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1362IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 52, NO. 8, AUGUST 2007Trajectory-Tracking and Path-Following ofUnderactuated Autonomous Vehicles WithParametric Modeling UncertaintyA. Pedro Aguiar, Member, IEEE, and João P. Hespanha, Senior Member, IEEEAbstract—We address the problem of position trajectory-tracking and path-following control design for underactuatedautonomous vehicles in the presence of possibly large modelingparametric uncertainty. For a general class of vehicles moving ineither 2- or 3-D space, we demonstrate how adaptive switchingsupervisory control can be combined with a nonlinear Lyapunov-based tracking control law to solve the problem of globalboundedness and convergence of the position tracking error to aneighborhood of the origin that can be made arbitrarily small.The desired trajectory does not need to be of a particular type(e.g., trimming trajectories) and can be any sufficiently smoothbounded curve parameterized by time. We also show how theseresults can be applied to solve the path-following problem, inwhich the vehicle is required to converge to and follow a path,without a specific temporal specification. We illustrate our designprocedures through two vehicle control applications: a hovercraft(moving on a planar surface) and an underwater vehicle (movingin 3-D space). Simulations results are presented and discussed.Index Terms—Path-following, supervisory adaptive control, trajectory-tracking, underactuated autonomous vehicles.I. INTRODUCTIONTHE past few decades have witnessed an increased researcheffort in the area of motion control of autonomous vehicles. A typical motion control problem is trajectory-tracking,which is concerned with the design of control laws that force avehicle to reach and follow a time parameterized reference (i.e.,a geometric path with an associated timing law). The degree ofdifficulty involved in solving this problem is highly dependenton the configuration of the vehicle. For fully actuated systems,the trajectory-tracking problem is now reasonably well understood.Manuscript received June 30, 2005; revised May 8, 2006. Recommended byAssociate Editor A. Garulli. This work was supported in part by the NationalScience Foundation under Grant ECS-0093762, Project MAYA-Sub of the AdI(PT), project GREX/CEC-IST under Contract 035223, of the Commission of theEuropean Communities, and by the FCT-ISR/IST plurianual funding throughthe POS C Program that includes FEDER funds.A. P. Aguiar was with the Center for Control Engineering and Computation,University of California, Santa Barbara, CA 93106 USA. He is now with theInstitute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal(e-mail: pedro@isr.ist.utl.pt).J. P. Hespanha is with the Center for Control Engineering and Computation, University of California, Santa Barbara, CA 93106 USA (e-mail: hespanha@ece.ucsb.edu).Color versions of one or more of the figures in this paper available online athttp://ieeexplore.ieee.org.Digital Object Identifier 10.1109/TAC.2007.902731For underactuated vehicles, i.e., systems with fewer actuators than degrees-of-freedom,1 trajectory-tracking is still an active research topic. The study of these systems is motivated bythe fact that it is usually costly and often not practical to fullyactuate autonomous vehicles due to weight, reliability, complexity, and efficiency considerations. Typical examples of underactuated systems include wheeled robots, hovercraft, spacecraft, aircraft, helicopters, missiles, surface vessels, and underwater vehicles. The tracking problem for underactuated vehicles is especially challenging because most of these systems arenot fully feedback linearizable and exhibit nonholonomic constraints. The reader is refereed to [3] for a survey of these concepts and to [4] for a framework to study the controllability andthe design of motion algorithms for underactuated Lagrangiansystems on Lie groups.The classical approach for trajectory-tracking of underactuated vehicles utilizes local linearization and decoupling of themulti-variable model to steer the same number of degrees offreedom as the number of available control inputs, which canbe done using standard linear (or nonlinear) control methods.Alternative approaches include the linearization of the vehicleerror dynamics around trajectories that lead to a time-invariantlinear system (also known as trimming