Publications
685 results found
Ioannides G, Kourouklides I, Astolfi A, 2022, Spatiotemporal dynamics in spiking recurrent neural networks using modified-full-FORCE on EEG signals, Scientific Reports, Vol: 12, ISSN: 2045-2322
Methods on modelling the human brain as a Complex System have increased remarkably in the literature as researchers seek to understand the underlying foundations behind cognition, behaviour, and perception. Computational methods, especially Graph Theory-based methods, have recently contributed significantly in understanding the wiring connectivity of the brain, modelling it as a set of nodes connected by edges. Therefore, the brain's spatiotemporal dynamics can be holistically studied by considering a network, which consists of many neurons, represented by nodes. Various models have been proposed for modelling such neurons. A recently proposed method in training such networks, called full-Force, produces networks that perform tasks with fewer neurons and greater noise robustness than previous least-squares approaches (i.e. FORCE method). In this paper, the first direct applicability of a variant of the full-Force method to biologically-motivated Spiking RNNs (SRNNs) is demonstrated. The SRNN is a graph consisting of modules. Each module is modelled as a Small-World Network (SWN), which is a specific type of a biologically-plausible graph. So, the first direct applicability of a variant of the full-Force method to modular SWNs is demonstrated, evaluated through regression and information theoretic metrics. For the first time, the aforementioned method is applied to spiking neuron models and trained on various real-life Electroencephalography (EEG) signals. To the best of the authors' knowledge, all the contributions of this paper are novel. Results show that trained SRNNs match EEG signals almost perfectly, while network dynamics can mimic the target dynamics. This demonstrates that the holistic setup of the network model and the neuron model which are both more biologically plausible than previous work, can be tuned into real biological signal dynamics.
Sassano M, Mylvaganam T, Astolfi A, 2022, Infinite-horizon optimal control problems for nonlinear systems, IEEE Conference on Decision and Control (CDC 2021), Publisher: IEEE, Pages: 1721-1721
Infinite-horizon optimal control problems for non-linear systems are studied and discussed. First, we thoroughlyrevisit the formulation of the underlying dynamic optimisation problem together with the classical results providing itssolution. Then, we consider two alternative methods to con-struct solutions (or approximations there of) of such problems, developed in recent years, that provide theoretical insights as well as computational benefits. While the considered methods are mostly based on tools borrowed from the theories of Dynamic Programming and Pontryagin’s Minimum Principles, or a combination of the two, the proposed control design strategies yield innovative, systematic and constructive methods to provide exact or approximate solutions of nonlinear optimal control problems. Interestingly, similar ideas can be extended also to linear and nonlinear differential games, namely dynamic optimisation problems involving several decision-makers. Due their advantages in terms of computational complexity, the considered methods have found several applications. An example ofthis is provided, through the consideration of the multi-agent collision avoidance problem, for which both simulations and experimental results are provided.
Franco E, Garriga Casanovas A, Tang J, et al., 2022, Adaptive energy shaping control of a class of nonlinear soft continuum manipulators, IEEE-ASME Transactions on Mechatronics, Vol: 27, Pages: 280-291, ISSN: 1083-4435
Soft continuum manipulators are characterized by low stiffness which allows safe operation in unstructured environments but introduces under-actuation. In addition, soft materials such as silicone rubber, which are commonly used for soft manipulators, are characterized by nonlinear stiffness, while pneumatic actuation can result in nonlinear damping. Consequently, achieving accurate control of these systems in the presence of disturbances is a challenging task. This paper investigates the model-based adaptive control for soft continuum manipulators that have nonlinear uniform stiffness and nonlinear damping, that bend under the effect of internal pressure, and that are subject to time-varying disturbances. A rigid-link model with virtual elastic joints is employed for control purposes within the port-Hamiltonian framework. The effects of disturbances and of model uncertainties are estimated adaptively. A nonlinear controller that regulates the tip orientation of the manipulator and that compensates the effects of disturbances and of model uncertainties is then constructed by using an energy shaping passivity-based approach. Stability conditions are discussed highlighting the beneficial role of nonlinear damping. The effectiveness of the controller is assessed with simulations and with experiments on a soft continuum manipulator prototype.
