Automobiles evolved from primarily mechan- ical to electro-mechanical, or mechatronic, vehicles. Forexample, carburetors have been replaced by fuel injection and air-fuel ratio control, leading to order of magnitude improvements in fuel economy and emissions. Mecha-tronic systems are pervasive in modern automobiles andrepresent a synergistic integration of mechanics, electro-nics and computer science. They are smart systems, whosedesign is more challenging than the separate design of theirmechanical, electronic and computer/control components.In this review paper, two recent methods for the design of mechatronic components are summarized and their applications to problems in automotive control are high-lighted. First, the combined design, or co-design, of asmart artifact and its controller is considered. It is shownthat the combined design of an artifact and its controllercan lead to improved performance compared to sequentialdesign. The coupling between the artifact and controllerdesign problems is quanti ﬁed, and methods for co-design are presented. The control proxy function method, which provides ease of design as in the sequential approach andapproximates the performance of the co-design approach,is highlighted with application to the design of a passive/active automotive suspension. Second, the design forcomponent swapping modularity (CSM) of a distributedcontroller for a smart product is discussed. CSM is realizedby employing distributed controllers residing in networked smart components, with bidirectional communication over the network. Approaches to CSM design are presented, aswell as applications of the method to a variable-cam-timingengine, and to enable battery swapping in a plug-in hybridelectric vehicle. Keywords mechatronics, automotive control, co-design, component swapping modularity, active suspensions, vari- able camshaft timing engine, plug-in hybrid electric vehicle1 Introduction One of the things that most clearly distinguishes humans from other species in the animal kingdom is that humansmake and use tools. The famous ﬂuid mechanician Theodore von Karman once stated [1]: “A scientist studies what is, whereas an engineer creates what never was. ” Figure 1 captures the same notion of the engineer as a creator of physical artifacts (i.e., the tools used to shape theworld), in contrast to the scientist who studies the physicalworld [2]. Of course, there are no neat and sharp divisionsof the disciplines as schematically indicated in Fig. 1, andany engineering task can draw signi ﬁcantly from the sciences, arts and humanities. Consequently, we see anincreasing emphasis on interdisciplinarity in engineering education. The world we experience in our daily lives is very much an engineered, and not at all a natural, world. Buildings,roads, cars, trains, sewer lines, clean water supplies, electric power, computers, smart phones, internet, satel- lites, and many other engineered systems profoundly effectour lives every day. Furthermore, the pace of engineeringinnovation is accelerating, and many of these engineered Received August 1, 2018; accepted September 3, 2018 A. Galip ULSOY ( ✉) Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109-2125, USA E-mail: ulsoy@umich.edu Fig. 1 A classi ﬁcation of the disciplines humanities, arts, sciences and engineering. Engineering is the discipline associated with creating physical artifactsFront. Mech. Eng. 2019, 14(1): 102 –112 https://doi.org/10.1007/s11465-019-0527-0 THE AUTHORS WARRANT THAT THEY WILL NOT POST THE E-OFFPRINT OF THE PAPER ON PUBLIC WEBSITES. http://journal.hep.com.cn/ offprint offprintsystems we take for granted in our daily lives are products of only the past century and many more engineeringinnovations (e.g., fusion-based energy, low-cost solarenergy, virtual reality, secure cyberspace, autonomouscars) are expected in the coming century [3,4]. Engineers,and their innovations, literally and rapidly change theworld we live in. For example, the editors of MIT Technology Review ,i n February 2003, identi ﬁed“10 Emerging Technologies That Will Change the World ”[5]. One of those 10 technologies isMechatronics , and the editors stated: “To improve everything from fuel economy to performance, automotiveresearchers are turning to mechatronics, the integration offamiliar mechanical systems with new electronic compo-nents and intelligent-software control. Take brakes. In thenext 5 to 10 years, electromechanical actuators will replace hydraulic cylinders; wires will replace brake ﬂuid lines; and software will mediate between the driver ’s foot and the action that slows the car. ”Thus, the century old automobile, the preferred mode for personal mobilitythroughout the world, is rapidly becoming a complexelectro-mechanical system, with dozens of networkedmicroprocessors in every vehicle [6]. Various new electro-mechanical technologies are being added to automobiles to improve operational safety, reduce congestion and energy consumption, and minimize environmental impact. Cur-rent vehicles often include many new features, which werenot widely available even just a few decades ago.Examples include electric or hybrid powertrains, electronicengine and transmission controls, cruise and headwaycontrol, anti-lock brakes, differential braking, vehiclestability