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In this note, a conditioned transfer technique for switching control is introduced. A controller state-reinitialization procedure is described which minimizes a performance-oriented quadratic cost. With the optimization routine performed off-line, the resulting multi-controller realization has a low computational load. The proposed technique allows the designer to trade off performance versus memory, even when the process to be controlled is described via a continuous distribution of models.

This paper addresses the problem of controlling an uncertain time-varying plant by means of a finite family of candidate controllers supervised by an appropriate switching logic. It is assumed that, at every time, the plant consists of an uncertain single-input/single output linear system. It is shown that stability of the switched closed-loop system can be ensured provided that 1) at every time there is at least one candidate controller capable of potentially stabilizing the current time-invariant “frozen” plant model, and 2) the plant changes are infrequent or satisfy a slow drift condition.

This paper addresses the problem of controlling an uncertain multi-input multi-output system by means of adaptive switching control schemes. In particular, the paper aims at extending the multi-model unfalsified control approach, so far restricted to single-input single-output systems, to a general multivariable setting. The proposed scheme relies on a data-driven ‘high-level’ unit, called the supervisor, which at any time can switch on in feedback with the uncertain plant one controller from a finite family of candidate controllers. The supervisor performs routing and scheduling tasks by monitoring suitable test functionals which, on the basis of the measured data, provide a measure of mismatch between the potential loop made up by the uncertain plant in feedback with the candidate controller and the nominal ‘reference loop’ related to the same candidate controller.

The paper studies how to infer behavioral features of a potential loop, consisting of an uncertain plant interconnected in feedback with a candidate controller, on the grounds of experimental data taken from the same plant possibly driven by a different controller. In such a context, convenient tools to work with are test functionals, computed via a virtual experiment, which quantify the discrepancy between the potential loop and the so-called ‘tuned-loop’ or ‘reference-loop’ related to the same candidate controller. Several test functionals are considered and analyzed so as to unveil conditions under which their adoption can accomplish the desired goals. These results are shown to be of practical relevance for on-line performance inference of feedback control systems and implementation of highly performing adaptive switching control systems.

This paper addresses the problem of controlling uncertain plants by means of a finite family of candidate controllers supervised by an appropriate switching logic. To this end, a novel adaptive control scheme is introduced, whereby switching among pre-designed candidate controllers is suitably combined with an iterative control design procedure. These results are shown to be of practical relevance for on-line implementation of highly performing adaptive switching control schemes. Simulation results are presented.

This paper addresses the problem of controlling an uncertain time-varying plant by means of a finite family of linear candidate controllers supervised by an appropriate switching logic. It is shown that global stability of the closed-loop system can be guaranteed provided that (i) at every time there is at least one candidate controller that would stabilize the current time-invariant “frozen” plant model, and (ii) the changes in the plant model are infrequent.

One of the key components of an Adaptive Optics system is the deformable mirror. This mirror can correct the atmospheric turbulence effects by changing its shape. In the last years Adaptive Secondary Mirrors (ASM) have been developed and the Large Binocular Telescope (LBT) will be soon equipped with two ASMs. Each LBT ASM unit has 672 voice-coil force actuators to change the shape of the mirror shell. Because the actuators apply force, an internal position control is required. The LBT ASM internal control uses a local feedback of position and velocity, then the control law for each actuator has two characteristic parameters which define the closed-loop shell dynamics. In this paper, an analysis of the dynamical behaviour of the ASM under the proposed control law is provided. Then an algorithm is suggested that, based on a simplified model of the shell dynamics, tunes the controller parameters by means of an automatic procedure, as a replacement for a manual procedure based on the operator skill.

In recent years, unfalsified adaptive switching control has emerged as a promising technique for the control of uncertain plants only on the basis of plant I/O data. This note analyzes the input-output stability of the resulting switched system in a noisy environment, and discusses the issue of equivalence among different input-output stability notions.