Mylvaganam T, Sassano M, Astolfi A, 2022, Nonlinear optimal control of a ballast-stabilized floating wind turbine viaexternally stabilised Hamiltonian dynamics, IEEE Conference on Decision and Control, Publisher: IEEE, Pages: 2428-2433
We consider the problem of controlling a ballast-stabilized offshore wind turbine. We formulate an optimal control problem with the objective of maximising the power generation while minimising structural fatigue of the wind turbine. Due to the nonlinear nature of the model, obtaining a solution to the above control task poses a severe challenge.Recalling that solutions of the optimal control problem are characterised by a certain (unstable) invariant manifold of the underlying Hamiltonian system, we demonstrate that nonlinear control strategies which approximate the solution of the optimalcontrol problem can be constructed through the introduction of an externally stabilised Hamiltonian system. This observation enables the construction of an algorithm to compute (with rel-atively low computational complexity) an approximate solution of the optimal control problem, without ignoring nonlinearities in the control design. This approach has several benefits, asdemonstrated via simulations on a ballast-stabilized offshore wind turbine.
Jiang ZP, Prieur C, Astolfi A, 2022, Preface, Lecture Notes in Control and Information Sciences, Vol: 488, Pages: vii-ix, ISSN: 0170-8643
Chen K, Astolfi A, 2022, Adaptive Control for Systems with Time-Varying Parameters—A Survey, Lecture Notes in Control and Information Sciences, Pages: 217-247
Adaptive control was originally proposed to control systems, the model of which changes over time. However, traditionally, classical adaptive control has been developed for systems with constant parameters. This chapter surveys the so-called congelation of variables method to overcome the obstacle of time-varying parameters. Two examples, illustrating how to deal with time-varying parameters in the feedback path and in the input path, respectively, are first presented. Then n-dimensional lower triangular systems to show how to combine the congelation of variables method with adaptive backstepping are discussed. Finally, we study how to control a class of nonlinear systems via output feedback: this is a problem that cannot be solved directly due to the coupling between the input and the time-varying perturbation. It turns out that if we assume a strong minimum-phase property, namely, ISS of the inverse dynamics, such a coupling is converted into a coupling between the output and the time-varying perturbation. Then, a small-gain-like analysis, which takes all subsystems into account, yields a controller that achieves output regulation and boundedness of all closed-loop signals. Simulation results to demonstrate that the proposed controller achieves asymptotic output regulation and outperforms the classical adaptive controller in the presence of time-varying parameters are presented.
Giannari AG, Astolfi A, 2022, Model of lateral inhibition using a network of heterogeneous Hodgkin-Huxley neurons, European Control Conference (ECC), Publisher: IEEE, Pages: 272-277
Dastjerdi AA, Astolfi A, Saikumar N, et al., 2022, Closed-Loop Frequency Analysis of Reset Control Systems, IEEE Transactions on Automatic Control, Pages: 1-8, ISSN: 0018-9286
Chen K, Astolfi A, Parisini T, 2022, Decentralized Adaptive Control for Interconnected Cyber-Physical Systems under Coordinated Attacks, IEEE 61st Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 703-708, ISSN: 0743-1546
Tarantino L, Sassano M, Galeani S, et al., 2022, Constant costate iterations for finite-horizon optimal control with nonlinear dynamics, IEEE 61st Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 3481-3486, ISSN: 0743-1546
Simard JD, Astolfi A, 2022, Regularization of Underconstrained Interpolants in the Loewner Framework, European Control Conference (ECC), Publisher: IEEE, Pages: 1684-1689
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Li A, Astolfi A, Liu M, 2022, On the Robustness of a Class of Saturated Controllers in the Presence of Large Disturbances with Bounded Moving Average, European Control Conference (ECC), Publisher: IEEE, Pages: 1654-1659
Simard JD, Astolfi A, 2022, Loewner Functions for a Class of Nonlinear Differential-Algebraic Systems, IEEE 61st Conference on Decision and Control (CDC), Publisher: IEEE, Pages: 6542-6547, ISSN: 0743-1546
Simard JD, Astolfi A, 2021, Nonlinear model reduction in the loewner framework, IEEE Transactions on Automatic Control, Vol: 66, Pages: 5711-5726, ISSN: 0018-9286
We introduce a novel method of model reduction for nonlinear systems by extending the Loewner framework developed for linear time-invariant systems. This objective is achieved by defining Loewner functions obtained by utilizing a state-space interpretation of the Loewner matrices. A Loewner equivalent model using these functions is derived. This allows constructing reduced order models achieving interpolation in the Loewner sense.