trajectories) combinedwith gain scheduling and/or linear parameter varying (LPV) design methodologies [5]–[7]. The basic limitation of these approaches is that stability is only guaranteed in a neighborhood ofthe selected operating points. Moreover, performance can suffersignificantly when the vehicle executes maneuvers that emphasize its nonlinearity and cross couplings. A different approachis to use output feedback linearization methods, [8]–[10]. Themajor challenge in this approach is that a straightforward application of this methodology, which in general involves dynamicinversion, is not always possible because certain involutivityconditions must hold [11]. In addition, even when dynamic inversion is possible, the resulting controller may not render thezero-dynamics stable.Nonlinear Lyapunov-based designs can overcome some ofthe limitations mentioned above. Several examples of nonlinear1The following definition of underactuated mechanical systems is adaptedfrom [1], [2]. Consider the affine mechanical system described byq f (q; q ) G(q )u(1)where q is a vector of independent generalized coordinates, f a vector field thatcaptures the dynamics of the system, G the input matrix, and u the vector ofgeneralized inputs. Equation (1) is underactuated if the rank of G is smaller thanthe dimension of q , i.e., the generalized inputs are not able to instantaneouslyset the accelerations in all directions of the configuration space.0018-9286/ 25.00 2007 IEEE

AGUIAR AND HESPANHA: UNDERACTUATED AUTONOMOUS VEHICLEStrajectory-tracking controllers for marine underactuated vehicles have been reported in the literature [12]–[19]. Typically,tracking problems for autonomous vehicles are solved by designing control laws that make the vehicles track pre-specifiedfeasible “state-space” trajectories, i.e., trajectories that specifythe time evolution of the position, orientation, as well as thelinear and angular velocities, all consistent with the vehicles’dynamics, [8], [13], [15]–[20], even through in practical applications one often only needs to track a desired position. Thisapproach suffers from the drawback that usually the vehicles’dynamics exhibit complex nonlinear terms and significant uncertainty, which makes the task of computing a feasible trajectory difficult.It is relevant to point out that most of the results mentionedabove only solve the problem in the horizontal plane. Only a fewauthors have tackled this control problems in 3-D space. Thereason might be that the vehicle’s dynamics become more complex and the number of degree of freedom that are not directlyactuated typically increases, making the control design more involved. For example, for an underactuated underwater vehicle,the dynamics include sway and heave velocities that generatenonzero angles of sideslip and attack.Motivated by these considerations, we propose a solution tothe trajectory-tracking problem for underactuated vehicles inboth 2- and 3-D spaces. In this paper, we are especially interested in situations for which there is parametric uncertainty inthe model of the vehicle. Typical parameters for which this uncertainty is high, include mass and added mass for underwatervehicles which may be subject to large variations accordingto the payload configuration, and friction coefficients that areusually strongly dependent on the environmental conditions.The main contribution of the paper is the design of an adaptive supervisory control algorithm that combines logic-basedswitching [21] with iterative Lyapunov-based techniques suchas integrator backstepping [22]. The classical approach to adaptive control relies solely on continuous tuning [22]–[24]. Thisapproach has some inherent limitations that can be overcome byhybrid adaptive algorithms based on switching and logic [25].The basic idea behind supervisory control [21], [26]–[30] is todesign a suitable family of candidate controllers. Each controlleris designed for an admissible nominal model of the process, anda supervision logic orchestrates the switching among the candidate controllers, deciding, at each instant of time, the candidate feedback controller that is more adequate. In order toguarantee stability and avoid chattering, a form of hysteresisis employed. We prove that the adaptive controller solves theproblem of global boundedness and convergence of the positiontracking error to a neighborhood of the origin that can be madearbitrarily small in the