The paper studies how on-line inferring stability of a potential control-loop consisting of an uncertain plant interconnected in feedback with a candidate controller using plant I/O pairs recorded while the plant is possibly driven by a different controller. In such a context, a convenient tool to work with is to resort to the conceptual entity of a virtual reference (VR). The adopted approach consists of embedding, in the so-called unfalsified adaptive switching control schemes based on VR, a family of nominal models pairwise associated with the given candidate controllers. The result is that the supervised switching mechanism can moderate the chance that destabilizing controllers be switched-on and, hence, reduce both the magnitude and time durations of ''learning'' transients after start-up, while, in contrast with pre-existing multi-model based methods, stability in-the-large is guaranteed under the minimal conceivable assumption that a stabilizing candidate controller exist.

A novel fault detection and isolation (FDI) filter design method is proposed for linear parameter varying (LPV) systems. The LPV system description can be used to approximate the behavior of nonlinear systems and leads to simple nonlinear FDI designs. The main goal here is to obtain residual generator (RG) filters with enhanced fault transmission dc-gains and large ℋ∞ nuisance attenuation. This is achieved using bilinear matrix inequality techniques by exploiting the relevant geometrical properties of the affine LPV description. Finally, it is shown by a nonlinear example that the RG filters designed by the proposed method compare well with alternative approaches including direct nonlinear design methods. Copyright © 2008 John Wiley & Sons, Ltd.

This paper deals with the design of a residual generator (RG) for linear time-invariant systems subject to simultaneous different faults, disturbances and measurement noises. The objective is to design an RG filter that maximizes the transmission from a potential fault to a related residual, while minimizing the ones from nuisances (disturbances, measurement noises and other faults). The isolation of each fault is carried out by designing a bank of RG filters, each one insensitive, as much as possible, to nuisances and capable of detecting the occurrence of its related fault. The design is carried out through ℋ∞ filtering techniques under an eigenstructure assignment constraint. Under mild assumptions, the RG filter can be obtained by solving a λ-parameterized linear matrix inequality optimization problem. A comparison with existing fault detection and isolation (FDI) methods is considered in order to exhibit the relative merits of the proposed method. Copyright © 2008 John Wiley & Sons, Ltd.

This paper proves the validity of a basic feasibility property which makes it possible to extend, to the case of set-point tracking in ANCBI systems under joint positional and incremental input saturations, the approach in [Mosca, E. (2005). Predictive switching supervisory control of persistently disturbed input-saturated plants. Automatica, 41, 55-67] restricted to positional input saturation and the pure regulation problem. An example on the use of the proposed technique is considered.

This paper addresses virtual reference adaptive switching control whereby a data-driven supervisor aims at stabilizing an unknown time-invariant dynamic system by switching at any time in feedback with system one element from a finite family of candidate controllers. Under the only assumption of problem feasibility, viz. the controller family contains a stabilizing controller, the resulting switched system is shown to be stable against arbitrary exogenous persistent bounded disturbances.

This paper extends of the horizon-switching predictive control approach, so far restricted to positional input saturation and the pure regulation problem, to the case of set-point tracking. It is proved a basic feasibility property which makes it possible to extend this approach so as to achieve offset-free asymptotic tracking under joint positional and incremental input saturations and constant disturbances. It is also addressed the same problem in the presence of time-varying disturbances. In such a case it is proved that, whenever the system is subject only to incremental input saturations, the goal is achieved with the property of bounded-noise bounded-state l ∞ - stability.

Control of nonlinear noisy systems affected by large uncertainties is approached via the introduction of a supervisor which, whenever needed, switches on, in feedback to the plant, a controller selected from a finite set of precomputed controllers. A Lyapunov-based falsification criterion allows one to ensure robust stability in the presence of uncertain constant parameters and exogenous bounded disturbances without a priori information on the system. Applicability of the method is illustrated through simulations on an automatic drug delivery system for anesthesia.

A predictive switching logic scheme is considered whereby a feedback-gain is switched-on at any time from a family of candidate feedback-gains so as to control a discrete-time LTI system possibly subject to joint positional and incremental input saturations. An example on the use of the technique in a hard disk drive control context is considered.