Gao J, Chaudhuri B, Astolfi A, 2021, A direct bounded control method for transient stability assessment, 7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control (LHMNC), Publisher: Elsevier, Pages: 294-301, ISSN: 2405-8963
The paper proposes a direct method for transient stability assessment, which is more efficient than traditional time-domain simulation methods. To achieve this objective, a new bounded control law for the model of a power system is designed. This yields superior closed-loop transient performances when compared to those achievable with traditional automatic voltage regulators and power system stabilizers. The designed control law allows defining an energy-based Lyapunov function which is instrumental in assessing transient stability properties of the post-fault system. A case study on a single machine infinite bus power system model is presented to illustrate the merits of the proposed method.
Jiang J, Astolfi A, 2021, Stabilization of a class of underactuated nonlinear systems via underactuated back-stepping, IEEE Transactions on Automatic Control, Vol: 66, Pages: 5429-5435, ISSN: 0018-9286
The stabilization problem for a class of nonlinear systems is solved via a novel method inspired by back-stepping. The method, that we call underactuated back-stepping, is introduced by solving the stabilization problem for an inertia wheel pendulum and it is then developed for a class of underactuated mechanical systems. The properties of the resulting closed-loop systems are studied in detail and case studies are given to show the effectiveness of the proposed method.
Faedo N, Scarciotti G, Astolfi A, et al., 2021, On the approximation of moments for nonlinear systems, IEEE Transactions on Automatic Control, Vol: 66, Pages: 5538-5545, ISSN: 0018-9286
Model reduction by moment-matching relies upon the availability of the so-called moment. If the system is nonlinear, the computation of moments depends on an underlying specific invariance equation, which can be difficult or impossible to solve. This note presents four technical contributions related to the theory of moment matching: first, we identify a connection between moment-based theory and weighted residual methods. Second, we exploit this relation to provide an approximation technique for the computation of nonlinear moments. Third, we extend the definition of nonlinear moment to the case in which the generator is described in explicit form. Finally, we provide an approximation technique to compute the moments in this scenario. The results are illustrated by means of two examples.
Machado JE, Ortega R, Astolfi A, et al., 2021, An adaptive observer-based controller design for active damping of a DC network with a constant power load, IEEE Transactions on Control Systems Technology, Vol: 29, Pages: 2312-2324, ISSN: 1063-6536
This article explores a nonlinear, adaptive controller aimed at increasing the stability margin of a direct-current (dc), small-scale, electrical network containing an unknown constant power load (CPL). Due to its negative incremental impedance, this load reduces the effective damping of the network, which may lead to voltage oscillations and even to voltage collapse. To overcome this drawback, we consider the incorporation of a controlled dc-dc power converter in parallel with the CPL. The design of the control law for the converter is particularly challenging due to the existence of unmeasured states and unknown parameters. We propose a standard input-output linearization stage, to which a suitably tailored adaptive observer is added. The good performance of the controller is validated through experiments on a small-scale network.
Faedo N, Scarciotti G, Astolfi A, et al., 2021, Nonlinear energy-maximising optimal control of wave energy systems: A moment-based approach, IEEE Transactions on Control Systems Technology, Vol: 29, Pages: 2533-2547, ISSN: 1063-6536
Linear dynamics are virtually always assumed when designing optimal controllers for wave energy converters (WECs), motivated by both their simplicity and computational convenience. Nevertheless, unlike traditional tracking control applications, the assumptions under which the linearization of WEC models is performed are challenged by the energy-maximizing controller itself, which intrinsically enhances device motion to maximize power extraction from incoming ocean waves. \GSIn this article, we present a moment-based energy-maximizing control strategy for WECs subject to nonlinear dynamics. We develop a framework under which the objective function (and system variables) can be mapped to a finite-dimensional tractable nonlinear program, which can be efficiently solved using state-of-the-art nonlinear programming solvers. Moreover, we show that the objective function belongs to a class of generalized convex functions when mapped to the moment domain, guaranteeing the existence of a global energy-maximizing solution and giving explicit conditions for when a local solution is, effectively, a global maximizer. The performance of the strategy is demonstrated through a case study, where we consider (state and input-constrained) energy maximization for a state-of-the-art CorPower-like WEC, subject to different hydrodynamic nonlinearities.