presence of possible large parametric uncertainty. The adaptive supervisory controller does not requirepersistence of excitation which sets it apart from most parameter estimation algorithms. In the control design, we take intoaccount that the vehicle may have non-negligible dynamics andmay undergo complex motions and exhibit large angles of attackand sideslip, which prevents us from using simple extensions ofcommon control designs for wheeled robots where the total velocity vector is aligned with the vehicles main axis. Also, thedesired trajectory does not need to be a trimming trajectory and1363can be any sufficiently smooth time-varying bounded curve, including the degenerate case of a constant trajectory (set-point).The class of vehicles for which the design procedure is applicable is quite general and includes any vehicle modeled asa rigid-body subject to a controlled force and either one controlled torque if it is only moving on a planar surface or twoor three independent control torques for a vehicle moving in3-D space. Furthermore, contrary to most of the approaches described above, the controller proposed does not suffer from geometric singularities due to the parameterization of the vehicle’srotation matrix. This is possible because the attitude control.problem is formulated directly in the group of rotationsThe literature on designing tracking control laws for underactuated vehicles directly in the configuration manifold (avoidingin this way geometric singularities) is relatively scarce. Noteworthy examples include [20] and [31].Another contribution of this paper is the application of theseresults to solve the path-following motion control problem. Inpath-following, the vehicle is required to converge to and followa path that is specified without a temporal law [32]–[36]. Pioneering work in this area for wheeled mobile robots is described in [32]. In [34], Samson addressed the path-followingproblem for a car pulling several trailers. More recently, Altafini[36] describes a path-following controller for a trailer vehicle that provides local asymptotic stability for a path of nonconstant curvature. Path-following controllers for aircraft andmarine vehicles have been reported in [6], [9], and [37]–[39].Using the approach suggested by Hauser and Hindman [37], anoutput maneuvering controller was proposed in [39] for a classof strict feedback nonlinear processes and applied to path-following of fully actuated ships. The underlying assumption inpath-following is that the vehicle’s forward speed tracks a desired speed profile, while the controller acts on the vehicle’sorientation to drive it to the path. Typically, in path-following,smoother convergence to the path is achieved and the controlsignals are less likely pushed into saturation, when compared totrajectory-tracking. In fact, in [40] and [41], we highlight a fundamental difference between path-following and standard trajectory-tracking by demonstrating that performance limitationsdue to unstable zero-dynamics can be removed in the path-following problem. Inspired by these ideas, we solve the path-following problem by decomposing it into two subproblems: i) ageometric task, which consists of converging the vehicle to andremaining inside a tube centered around the desired path, andii) a dynamic assignment task, which assigns a speed profile tothe path.In Section II, we describe the dynamic model for the classof underactuated autonomous vehicles considered in the paperand formulate the trajectory-tracking and path-following controlproblems. As a preliminary material for the subsequent sections,Section III presents a nonlinear control law to solve the trackingproblem and discusses the stability of the resulting closed-loop.At this point it is assumed that there is no parametric uncertainty.Sections IV and V present the main results of the paper. In Section IV, a solution to the trajectory-tracking is proposed usingan estimator-based supervisory controller, and in Section V anextension is made to solve the path-following problem. In Section VI, we illustrate our design methodologies in the context