In this paper, we present a predictive strategy for teleoperating through communication channels with unknown and possibly unbounded time delay, a remotely located precompensated plant subject to input and state-related pointwise-in-time constraints. The significance of the method is that it is capable of generating command signals such that, once received and applied to the remote plant, stability is preserved and no constraints violation occurs regardless of any possible time delay. The method can be further specialized to deal either with bounded time delay, as typically encountered in space and underwater applications, or with random and possibly unbounded roundtrip time delay, as in the Internet case. In order to demonstrate its effectiveness, a master/slave teleoperation problem over the Internet is considered. It consists of stabilizing via teleoperation a cart/rod inverted pendulum under cart motor voltage and rod angle constraints, through a communication link affected by time delay.

Predictive switching logic schemes are considered whereby a feedback-gain is switched-on at any time from a family of candidate feedback-gains so as to control a discrete-time input-saturated LTI system possibly subject to persistent bounded disturbances of unknown arbitrary magnitude. It is constructively shown that such schemes do exist which ensure, along with good tracking performance, global asymptotic and semi-global exponential stability in the noiseless case, as well as finite l"~-induced gain to the disturbance-to-state map, whenever the structure of the disturbed plant can make such properties conceptually achievable, viz., the disturbance which enters an Asymptotically Null-Controllable with Bounded Input (ANCBI) system acts directly only on the stable modes, while the critically unstable ones are indirectly affected by the disturbance only via the feedback controls. More generally, in ANCBI systems general disturbances of suitably bounded magnitude can also be handled by the scheme, provided that the switching logic be equipped with an appropriate hysteresis facility.

The problem of controlling nonlinear noisy systems affected by large possibly non-parametric uncertainties is approached via the introduction of a supervisor which, whenever needed, switches on, in feedback to the plant, a controller selected from a finite set of predesigned controllers. An application to automatic drug delivery for anesthesia is presented to illustrate the method.

The fault sensitivity of residual generator filters to multiple simultaneous faults is optimized for linear parameter varying (LPV) systems. The filters are synthesized by robust filtering design techniques under a geometric eigenvector assignment constraint. The goal is to design a residual generator providing both high disturbance attenuation and enhanced fault transmission level.

This paper provides a solution to a class of satellites' formations reconfiguration and keeping problems under input-saturation and formation accuracy constraints and subject to persistent disturbances. Both relative position and attitude dynamics have been considered. The proposed control scheme aims at producing, provided that the initial conditions are admissible, system evolutions that fulfill the coordination constraints at any time and satisfy desirable control performance. This is accomplished by resorting to the Command Governor approach. An example is provided for showing the effectiveness of the technique.

The purpose of this paper is to exhibit the relevance of command governor (CG) strategies in real control problems, where consideration has to be given to nonlinearities, model uncertainties, disturbances, input and state-related constraints and requirements of moderate computational complexity. The plant considered is a laboratory cart/rod inverted pendulum for which the control problem consists of regulating vertically its rod subject to motor voltage and rod angle constraints, while providing full-range optimized tracking performance for the cart position. The merits of the proposed CG technique in this application are investigated, and its advantages over classic linear time-invariant design methods are discussed.

The paper addresses two of the basic issues of switching supervisory control (SSC): controller falsification (CF) and inference of candidate loop behaviour (ICLB). CF is approached as a statistical fault detection problem in that the currently operating controller is falsified as soon as a divergence trend is detected. This is achieved by considering a statistic (or residual) in the form of a ratio of closed-loop variables, and the falsification test is carried out by comparing at each time the ratio statistic with a threshold. It is constructively shown that the thresholds can be fixed, irrespective of the disturbance intensity, in such a way that faults are detected with probability one while probability of false alarms can be made as small as we wish. The ICLB issue is approached by the virtual reference approach. This allows one to obtain an inference of the performance of a candidate loop via a mean-square average of suitably filtered prediction errors. It is shown how a supervisory logic can be built by combining the results on CF with those on ICLB.

In this paper we present a predictive control strategy for the supervision of a formation of autonomous vehicles subject to coordination constraints. Such a system paradigm, referred hereafter to as constrained dynamic network, is characterized by a set of spatially distributed dynamic systems, connected via communication channels, with possibly dynamical coupling and constraints among them which need to be controlled and coordinated in order to accomplish their overall objective. The significance of the method is that it is capable of ensuring no constraints violation and loss of stability regardless of any, possibly unbounded, time-delay occurrence. An application to the coordination of two autonomous vehicles under input-saturation and formation accuracy constraints is presented.