Astolfi A, Sassano M, 2021, Constructive design of open-loop Nash equilibrium strategies that admit a feedback synthesis in LQ games, Automatica, Vol: 133, Pages: 1-12, ISSN: 0005-1098
Open-loop Nash equilibrium strategies that admit a feedback synthesis in Linear-Quadratic (LQ) games are studied. Acharacterization alternative to the classic system of coupled (asymmetric) Riccati equations - one for each player - is providedby relying on a fixed-point argument based on the composition of flows of the underlying state/costate dynamics. As a result,it is shown that in competitive games, namely games in which the players influence the shared state via linearly independentinput channels, the characterization of Nash equilibrium strategies hinges upon the solution to a single (regardless of thenumber of players), sign-definite Riccati equation, with coefficients described by polynomial functions of the feedback gains.The structure of the latter equation is computationally appealing since it naturally allows for gradient-descent algorithms onmatrix manifolds, thus ensuring (local) guaranteed convergence to the equilibrium strategy. In the case of antagonistic games,namely games in which the players may share linearly dependent input directions, the fixed-point condition above is combinedwith a geometric requirement involving the largest invariant subspace contained in the kernel of an auxiliary output matrix.Finally, by building on the latter characterization it is shown that closed-form expressions for the equilibrium strategy for aclass of dynamic games can be given
Chen Y-Y, Chen K, Astolfi A, 2021, Adaptive formation tracking control of directed networked vehicles in a time-varying flowfield, Journal of Guidance, Control, and Dynamics: devoted to the technology of dynamics and control, Vol: 44, Pages: 1-9, ISSN: 0731-5090
Sassano M, Mylvaganam T, Astolfi A, 2021, Optimal control for nonlinear systems driven by a known exogenous signal, IEEE Transactions on Automatic Control, Vol: 67, Pages: 3678-3684, ISSN: 0018-9286
We consider optimal control problems forcontinuous-time systems with time-dependent dynamics,in which the time-dependence arises from the presence of aknown exogenous signal. The problem has been elegantlysolved in the case of linear input-affine systems, for whichit has been shown that the solution has a remarkablestructure: it is given by the sum of two contributions; a statefeedback, which coincides with the unperturbed optimalcontrol law, and a purely feedforward term in charge ofcompensating the effect of the exogenous signal. The objective of this note is to extend the above result to nonlinearinput-affine systems. It is shown that, while some of therelevant features of the linear case indeed rely heavily onlinearity and are not preserved in the nonlinear setting, several structural claims can be proved also in the nonlinearcase.
Faedo N, Scarciotti G, Astolfi A, et al., 2021, Energy-maximising moment-based constrained optimal control of ocean wave energy farms, IET Renewable Power Generation
Sassano M, Astolfi A, 2021, A fixed-point characterization of the optimal costate in finite-horizon optimal control problems, IEEE Transactions on Automatic Control, Vol: 66, Pages: 3562-3574, ISSN: 0018-9286
A fixed-point characterization of the optimal costate in finite-horizon optimal control problems for nonlinear systems is presented. It is shown that the optimal initial condition of the costate variable must be a fixedpoint, for any time, of the composition of the forward and backward flows of the underlying Hamiltonian dynamics. Such an abstract property is then translated into a constructive condition by relying on a sequence of repeated Lie brackets involving the Hamiltonian dynamics and evaluated at a single point in the state-space. This leads to a system of algebraic equations in the unknown initial optimal costate that allows achieving a desired degree of accuracy of the approximation while always consisting of a number of equations equal to the dimension of the state of the underlying system, regardless of the achieved accuracy. A dual characterization of the optimal terminal value of the state is also discussed, together with a few computational aspects of the proposed strategy.
Jiang J, Astolfi A, Parisini T, 2021, Robust traffic wave damping via shared control, Transportation Research Part C: Emerging Technologies, Vol: 128, Pages: 1-23, ISSN: 0968-090X
The traffic wave damping problem in a circular single lane track is addressed and solved via a shared control technique which takes a model of the human drivers’ driving habits into consideration. A formal analysis shows that the effectiveness of the proposed shared controller does not depend on the parameters of the human driver’s model, which is an important property in the implementation of the shared controller, since these parameters are difficult to measure, and vary from one human driver to another and from one driving situation to another. In addition, the proposed control law is robust: the stop-and-go wave can be dampened and there is no collisions among vehicles even if there is noise on the information each vehicle receives from the higher level traffic control center. A comparison between performances of the vehicles with and without the proposed control scheme demonstrates the robustness and the effectiveness of the shared control solution.
