Multi-objective Optimization of Multi-loop Control Systems

Marshall University Marshall Digital Scholar Theses, Dissertations and Capstones 2020 Multi-objective Optimization of Multi-loop Control Systems Yuekun Chen 1102306990@qq.com Follow this and additional works at: https://mds.marshall.edu/etd Part of the Acoustics, Dynamics, and Controls Commons Recommended Citation Chen, Yuekun, "Multi-objective Optimization of Multi-loop Control Systems" (2020) Theses, Dissertations and Capstones 1270 https://mds.marshall.edu/etd/1270 This Thesis is brought to you for free and open access by Marshall Digital Scholar It has been accepted for inclusion in Theses, Dissertations and Capstones by an authorized administrator of Marshall Digital Scholar For more information, please contact zhangj@marshall.edu, beachgr@marshall.edu MULTI-OBJECTIVE OPTIMIZATION OF MULTI-LOOP CONTROL SYSTEMS A thesis submitted to the Graduate College of Marshall University In partial fulfillment of the requirements for the degree of Master of Science In Mechanical Engineering by Yuekun Chen Approved by Dr Yousef Sardahi, Committee Chairperson Dr Gang Chen Dr Mehdi Esmaeilpour Marshall University May 2020 ii ACKNOWLEDGMENTS I would like to express my gratitude to all those who helped me during the writing of this thesis I gratefully acknowledge the help of my supervisor, Dr Yousef Sardahi, who has offered me valuable suggestions in the academic studies Without his consistent and illuminating instruction, this thesis could not have reached its present form Second, I would like to express my heartfelt gratitude to my thesis committee: Dr Gang Chen and Dr Mehdi Esmaeilpour, for their instruction and assistance Finally, I would like to thank my beloved family and my friends for their continuous support and encouragement Without their trust and help, I couldn’t have the strong motivations to urge me working hard on this thesis Thank you all iii TABLE OF CONTENTS List of Tables vi List of Figures vii Abstract xii Chapter 1: Introduction 1.1 Literature Review 1.2 Multi-Objective Optimization 1.3 NSGA-II 1.4 Outline of the Thesis 11 Chapter 2: Multi-Objective Optimal Design of a Cascade Control System for a Class of Underactuated Mechanical Systems 13 2.1 Cascade control systems 13 2.2 Underactuated Ball and Beam System 16 2.3 Multi-Objective Optimal Design 18 2.4 Results and discussion 19 Chapter 3: Multi-Objective Optimal Design of an Active Aeroelastic Cascade Control System for an Aircraft Wing With a Leading and Trailing Control Surface 27 3.1 Introduction 27 3.2 Airfoil wing model with two control surfaces 30 3.3 LQR-based Outer Control Loop 32 3.4 Actuator Dynamics 35 3.5 PV-based Inner Control Loop 37 3.6 Multi-objective and Multidisciplinary Optimal Design 39 iv 3.7 Results and Discussion 42 3.7.1 Pareto Frontier and Set 42 3.7.2 Closed-Loop Eigenvalues 43 3.7.3 Gust Loading Impact 44 Chapter 4: Summary and future directions 52 4.1 Conclusions 52 4.2 Future Works 53 References 54 Appendix A: INSITITUTIONAL REVIEW BOARD LETTER 59 Appendix B: 60 B.1 Aircraft Flexible Wing 60 B.2 Electromagnetic Actuator 61 B.3 Slider-Crank Mechanism 62 v LIST OF TABLES Table 1: The model parameters (Singh et al., 2016) 60 Table 2: Motor parameters (Habibi et al., 2008) 62 vi LIST OF FIGURES Figure 1: NSGA-II algorithm flowchart 