systems, and active/semi-active suspensions. Many of these functions can be, and have been, achieved using purely mechanical devices. The major advantages inusing electro-mechanical (or mechatronic) devices, asopposed to their purely mechanical counterparts include:(1) The ability to embed knowledge about the systembehavior into the design of the system itself, (2) theﬂexibility inherent in these systems to trade-off among different goals, and (3) the potential to coordinate the functioning of subsystems. Knowledge about system behavior, in terms of vehicle, engine or even driverdynamic models, or constraints on physical variables, areincluded in the design of these electro-mechanical systems.Flexibility enables adaptation to the environment, thusproviding more reliable performance under a wide varietyof conditions. Furthermore, re-programmability implieslower cost through exchangeable parts and reuse. Exchange of information makes it possible to integrate sub-systems and obtain superior performance and func-tionality, which are not possible with un-coordinatedsystems. Thus, these are smart systems whose engineeringdesign is more challenging than simply the separate andsequential design of their mechanical, electronic andcomputer/control components. In this article, two recent methods for the design of suchmechatronic systems, or smart products, are summarized and their applications to problems in automotive controlare highlighted. First the combined design, or co-design ,o f a smart artifact and its controller is considered [7 –31]. It is shown that the combined design of an artifact and itscontroller can lead to improved performance compared tosequential design (i.e., design the artifact ﬁrst, then design its controller). The coupling between the artifact and controller design problems is quanti ﬁed, and methods for co-design are presented. The control proxy function (CPF) method, which provides ease of design as in the sequentialapproach and approximates the performance of the co-design approach, is highlighted with application to thedesign of a passive/active automotive suspension. Second,the design for component swapping modularity (CSM) of a distributed controller for a smart product is discussed [32 – 50]. CSM is realized by employing distributed controllers residing in networked smart components, with bidirec-tional communication over the network. Severalapproaches to CSM design are presented and discussed.Applications of the method to a variable-cam-timingengine, and to enable battery swapping in a plug-in hybridelectric vehicle, are highlighted. 2 Coupling between artifact and controller design problems Mechatronic systems, or smart products, arise in the design of many modern engineered systems. For example,actively controlled structures such as lightweight satellitesand bridges must minimize both weight and vibrations.Robots and automated machines also need to minimizeweight, while achieving high accuracy and repeatability. Micro-electro-mechanical systems (MEMS) must achieve a range of motion with fast response and high accuracy.Consequently, smart products typically require the designof a mechanical artifact (e.g., a lightweight solar panel, alinear axis) as well as the design of a controller for thatartifact (e.g., active vibration suppression, axis controller).Traditionally, one ﬁrst designs the artifact, then given that artifact designs the controller, in a sequential design process. Such a sequential design process often works well, and leads to good results. However, numerous studiesin the literature have clearly demonstrated that thecombined , or simultaneous, design of the artifact and its controller can lead to better performance. The quanti ﬁca- tion of the coupling between the artifact and controllerdesign problems is considered in the next subsection.When the coupling between these two design problems is zero (or weak) one can use the traditional sequential approach and achieve excellent performance. If thecoupling is strong, then a co-design approach is neededto achieve system optimality, and various approaches toco-design have been considered and are summarized. Oneapproach, discussed in the second subsection, is to use aA. Galip ULSOY. Smart product design for automotive systems 103 THE AUTHORS WARRANT THAT THEY WILL NOT POST THE E-OFFPRINT OF THE PAPER ON PUBLIC WEBSITES. http://journal.hep.com.cn/ offprint offprintCPF, which can provide the system optimal results (or near system optimal results) of a co-design while maintainingthe ease of design (both organizationally and computa-tionally) of the sequential design approach. 