1364IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 52, NO. 8, AUGUST 2007of two vehicle control applications: a hovercraft (moving ona planar surface) and an underwater vehicle (moving in 3-Dspace). The designs are validated through computer simulations.The paper concludes with a summary of the results and suggestions for further research.A subset of the results reported here were presented in[42]–[44].Notation: Throughout this paper, given a matrix ,de,are the minimum andnotes its transpose,maximum eigenvalues of , respectively. Given two vectors,, we denote bythe vector. The Euclidean norm is denoted byand the spectral norm by. A piecewise continuous func,is in , being a positivetioninteger, iffor some constant .II. PROBLEM STATEMENTConsider an underactuated vehicle modeled as a rigid bodybe an inertial cosubject to external forces and torques. Leta body-fixed coordinate frame whoseordinate frame andorigin is located at the center of mass of the vehicle. The conof the vehicle is an element of the Special Eufiguration, whereclidean groupis a rotation matrixthat describes the orientation of the vehicle by mapping bodyis the positioncoordinates into inertial coordinates, andin. Denoting byandtheof the origin ofexlinear and angular velocities of the vehicle relative topressed in, respectively, the following kinematic relationsapply:(2a)(2b)wherematricesis a function fromto the space of skew-symmetricdefined byWe consider here underactuated vehicles with dynamic equations of motion of the following form:forces and torques acting on the body. For the special case ofand also include the so-called hyan underwater vehicle,and added-inertiamatrices, redrodynamic added-massspectively, i.e.,,, whereandare the rigid-body mass and inertia matrices, respectively.For an underactuated vehicle restricted to moving on a planarsurface, the same equations of motion (2), (3) apply withoutthe first two right-hand side terms in (3b). Also, in this case,,,,,,, with all the other terms in (3) having appropriateis given bydimensions, and the skew-symmetric matrix. For simplicity, in what follows, we restrict our attention to the 3-D case. However, all results are directly applicable to the 2-D case, as will be illustrated in Section VI-A for the control of a Hovercraft.Remark 1: The vehicle dynamic model (3) does not allowto depend on . This was done in part to simplify the analysis and also because in many vehicles this dependance is notpresent as is the case of the Hovercraft and the AUV described inSection VI. The methodology presented here still applies for themore general case if the dependence on is in the form, provided thatis bounded orthat a suitable rank condition holds. For details see Property 1in the Appendix and [42], [44].The problems considered in this paper can be stated as follows:Trajectory-tracking problem: Letbe a given sufficiently smooth time-varying desired trajectory with its time-derivatives bounded. Design a controllersuch that all the closed-loop signals are bounded and theconverges to a neighborhoodtracking errorof the origin that can be made arbitrarily small.be a desiredPath-following problem: Letanda desiredpath parameterized byspeed3 assignment. Suppose also thatis sufficientlysmooth with respect to and its derivatives (with respect to) are bounded. Design feedback control laws for , ,and such that all the closed-loop signals are bounded,the position of the vehicle converges to and remains inside a tube centered around the desired path that can beconverges tomade arbitrarily thin, i.e.,a neighborhood of the origin that can be made arbitrarilysmall, and the vehicle satisfies a desired speed assignmentalong the path, i.e., the speed errorcanbe confined to an arbitrarily small ball.(3a)(3b)whereanddenote constant symmetricandpositive definite mass and inertia matrices;denote the control inputs, which act upon the system through aand a constant nonsingular maconstant nonzero vector, respectively; the termsin (3a) andtrix2in (3b) are the rigid-body Coriolis terms,and thefunctions,represent all the remaining2SeeRemark 4 for the special case of G2.III. TRAJECTORY-TRACKING CONTROLLER DESIGNA. Controller DesignThis section proposes a Lyapunov-based control law to solvethe trajectory-tracking problem assuming that there is no parametric uncertainty. For the sake of clarity, control-Lyapunovfunctions are introduced iteratively borrowing from the techniques of backstepping [22].v3For( )simplicity of presentation it will be assumed that the speed assignmentdoes not depend directly on time t.2