10 Figure 2: Block diagram of two-level cascade control system 13 Figure 3: Ball and beam system 16 Figure 4: Projections of the Pareto set: (a) 𝐾𝑑𝑖 versus 𝐾𝑝𝑖 , (b) 𝐾𝑑𝑜 versus 𝐾𝑝𝑜 The color code indicates the level of ||𝑘||𝐹 , where red denotes the highest value, and dark blue denotes the smallest 22 Figure 5: Projections of the Pareto front: (a) 𝐹1 versus ||𝑘||𝐹 , (b) 𝐹2 versus ||𝑘||𝐹 The color code indicates the level of ||𝑘||𝐹 , where red denotes the highest value, and dark blue denotes the smallest 23 Figure 6: Projections of the Pareto front: (a) r versus ||𝑘||𝐹 , (b) 𝐹2 versus 𝐹1 The color code indicates the level of ||𝑘||𝐹 , where red denotes the highest value, and dark blue denotes the smallest 23 Figure 7: Pole maps, on the y-axis is the imaginary part of the pole, Im(s), and the x-axis is the real part of the pole, Re(s): (a) Pole map of the inner closed-loop system, (b) Pole map of the outer closed-loop system The color code indicates the level of ||𝑘||𝐹 , where red denotes the highest 24 Figure 8: Outer and inner controlled systems’ responses when r = 0.5 (a) Response of the outer closed-loop system 𝑥𝑜 (𝑡)versus time, (b) Response of the inner closed-loop system 𝑥𝑜 (𝑡)versus time Red solid line: reference signal, Black solid line: actual system, response with 𝑑𝑖 (t) = 𝑑𝑜 (t) = 0.5sin(t) 24 vii Figure 9: Outer and inner controlled systems’ responses when r = 0.07 (a) Response of the outer closed-loop system 𝑥𝑜 (𝑡)versus time, (b) Response of the inner closed-loop system 𝑥𝑜 (𝑡)versus time Red solid line: reference signal, Black solid line: actual system response with 𝑑𝑖 (t) = 𝑑𝑜 (t) = 0.5sin(t) 25 Figure 10: Ball position versus time (a) Controlled system response at (𝐹1 ), (b) Controlled system response at max (𝐹1 ) Red solid line: reference signal 𝑥𝑑 (𝑡), black solid line: system response with 𝑑𝑖 (t) = 𝑑𝑜 (t) = 0, blue dotted line: system response with 𝑑𝑖 (t) = 𝑑𝑜 (t) = 0.5sin(t) 25 Figure 11: Ball position versus time (a) Controlled system response at (||𝑘||𝐹 ), (b) controlled system response at max (||𝑘||𝐹 ) Red solid line: reference signal 𝑥𝑑 (𝑡), black solid line: system response with 𝑑𝑖 (t) = 𝑑𝑜 (t)= 0, blue dotted line: system response with 𝑑𝑖 (t) = 𝑑𝑜 (t)= 0.5sin(t) 26 Figure 12: Ball position versus time (a) Controlled system response at (𝐹2 ), (b) Controlled system response at max (𝐹2 ) Red solid line: reference signal 𝑥𝑑 (𝑡), black solid line: system response with 𝑛𝑖 (t) = 𝑛𝑜 (t) = 0, blue dotted line: system response with 𝑛𝑖 (t) = 𝑛𝑜 (t) = WN 26 Figure 13: Cascade control system of aeroelastic structure and actuators 29 Figure 14: Airfoil wing model with two control surfaces (Singh et al., 2016) 30 Figure 15: A generic EMA system (Habibi et al., 2008) 36 Figure 16: Control surface driven by slider-crank mechanism 37 Figure 17: Projections of the Pareto front: (a) Eav versus Dav , (b) Eav versus r The color code indicates the level of Eav , where red denotes the highest value, and dark blue denotes the smallest 45 viii Figure 18: Projections of the Pareto set: (a) k pT versus k dT (b) k pL versus k dL The color code indicates the level of Eav , where red denotes the highest value, and dark blue denotes the smallest 45 Figure 19: Projections of the Pareto set: (a) Q1 versus Q3 (b) Q2 versus Q4 The color code indicates the level of Eav , where red denotes