2.1 Coupling between artifact and controller design problems Consider an artifact design problem , formulated as a constrained minimization problem: min faðdaÞ, (1) subject to gaðdaÞ£0 haðdaÞ¼0:( (2) The artifact objective function, fa, is to be minimized subject to inequality constraints, ga, and equality con- straints, ha, with respect to the artifact design variables, da. Similarly, one can de ﬁne a controller design problem as min fcðda,dcÞ, (3) subject to gcðda,dcÞ£0 hcðda,dcÞ¼0:( (4) The controller objective function, fc, is to be minimized subject to inequality constraints, gc, and equality con- straints, hc, with respect to the vector of artifact design variables, daand the controller design variables, dc. The optimal design problems in Eqs. (1) –(4) are said to exhibit unidirectional coupling , since the control design problem depends on daanddc, but the artifact design problem depends only on da. In this review only unidirectional coupling is considered, since many design problems forsmart products can be formulated as such. The more general bidirectional coupling case, where f a,ga, and ha also depend on dc, is discussed in Refs. [10,15,23,25]. The traditional, sequential , approach is to ﬁrst solve the artifact design problem in Eqs. (1) and (2) to obtain da= da*, then ﬁx the value of the artifact design variables as da= da*in Eqs. (3) and (4) while solving for the controller design variables dc. Alternatively, one can also formulate a combined, or simultaneous, or co-design , problem as minfwafaðdaÞþwcfcðda,dcÞg, (5) subject to: gaðdaÞ£0 haðdaÞ¼0 gcðda,dcÞ£0 hcðda,dcÞ¼0,8 >>>< >>>:(6)where w aandwc(wa+wc= 1) are the weights assigned to the artifact and controller design objectives, respectively,and the minimization is carried out with respect to both theartifact, d a, and controller, dc, design variables. First-order necessary conditions for optimality of optimal design problems such as Eqs. (1) and (2), Eqs.(3) and (4) or Eqs. (5) and (6), with both equality andinequality constraints, are termed the Karush-Kuhn-Tucker (KKT) conditions. If one derives the KKT conditions for the co-design problem in Eqs. (5) and (6) and comparesthem to the KKT conditions for the sequential designproblem (i.e., ﬁrst solve Eqs. (1) and (2) for d a*, then solve Eqs. (3) and (4) for dc*, given da*), there is only one term that is different [8,10]: Γv¼wc wa∂f* c ∂da¼wc wa∂fc ∂daþ∂fc ∂dcddc dda/C18/C19 : (7) Consequently, one can refer to Gvin Eq. (7) as the coupling vector , which quanti ﬁes the coupling between the plant and controller optimization problems by consideringthe in ﬂuence of plant design on the optimal attainable control objective. The Euclidean norm of this vector,evaluated at the optimal solution, can be used to characterize the coupling strength between the artifact and controller design problems. Speci ﬁcally, when the coupling vector is zero one is said to have objective decoupling . When the coupling vector is non-zero, but active constraints prevent the system from achieving a zerovalue of the coupling vector, one is said to have constraint decoupling . When either of these decoupling conditions are satis ﬁed, the artifact design problem in Eqs. (1) and (2) and the controller design problem in Eqs. (3) and (4) become decoupled. Thus, yielding a sequential solution,which is identical to the co-design solution. When the twodesign problems are not decoupled, the solution to thesequential problem will not be as good as the solution tothe co-design problem. A more general measure ofcoupling, applicable to systems with bi-directional cou-pling, and with more than two coupled design problems, can be found in Refs. [15,23,25]. In this review paper, our discussion is restricted to the coupling measure given bythe vector G vin Eq. (7). While some systems are uncoupled, or weakly coupled, many systems do exhibit strong coupling. In particular,uncertainty in system parameters and stringent perfor-mance requirements can both lead to strongly coupledsystems. In such cases traditional sequential optimization is not recommended, but how then should such coupled artifact/controller design problems best be solved?Figure 2 schematically illu strates various proposed approaches to solving such coupled design problems.These include iterative methods, which can include nestingand partitioning, to try to retain the simplicity andconvenience of the sequential solution approach whileproviding the system optimal solutions available via104 Front. Mech. Eng. 2019, 14(1): 102 –112 THE AUTHORS WARRANT THAT THEY WILL NOT POST THE E-OFFPRINT OF THE PAPER ON PUBLIC WEBSITES. http://journal.hep.com.cn/ offprint offprintco-design ( akacombined design or simultaneous design). The iterative methods, depending on the problem, may ormay not converge to the system optimal solution availablevia co-design. One successful iterative solution methoduses the coupling vector in Eq. (7) to “steer ”the solution to that which would have been obtained via co-design [28]. Aco-design solution is generally not easy to obtain.Computationally it typically leads to non-convex optimi- zation, which can often be a combined static and dynamic optimization. Furthermore, it is organizationally incon-venient as well, since many engineering organizations willhave separate engineering groups with expertise in theartifact design and the controller design, respectively.Consequently, it is desirable to have solution methods thatcan provide the convenience of a sequential approach,while providing the solution of a co-design approach. 