AGUIAR AND HESPANHA: UNDERACTUATED AUTONOMOUS VEHICLES1365Step 1. Coordinate transformation: Consider the global diffeomorphic coordinate transformationThe time derivative ofcan be written as(7)wherewhich expresses the tracking errorin the body-fixedframe. The dynamic equation of the body-fixed trackingerror is given by(8)Step 2. Convergence of : We start by defining the controlLyapunov functionIn Appendix (cf. Property 1), we show that the matrix Bcan always be made full-rank by choosing a suitable .One can now regard as a virtual control (actually its firstcomponent is already a “real” control) that one would likenegative. This could be achieved, byto use to makesetting equal toand computing its time derivative to obtain(4)We can regard as a virtual control that one would usenegative. This could be achieved, by settingto makeequal to, for some positive constant .To accomplish this we introduce the error variablewhereis a symmetric positive definite matrix.To accomplish this we setto be equal to the first entryof , i.e.,(9)and introduce the error variablethat we would like to drive to zero, and re-write (4) as(5)that one would like to set to zero. We can now rewrite (7),given by (9), aswithStep 3. Backstepping for : After straightforward algebraic manipulations, the dynamic equation of the errorcan be written asStep 4. Backstepping for : Consider now a third controlLyapunov function given bywhere(10)(6)Computing its time derivative one obtainsand. It turns out that it will not always be possible to drive to zero. We need to explore the couplingof the translation dynamics with the rotational inputs. Toto a constant design vectorthis effect, we will drive. To achieve this we defineas a newerror variable that we will drive to zero and consider theaugmented control-Lyapunov functionFor simplicity, we did not expand the derivative of . If wethen choose(11)

1366IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 52, NO. 8, AUGUST 2007wherederivative ofis a symmetric positive matrix, the timebecomestroller parameters. To this effect, suppose we pick a desired ra, and we select such that Bdius and a convergence rateis full rank. Such value for may depend on the upper bound of(see Property 1). We can then define,provided that we choose sufficiently large so thatNote that althoughis not necessarily always negative,this will be sufficient to prove boundedness and convergence of to a neighborhood of the origin.B. Stability AnalysisWe can now prove that all signals will remain bounded, andthat the tracking error converges exponential to an arbitrarilysmall neighborhood of the origin.Theorem 1: Given a sufficiently smooth time-varying desiredwith its time-derivatives bounded,trajectoryconsider the nonlinear system described by the underactuatedvehicle model (2), (3) in closed-loop with the feedback controller (9), (11).(i) For every initial condition of , the solution exists globally, all closed-loop signals are bounded, and the trackingsatisfieserror(12)where , , and are positive constants. From these, onlydepends on initial conditions., by appropriate(ii) For a given upper bound onchoice of the controller parameters, anydesired values for and in (12) are possible.Proof: To prove (i) we use Young’s inequality4 to concludethat for any(13)Suppose now that we choose sufficiently small so that theis positive definite. In this case wematrixconclude that there is a sufficiently small positive constantsuch thatIf we then select,, we conclude from (13) that (14) indeed holds for the prespecified ,from which (15) follows. However, now the above choices for.the parameters lead to a radiusRemark 2: We did not impose any constraints on the desiredtrajectory (besides being sufficiently smooth and its derivativebeing bounded) and we also did not require that the linear vecanlocity of the vehicle be always nonnull. Consequently,be arbitrary, that is, the desired trajectories do not need to satisfy “dynamic” models, and in particular can be constant for all. In that case, the controller solves the position regulationproblem.Remark 3: In practice, the vector determines if the vehiclewill follow the desired trajectory backward or forward. To observe this, define the following two angles:and, where , , andare thethree components of the body-fixed tracking error . Notice thatand can be seen as the elevation and azimuth angles, respec,), from (5), it followstively. In steady-state (with. Thus, when the first comthatis negative and larger (in absolute value) thanponent ofthe other two components, the vehicle will converge to the trajectory with positive surge velocity, and