Franco E, Garriga Casanovas A, Tang J, et al., 2021, Position regulation in Cartesian space of a class of inextensible soft continuum manipulators with pneumatic actuation, Mechatronics, Vol: 76, Pages: 1-21, ISSN: 0957-4158
This work investigates the position regulation in Cartesian space of a class of inextensible soft continuum manipulators with pneumatic actuation subject to model uncertainties and to unknown external disturbances that act on the tip. Soft continuum manipulators are characterised by high structural compliance which results in a large number of degrees-of-freedom, only a subset of which can be actuated independently or instrumented with sensors. External disturbances, which are common in many applications, result in uncertain dynamics and in uncertain kinematics thus making the control problem particularly challenging. We have investigated the use of integral action to model the uncertain kinematics of the manipulators, and we have designed a new control law to achieve position regulation in Cartesian space by employing a port-Hamiltonian formulation and a passivity-based approach. In addition, we have compared two adaptive laws that compensate the effects of the external disturbances on the system dynamics. Local stability conditions are discussed with a Lyapunov approach and are related to the controller parameters. The performance of the controller is demonstrated by means of simulations and experiments with two different prototypes.
Chen K, Astolfi A, 2021, Adaptive control for systems with time-varying parameters, IEEE Transactions on Automatic Control, Vol: 66, Pages: 1986-2001, ISSN: 0018-9286
This article investigates the adaptive control problem for systems with time-varying parameters using the so-called congelation of variables method. First, two scalar examples to illustrate how to deal with time-varying parameters in the feedback path and in the input path, respectively, are discussed. The control problem for an n -dimensional lower triangular system via state feedback is then discussed to show how to combine the congelation of variables method with adaptive backstepping techniques. To achieve output regulation problem via output feedback, problem which cannot be solved directly due to the coupling between the input and the time-varying perturbation, the ISS of the inverse dynamics, referred to as strong minimum-phaseness, is exploited. This allows converting such coupling into the coupling between the output and the time-varying perturbation. A set of filters, resulting in ISS state estimation error dynamics, are designed to cope with the unmeasured state variables. Finally, a controller is designed based on a small-gain-like analysis that takes all subsystems into account. Simulation results show that the proposed controller achieves asymptotic output regulation and outperforms the classical adaptive controller, in the presence of time-varying parameters that are neither known nor asymptotically constant.
Chen Y-Y, Chen K, Astolfi A, 2021, Adaptive formation tracking control for first-order agents in a time-varying flowfield, IEEE Transactions on Automatic Control, Vol: 67, Pages: 1-1, ISSN: 0018-9286
A novel adaptive method to achieve both path following and formation moving along desired orbits in the presence of a spatio-temporal flowfield is presented. The flowfield is a spatio-temporal general flow with unknown time-varying parameters. The so-called \emph{congelation of variables} method is used to estimate the time-varying flow parameters, which do not have any restrictions on the rate of their variation. The asymptotic properties of the resulting adaptive system are studied in detail. Simulation results demonstrate the effectiveness of the proposed method.
Franco E, Tang J, Garriga Casanovas A, et al., 2021, Position control of soft manipulators with dynamic and kinematic uncertainties, 21st IFAC World Congress, Publisher: Elsevier, Pages: 9847-9852, ISSN: 2405-8963
This work investigates the position control problem for a soft continuum manipulator in Cartesian space intended for minimally invasive surgery. Soft continuum manipulators have a large number of degrees-of-freedom and are particularly susceptible to external forces because of their compliance. This, in conjunction with the limited number of sensors typically available, results in uncertain kinematics, which further complicates the control problem. We have designed a partial state feedback that compensates the effects of external forces employing a rigid-link model and a port-Hamiltonian approach and we have investigated in detail the use of integral action to achieve position regulation in Cartesian space. Local stability conditions are discussed with a Lyapunov approach. The performance of the controller is compared with that achieved with a radial-basis-functions neural network by means of simulations and experiments on two prototypes.
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