the highest value, and dark blue denotes the smallest 46 Figure 20: A Projection of the Pareto set: R1 versus R The color code indicates the level of Eav , where red denotes the highest value, and dark blue denotes the smallest 46 Figure 21: Pole maps, on the y-axis is the imaginary part of the pole, imag(λ), and the x-axis is the real part of the pole, real(λ): (a) Pole map of the outer controlled system: outer control loop and aeroelastic structure, (b) Pole map of the inner controller applied to the trailing actuator, and (c) Pole map of the inner controller applied to the leading actuator 47 Figure 22: Dominant pole maps, the x-axis is the location of pole closer to the imaginary axis, max(real(λ)) the y-axis is unlabeled, and: (a) Dominant pole map of the outer controlled system: outer control loop and aeroelastic structure, (b) Dominant pole map of the trailing and leading inner controllers, (c) Dominant pole map of the inner controller applied to the trailing actuator, and (d) Dominant pole map of the inner controller applied to the leading actuator 47 Figure 23:Gust load wg (𝑡) profile versus time 48 Figure 24: Controlled systems’ responses when the disturbance rejection is the best (𝐷𝑎𝑣 ) Top left: time versus the plunging displacement (h) Top right: time versus the plunging the pitching angle α Bottom left: time versus the actual X T and desired XdT ball-screw mechanism displacement of the actuator at the trailing aileron Bottom Right: time versus the actual XL and desired XdL ball-screw mechanism displacement of the actuator at the leading aileron 48 ix versus the actual 𝑿𝑳 and desired 𝑿𝒅𝑳 ball-screw mechanism displacement of the actuator at the leading aileron Figure 27: Controlled systems’ responses when the control energy is the minimum min(𝑬𝒂𝒗 ) Top left: time versus the plunging displacement (h) Top right: time versus the plunging the pitching angle α Bottom left: time versus the actual 𝑿𝑻 and desired 𝑿𝒅𝑻 ballscrew mechanism displacement of the actuator at the trailing aileron Bottom Right: time versus the actual 𝑿𝑳 and desired 𝑿𝒅𝑳 ball-screw mechanism displacement of the actuator at the leading aileron 50 Figure 28: Controlled systems’ responses when the inner closed-loop algorithms are way faster than outer control loop max (r) Top left: time versus the plunging displacement (h) Top right: time versus the plunging the pitching angle α Bottom left: time versus the actual 𝑿𝑻 and desired 𝑿𝒅𝑻 ball-screw mechanism displacement of the actuator at the trailing aileron Bottom Right: time versus the actual 𝑿𝑳 and desired 𝑿𝒅𝑳 ball-screw mechanism displacement of the actuator at the leading aileron Figure 29: Controlled systems’ responses when the inner closed-loop algorithms are way slower than outer control loop max (r) Top left: time versus the plunging displacement (h) Top right: time versus the plunging the pitching angle α Bottom left: time versus the actual 𝑿𝑻 and desired 𝑿𝒅𝑻 ball-screw mechanism displacement of the actuator at the trailing aileron Bottom Right: time versus the actual 𝑿𝑳 and desired 𝑿𝒅𝑳 ball-screw mechanism displacement of the actuator at the leading aileron 51 CHAPTER 4: SUMMARY AND FUTURE DIRECTIONS 4.1 Conclusions We have studied the multi-objective optimal design of a two cascaded controller based on two PD controllers A numerical example