will stay “behind” thedesired trajectory, see examples in Section VI.Remark 4: When the vehicle is subject to one controlled forceand only two independent control torques, i.e.,, but(and consequently), one can use, e.g.,, provided that there exists asymmetric positive definite matrix such that(which is the case for the AUV in Section VI). If we then set(14)and, therefore, it is straightforward to conclude from the Comparison Lemma [45] thatthe time derivative ofbecomes(15)along solutions to . From here we conclude that all signals remain bounded and therefore the solution exists globally. Moreconverges to a ball of radiusand thereforeover,converges to a ball of radius, because of (10).can beTo prove (ii), we show next that the radiusmade as small as we want by appropriately choosing the con4A special case of the Young’s inequality is abwhere a; b0, and is any positive constant. ( 2)a (1 2 )b ,where is a disturbance term that depends on the componentin the null space of. From the above, oneof the statecan prove boundedness if this component is bounded. For underwater vehicles, this component typically corresponds to theroll motion which usually is stable due to the restoring forces.IV. ESTIMATOR-BASED SUPERVISORY CONTROLUsing the previous results, this section proposes an estimator-based supervisory control architecture to solve the

AGUIAR AND HESPANHA: UNDERACTUATED AUTONOMOUS VEHICLES1367. We also restrict our attention to state feedback laws and,.therefore,A. Multi-EstimatorThis section addresses the design of a family of estimatorsfor the underactuated vehicle modelparameterized by(2), (3). Motivated by Assumption 1 and in view of (3), we consider a family of estimators of the form5Fig. 1. Supervisory control architecture.(16a)trajectory-tracking problem in the presence of parametric modbe a vector that contains alleling uncertainty. Letthe unknown parameters of the dynamic equations of motiondenotes the number of unknown parameters. The(3), wherefollowing technical assumption is assumed to hold.Assumption 1: Let be a finite set of candidate parametervalues(16b),are diagonal positive definitewherematrices and for eachthe scalar positive funcandtionssatisfy6(17a)The actual parameterbelongs to .In practice, this assumption can be relaxed tobeingsufficiently close to an element of , which can be achievedby taking a fine grid, at the price of increased computationalburden.The supervisory consists of three subsystems (see Fig. 1)[21].multi-estimator—a dynamical system whose inputs are theprocess input and its output , and whose outputs are ,, where eachis a suitably defined estimate ofwhich would be asymptotically correct ifwas equal to.multi-controller—a dynamical system whose inputs are theand the estimation errors,output estimate, and whose outputs are the control signals,, where eachis generated by a control law thatwas equal to .would be adequate ifswitching logic—a dynamical system whose inputs are theand whose output is a switching signalestimation errorswhich is used to define the control law.The underlying decision-making strategy used by theswitching logic basically consists of selecting for , thefor which the correspondingcandidate controller index(which is a suitably “normed” valueperformance signal) is currently the smallest. This strategy is motivatedofby the idea that the nominal process model with the smallestperformance signal is the one that “best” approximates theactual process, and, thus, the candidate controller associatedwith that model can be expected to have a better performanceof controlling the process.In this paper we assume that the whole state of the processis available for feedback. Therefore,, and. Since thereis no uncertainty in (2), we can simply pickand(17b)(17c). The functionsfor some positive constants ,andwill be defined later (cf. (28a), (28b)). Themulti-estimator has the desirable property that the estimatorconerror that corresponds to the actual parameter valueverges exponentially to zero and satisfies a -like property.be the actual parameter value. ThereLemma 1: Let,, such that for every initial condition ofexist(2), (3), (16), and continuous signal, there existpositive constants , , that depend on the initial conditionssuch that(18a)(18b)(18c)5When P has a large number of elements, an alternative approach is describedin Section IV-E.6The existence of(1) and(1) follows directly from the fact thatf (1) and f (1) are C .