2.2 Control proxy function As noted in the previous subsection, the traditionalsequential design, where we ﬁrst solve Eqs. (1) and (2) then solve Eqs. (3) and (4), provides a very convenientdesign approach but does not necessarily yield the systemoptimal solution that one would achieve by solving the co-design problem in Eqs. (5) and (6). Thus, researchers haveconsidered the possibility of modifying the sequentialapproach to obtain system optimal results while preserving the convenience of the sequential approach. One proposal is to modify the artifact design problem in Eqs. (1) and (2)such that the resulting artifact becomes, in some sense, easier to control in the second step (i.e., Eqs. (3) and (4)).Researchers have proposed, and tried, various methods,and the CPF method is described in the following[22,23,26,29,31]. Consider a modi ﬁed artifact design problem, formulated as a constrained minimization problem: minff aðdaÞþαχðdaÞg, (8) subject to gaðdaÞ£0 haðdaÞ¼0:( (9) The new artifact objective function, fa+αχ,i st ob e minimized subject to inequality constraints, ga,a n d equality constraints, ha, with respect to the vector of artifact design variables, da. The scalar function χ(da) is the CPF and αis a weight. If the designer can choose an appropriate CPF and a weight, then the solution to thismodiﬁed sequential problem could be equal to, or close to, the solution obtained via co-design. Various CPFs (e.g.,natural frequencies) have been suggested in the literature, and here a scalar function of the steady-state controllability Gramian is considered: χðd aÞ¼1=detðW1 cÞ, (10) where for a linear time invariant dynamic system, _xðtÞ¼AðdaÞxðtÞþBðdaÞuðtÞ, the steady-state controll- ability Gramian is de ﬁned as Fig. 2 Solution methods for coupled systemsA. Galip ULSOY. Smart product design for automotive systems 105 THE AUTHORS WARRANT THAT THEY WILL NOT POST THE E-OFFPRINT OF THE PAPER ON PUBLIC WEBSITES. http://journal.hep.com.cn/ offprint offprintW1 c¼!1 0eAðdaÞtBðdaÞBTðdaÞðeAðdaÞtÞTdt, (11) and can be obtained as the solution to the Lyapunov equation: AðdaÞW1 cþW1 cATðdaÞ¼BðdaÞBTðdaÞ: (12) Notice that Wc1is only a function of daand does not depend on dc. Furthermore, the minimum control effort needed to achieve a control objective is related to theinverse of the controllability Gramian. Consequently, thescalar function of the controllability Gramian given in Eq. (10) is one good candidate CPF . Other candidate CPFs can be found in Refs. [20 –23,29 –31]. When one solves Eqs. (8) and (9) followed by Eqs. (3) and (4) and obtainsthe system optimal co-design solution, then the CPF inEq. (8) is termed a perfect CPF , and satis ﬁes certain optimality conditions [10,15,23]. Nevertheless, even whena candidate CPF is not a perfect CPF it can often steer thesolution closer to the system optimal co-design solution, as illustrated in the following passive/active car suspension design problem [29]. A detailed discussion of when theCPF approach to optimal co-design should be preferred isgiven in Ref. [31]. The passive/active suspension design is based upon a 2-degree-of-freedom (i.e., four state) quarter-car model anda linear quadratic optimal control approach [6]. The artifactdesign variables are the passive suspension stiffness, k s, and damping, cs: da¼kscs ½/C138T, (13) while the controller design variables, dc, are the gains of a state feedback controller: dc¼k1k2k3k4 ½/C138T,uðtÞ¼ –dT cx: (14) The objective function for the co-design optimization of this system is Jðda,dcÞ¼Jqðda,dcÞþr3Juðda,dcÞ, (15) where Jq¼!1 0ðr0_x2 4þr1x2 1þr2 23Þdt,Ju¼!1 0u2dt: (16) The sequential optimization problem is formulated as in Eqs. (1) and (2) with the control gains dc= 0 in evaluating Jqand r3=Ju= 0. Then given the solution da=da*, the objective given in Eq. (15) is minimized with respect to dc. The CPF designs are obtained in the same sequentialfashion, but using Eq. (8) instead of Eq. (1) in the ﬁrst step. The candidate CPF, based on the controllability Gramian,is given by Eq. (10). The weights, r 0,r1,r2, and r3, have been selected to be typical of a suspension design for asporty vehicle [29]. The results of the passive/active suspension design study are given in the Pareto curve in Fig. 3, which shows thetrade-off between improved (lower) ride quality andimproved (lower) control effort. Note that for a givendesired level of the ride quality measure J q(e.g., 35), the required control effort Juis much higher (e.g., 2 vs. 1.3) when a sequential design is used instead of a simultaneous (or co-design) approach. Consequently, the co-design achieves the same suspension performance with muchless engine power required. Furthermore, note that the CPFdesigns, with two different weights r 4, lie between the curves for the sequential and co-design cases. Conse-quently, the candidate CPF selected here improves thedesign compared to the sequential case, but is not a perfectCPF. Other examples of design using the CPF approach include a MEMS actuator [18,23], which shows an example of a perfect CPF, and a passive/active vibrationisolator [49], which shows an extension of Eq. (10) to ease-of-state-estimation as well as ease-of-control by use of abalanced realization such that the controllability andobservability matrices of a dynamic system are equal. 3 Controller design for component swapping modularity Charles Darwin wrote: “It is not the strongest species that survive, nor the most intelligent, but those most responsiveto change ”[51]. This is not only true for natural systems, but for engineered systems as well. It is desirable to designand build engineered systems that can change as needed in response to external factors, such as consumer p