which consists of a servo DC motor and ball-beam system is used The optimization problem with four design parameters and four conflicting objective functions is solved with the NSGA-II algorithm The Pareto set and front are obtained The Pareto set includes multiple design options from which the decision-maker can choose to implement The results show there are many optimal trade-offs among load disturbance rejection, measurement noise repudiation, control energy saving, tracking error reduction, and relative speed of response of the inner loop subsystem with respect to the outer one Also, the pole maps of the control loops demonstrate that the inner closed-loop system has a faster dynamic than that of the outer controlled system We have also investigated the multi-objective optimal design of three cascaded controllers, two slave algorithms applied to the actuators and a master controller for the aircraft’s wing The outer algorithm is based on the optimal LQR algorithm while the inner loops are PVbased controllers A numerical example which consists of an aircraft’s flexible structure and two EMA actuators are used The optimization problem with ten design parameters and three conflicting objective functions is solved with the NSGA-II algorithm The Pareto set and front are obtained, and the results show inherit trade-offs among the design goals The pole locations of the three subsystems clearly show that the inner closed-loop systems are faster than that of the outer controlled system 52 4.2 Future Works Future work will include designing an optimal and multidisciplinary cascade controller aeroelastic structures or aircraft wings with different number of ailerons The design will include the controllers’ gains as well as the geometrical parameters of the control surfaces Also, the backlash effect on the ball-screw mechanism connected to the DC motor will be investigated Furthermore, the dynamic of the slider-crank mechanism and its effect on the system behavior will be included in the future studies 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does not exist in the original model and it was added to show the effect of the aerodynamic loads on the system performance The elements of 𝑩𝑎𝑑 were estimated by comparing the values of the control distribution matrix 𝑩𝑐𝑠 and the aerodynamic load distribution matrix 𝑩𝑎𝑑 proposed by Kumar et al (2012b) with those of the model at hand The 2D lift-curve slope was set to 2𝜋 since the ideal lift curve slope of any 2D wing is 2𝜋 In fact, inspecting wind tunnel data for any airfoil shape, it can be found that the slope of the lift curve is very close to this value (Aerospaceweb, 2012) Retrieved from http://www.aerospaceweb.org/question/aerodynamics/q0167.shtml Symbol Definition Value 𝜌 air density 𝛼 pitching angle (positive nose up) -0.6719 b semichord 0.1905,m 1.225,kg/𝑚3 60 𝑟𝑐𝑔 distance from elastic axis to center of mass 𝑥𝑎 nondimensional distance from elastic axis to center of mass s -b(0.0998+α) , m 𝑟𝑐𝑔 /𝑏 semispan 0.5945, m 𝑘ℎ Plunge stiffness 2844,N/m k𝛼 pitch stiffness 12.77,Nm/rad 𝐶𝑙𝛼 lift derivative with respect to pitch angle α 6.757 𝐶𝑙𝛽𝑇 lift derivative with respect to trailing-edge control angles 3.774 𝐶𝑙𝛽𝐿 lift derivative with respect to leading-edge control angles +1 𝐶𝑚𝛼 𝑐ℎ plunge 𝑐𝛼 pitch damping 𝑚𝑤 mass of wing 4.340,kg 