control and trajectory tracking

This paper presents the dynamic modelling and control technique for a tilt-rotor aerial vehicle operating in bi-rotor mode. This kind of aircraft combines two flight envelopes, making it ideal for scenarios that require hovering, vertical take-off/landing and fixed-wing capabilities. In this work, a detailed mathematical model is derived using Newton–Euler formalism. Based on the obtained model, a new control scheme that incorporates six Proportional-Derivative (PD) controllers is proposed for the attitudes (roll (φ), pitch (θ), yaw (ψ)) and the positions (x, y, z) of the aircraft. Then, intelligent Particle Swarm Optimization (PSO) and conventional Reference Model (RM) techniques are applied for optimal tuning of the controllers\u27 parameters. The stability analysis is developed using the Lyapunov approach and its application to the tilt-rotor system in the case of intelligent and conventional PD controllers. Numerical results of two scenarios prove the efficiency of the controllers tuned using the PSO method. Indeed, its ability to track the desired trajectories is demonstrated through 3D path tracking simulations, even in the presence of wind disturbances. Finally, experimental tests of stabilization and trajectory tracking are carried out on our prototype. These testing showed that our tilt-rotor was stable and suitably follows the imposed trajectories

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Hybrid active force control for fixed based rotorcraft

Disturbances are considered major challenges faced in the deployment of rotorcraft unmanned aerial vehicle (UAV) systems. Among different types of rotorcraft systems, the twin-rotor helicopter and quadrotor models are considered the most versatile flying machines nowadays due to their range of applications in the civilian and military sectors. However, these systems are multivariate and highly non-linear, making them difficult to be accurately controlled. Their performance could be further compromised when they are operated in the presence of disturbances or uncertainties. This dissertation presents an innovative hybrid control scheme for rotorcraft systems to improve disturbance rejection capability while maintaining system stability, based on a technique called active force control (AFC) via simulation and experimental works. A detailed dynamic model of each aerial system was derived based on the Euler–Lagrange and Newton-Euler methods, taking into account various assumptions and conditions. As a result of the derived models, a proportional-integral-derivative (PID) controller was designed to achieve the required altitude and attitude motions. Due to the PID's inability to reject applied disturbances, the AFC strategy was incorporated with the designed PID controller, to be known as the PID-AFC scheme. To estimate control parameters automatically, a number of artificial intelligence algorithms were employed in this study, namely the iterative learning algorithm and fuzzy logic. Intelligent rules of these AI algorithms were designed and embedded into the AFC loop, identified as intelligent active force control (IAFC)-based methods. This involved, PID-iterative learning active force control (PID-ILAFC) and PID-fuzzy logic active force control (PID-FLAFC) schemes. To test the performance and robustness of these proposed hybrid control systems, several disturbance models were introduced, namely the sinusoidal wave, pulsating, and Dryden wind gust model disturbances. Integral square error was selected as the index performance to compare between the proposed control schemes. In this study, the effectiveness of the PID-ILAFC strategy in connection with the body jerk performance was investigated in the presence of applied disturbance. In terms of experimental work, hardware-in-the-loop (HIL) experimental tests were conducted for a fixed-base rotorcraft UAV system to investigate how effective are the proposed hybrid PID-ILAFC schemes in disturbance rejection. Simulated results, in time domains, reveal the efficacy of the proposed hybrid IAFC-based control methods in the cancellation of different applied disturbances, while preserving the stability of the rotorcraft system, as compared to the conventional PID controller. In most of the cases, the simulated results show a reduction of more than 55% in settling time. In terms of body jerk performance, it was improved by around 65%, for twin-rotor helicopter system, and by a 45%, for quadrotor system. To achieve the best possible performance, results recommend using the full output signal produced by the AFC strategy according to the sensitivity analysis. The HIL experimental tests results demonstrate that the PID-ILAFC method can improve the disturbance rejection capability when compared to other control systems and show good agreement with the simulated counterpart. However, the selection of the appropriate learning parameters and initial conditions is viewed as a crucial step toward this improved performance