1368IEEE TRANSACTIONS ON AUTOMATIC CONTROL, VOL. 52, NO. 8, AUGUST 2007for every time in the maximum interval of existence of solution,.to the closed-loopProof: See the Appendix.whereB. Multi-ControllerWe now design a family of candidate feedback lawssuch that for each,wouldsolve the tracking problem formulated in Section II for a processmodel given by (2) and (16), and “sufficiently” small estima. For a given, we designby contion errors ,structing control-Lyapunov functions iteratively, following thedesign procedure proposed in Section III.Step 1 and 2: Same as in Section III. However, in this caseis redefined as(19)and, therefore(20)Following the same line of reason described in Step 3 ofSection III, letbe a virtual control law for eachis nonsingular. Letsuch that, i.e.entry of(21), where is chosenbe equal to the first(22)Step 3: The dynamic equation of the errorbyis now givenand(23)ThenwhereStep 4: The third control-Lyapunov function is now givenby(24)Thus,Computing its time derivative one obtainsis redefined aswherewritten as. The time derivative ofcan bewherecan be decomposed in two terms:. Here,, andis defined to be the

AGUIAR AND HESPANHA: UNDERACTUATED AUTONOMOUS VEHICLESsame as , but substituting the arguments ,respectively. Selectingby,1369,(25),where for eachpositive matrix, the time derivative ofis a symmetricbecomes(26)Given a sufficiently smooth time-varying desired trajectorywith its time-derivatives bounded and any initial,condition of the resulting closed-loop system, the signals,, andare bounded on. Moreover, if, then, as, the tracking error(29) holds withconverges to a neighborhood of the origin that canbe made arbitrarily small by appropriate choice of the controllerparameters.Proof: See the Appendix.Loosely speaking, Lemma 2 states that each candidate controller solves the trajectory-tracking problem formulated in Section II provided that the input disturbances due to the estimationerrors have finite energy as defined by the integral (29). Theswitching-logic will guarantee that (29) holds by the Scale-Independent Hysteresis Switching Lemma [21] (cf. proof of Theorem 2).whereC. Switching-LogicMotivated by (29), (30), for each, we start by definingas the state of the dynamic equationthe performance signal(31)(27)The last term, wherecan be rewritten as(28a)(28b)has indefinite terms, it will be verFrom (26), althoughified that they will be dominated by the negative definite,are sufficientlyterms when the estimator errorssmall. This is stated in the following lemma.,denote the maximum inLemma 2: Letterval of existence of solution to the closed-loop and supposesuch thatfor allthat there exists a timeand(29)where the control law. Equation (31)with the initial values satisfyingis the sum of an expoimplies that each performance signalnentially decaying term that depends on initial conditions anda suitable exponentially weighted “norm” of the correspondingacts as a forgettingestimation errors. The control parameterfactor in the evaluation of the performance signals, henceestablishing a compromise between adaptation alertness andswitching dither.The switching logic consider here is the scale-independenthysteresis switching logic proposed in [21]. Let be a positive constant called the hysteresis constant. The operation of theswitching logic can be briefly explained as follows: First, we

conditions must hold [11]. In addition, even when dynamic in-version is possible, the resulting controller may not render the zero-dynamics stable. Nonlinear Lyapunov-based designs can overcome some of the limitations mentioned above. Several examples of nonlinear 1The following definition of underactuated mechanical systems is adapted from [1 .

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, VOL. 33, NO. 9, SEPTEMBER 2014 1277 Placement for Binary-Weighted Capacitive Array in SAR ADC Using Multiple Weighting Methods Yongfu Li, Student Member, IEEE, Zhe Zhang, Student Member, IEEE, Dingjuan Chua, Student Member, IEEE, and Yong Lian, Fellow, IEEE Abstract—The overall accuracy and linearity of a matching .

112 IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, VOL. 15, NO. 1, MARCH 2018 Efficient Deep Neural Network Serving: Fast and Furious Feng Yan , Member, IEEE, Yuxiong He, Member, IEEE, Olatunji Ruwase, Member, IEEE, and Evgenia Smirni, Senior Member, IEEE Abstract—The emergence of deep neural networks (DNNs) as a state-of-the-art machine learning technique has enabled a vari-

2022 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 19, NO. 3, MARCH 2020 Federated Learning via Over-the-Air Computation Kai Yang, Student Member, IEEE, Tao Jiang , Student Member, IEEE, Yuanming Shi , Member, IEEE,andZhiDing, Fellow, IEEE Abstract—The stringent requirements for low-latency and privacy of the

3150 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 54, NO. 7, JULY 2008 Capacity of the Trapdoor Channel With Feedback Haim Permuter, Student Member, IEEE, Paul Cuff, Student Member, IEEE, Benjamin Van Roy, Member, IEEE, and Tsachy Weissman, Senior Member, IEEE Abstract—We establish that the feedback capacity of the trap