𝑚𝑤𝑇 total wing section and mount mass 5.230,kg 𝑚𝑇 total mass of pitch–plunge system 15.57,kg 𝐼𝑐𝑎𝑚 pitch cam moment of inertia 0.04697,kg∙ 𝑚2 𝐼𝑐𝑔𝑤 wing section moment of inertia about the center of gravity 0.04342, kg∙ 𝑚2 𝐼𝑎 27.43,kg/s 0.036, kg ∙ 𝑚2 /𝑠 total pitch moment of inertia about elastic axis 𝐶𝑚𝛽𝐿 , 𝐶𝑚𝛽𝐿𝑇 effective trailing- and leading-edge control derivatives, respectively 𝐼𝑐𝑎𝑚 + 𝐼𝑐𝑔𝑤 + 𝑚𝑤 𝑟𝑐𝑔 -0.1005,-0.6719 𝐶𝑚𝛼eff effective moment derivative 𝐶𝑚𝛽Teff effective trailing-edge control derivatives (0.5+α)𝐶𝑙𝛽𝑇 + 2C𝑚𝛽𝑇 𝐶𝑚𝛽Leff effective leading-edge control derivatives (0.5+α)𝐶𝑙𝛽𝐿 + 2C𝑚𝛽𝐿 𝑎𝑤 (0.5+α)𝐶𝑙𝛼 + 2𝐶𝑚𝛼 2𝜋 2D lift-curve slope Table 1: The model parameters (Singh et al., 2016) B.2 Electromagnetic Actuator The EMA shown in Figure 15 (see Chapter 3) is described by the following equations 𝐺𝑒 = 1/𝑅𝑐 𝐿𝑐 +1 𝑅𝑐 𝑠 1/𝑅𝑐 =𝜏 , (B.2) 𝑒 𝑠+1 61 𝜏𝑒 and 1/𝑅𝑐 are the motor’s electrical time constant and gain Assuming that the inductance is very small (𝐿𝑐 = → 𝜏𝑒 = 0), which is the case in many inductive loads The motor’s dynamics can be reduced to the following transfer function 𝐺𝑒 =1/𝑅𝑐 (B.3) The transfer function of the mechanical part of the motor (motor shaft and gearbox) is approximated by 𝐺𝑚𝑒𝑐ℎ such that 𝐺𝑚𝑒𝑐ℎ = 1⁄𝐾𝑚𝑣 𝐽𝑚 𝑠+1 𝐾𝑚𝑣 =𝜏 𝐾𝑚 , (B.4) 𝑚 𝑠+1 Definitions and values of some of the parameters used in the computer simulations are tabulated in Table Symbol Definition 𝐽𝑚 Rotor inertia 𝐾𝑐 Torque constant Value 0.000391, lb 𝑖𝑛.2 2.376, in.lb/A 𝐾𝑚𝑣 Viscous friction and damping 𝐾𝜔 Back emf constant 𝑅𝑐 Winding resistance τm Mechanical time constant 0.00116, in.lb s/rad 0.1342, V s/rad 2.12, Ω 0.3371, s Table 2: Motor parameters (Habibi et al., 2008) B.3 Slider-Crank Mechanism The kinematic equations of the slider-crank mechanism in Figure 16 (see chapter 3) read x = (𝑎 + b) − (b cos Φ + 𝑎 cos 𝛽) 𝑏 X = 𝑎 [𝑎 (1 − cos Φ) + (1 − β)] Knowing that sin Φ2 + cos Φ2 = 1, cos Φ2 = − sin Φ2, cos Φ = √1 − sin Φ2 and setting 𝑏 n = 𝑎, we notice that sin Φ = sin 𝛽 𝑛 After few steps of mathematical substitutions and 62 simplifications, the relationship between the rock-pinion displacement X and slider-crank angular displacement 𝛽 can be found as follows cos Φ = √1 − sin Φ2 = √1 − X = 𝑎 [𝑛 (1 − √1 − 𝑋 𝑎 𝑋 𝑎 = [𝑛 (1 − √1 − = 𝑛 − 𝑛 √1 − sin 𝛽 𝑛2 sin 𝛽 𝑛2 sin 𝛽 𝑛2 sin 𝛽 𝑛2 ) + (1 − cos 𝛽)] (B.5) ) + (1 − cos 𝛽)] + − cos 𝛽 𝑋 𝑛2 − sin 𝛽 = 𝑛 − 𝑛√ + − cos 𝛽 𝑎 𝑛2 𝑋 = n − √𝑛2 − sin 𝛽 + − cos 𝛽 𝑎 𝑋 − n − = −√𝑛2 − sin 𝛽 − cos 𝛽 𝑎 √𝑛2 − sin 𝛽 + cos 𝛽 = + 𝑛 − now, sin 𝛽 + cos 𝛽 = 𝑋 𝑎 sin 𝛽 = − cos 𝛽 √𝑛2 − + cos 𝛽 + cos 𝛽 = + 𝑛 − 𝑋 𝑎 𝐴 = cos 𝛽 { 𝐵 =1+𝑛− 𝑋 𝑎 √𝑛2 − + 𝐴2 + A = 𝐵 𝑛2 − + 𝐴2 = 𝐵 + 𝐴2 − 2AB 63 𝐵 − 𝑛2 + A= 2𝐵 𝑋 cos 𝛽 = (1 + 𝑛 − 𝑎 )2 − 𝑛2 + 𝛽 =arccos 𝑋 2（1 + 𝑛 − 𝑎 ） 𝑋 𝑎 (1+𝑛− )2 −𝑛2 +1 (B.6) 𝑋 𝑎 2（1+𝑛− ） 64 .. .MULTI-OBJECTIVE OPTIMIZATION OF MULTI-LOOP CONTROL SYSTEMS A thesis submitted to the Graduate College of Marshall University In partial fulfillment of the requirements for the degree of Master... colleagues (Kumar et al., 2012a) developed a multi-objective optimal control of a multi-loop controller consiting of a PI controller in its inner and outer loop The control algorithm was used to regulate... delineate the working principle of NSGA-II, elaborate on the structure of cascade control systems, and outline the thesis 1.2 Multi-Objective Optimization Multi-objective optimization problems (MOPs)