Experimental results on industrial applications of the MUSMAR adaptive Predictive Controller are reported in this paper. They concern superheated steam temperature control in a thermoelectric power unit, oil temperature control in a distributed collector solar field, and trailing center line rate of cooling in a arc welding process. The processes considered are described, with an emphasis on the explanation of the difficulties met in the control problems associated to them. It is stressed that the examples presented in the paper are all based on experiments performed on actual full scale plants.

This paper describes some recent results in multi-model switching control. The scheme here considered embeds a finite family of pre-designed controllers and a high-level unit which selects, at each instant of time, the candidate controller to be placed in feedback to the uncertain plant. The study considers a switching strategy where controller selection is based on windowed cost functions. The key feature of the proposed strategy is that the window (the memory) is not kept constant, but, on the contrary, is adjusted on-line, on the grounds of measured data. The potential benefits of using an adaptive memory switching strategy are discussed and illustrated through a benchmark example. (C) 2013 Elsevier Ltd. All rights reserved.

The paper tackles the controller falsification problem in the framework of switching supervisory control. A comparison is carried out between two approaches: one is based on on-line testing candidate Lyapunov functions, the other on a suitable statistic which allows to detect divergence drifts in the currently operating loop.

This paper considers nonlinear system control via robust predictive controllers developed for constrained LTV systems with polytopic model uncertainties. The nonlinear systems dealt with are the ones whose trajectories can be embedded within those of a polytopic LTV system. This condition can be satisfied in bounded regions of the state space for a large class of nonlinear systems. The proposed predictive controller is proved to robustly quadratically stabilize input and state constrained LTV polytopic systems, as well as any other embedded non-linear system.

In this paper we present a predictive strategy for teleoperating, through communication channels with unknown and possibly unbounded time-delay, a remotely located pre-compensated plant subject to input and state-related pointwise-in-time constraints. The method can be specialized to deal either with bounded time-delays, as typically encountered in space and underwater applications, or with random and possibly unbounded round-trip time-delays, as in the Internet case.

The paper studies the problem of inferring the performance of a linear feedback-loop consisting of an uncertain plant and a candidate controller from data taken from the same plant possibly driven by a different controller. In such a context, a convenient tool to work with is a quantity called normalized discrepancy. This is a quadratic measure of mismatch between the loop made up by the unknown plant in feedback with the candidate controller and the nominal ''tuned-loop'' related to the same candidate controller. It is shown that discrepancy can be in principle obtained by resorting to the concept of a virtual reference, and conveniently computed in real-time by suitably filtering an output prediction error. The latter result is of relevant practical value for on-line implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse candidate model distribution.

The paper studies the problem of inferring the behaviour of a linear feedback loop made up by an uncertain MIMO plant and a given candidate controller from data taken from the plant possibly driven by a different controller. In such a context, it is shown here that a convenient tool to work with is a quantity called normalized discrepancy. This is a measure of mismatch between the loop made up by the unknown plant in feedback with the candidate controller and the nominal ‘tuned-loop’ related to the same candidate controller. It is shown that discrepancy can in principle be obtained by resorting to the concept of a virtual reference, and conveniently computed in real time by suitably filtering an output prediction error. The latter result is of relevant practical value for on-line implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse distribution of candidate models. Copyright © 2001 John Wiley & Sons, Ltd.

An analysis is presented on the feasibility issue in command governor (CG) control with deterministic set-membership state estimators for determining the state uncertainty set (SUS), viz. the set of all states which are compatible with measurements and a priori knowledge on the system. It is shown that the recipe of modifying standard receding horizon CG schemes based on the full knowledge of the state is effective provided that the latter is exactly computed at any time. On the contrary, when simple-shaped outer approximations of SUS are exploited for reducing the numerical complexity of the algorithm, loss of solvability or stability may result.

In this paper we present a predictive strategy for teleoperating, through communication channels with unknown and possibly unbounded time-delay, a remotely located pre-compensated plant subject to input and state-related pointwise-in-time constraints. In order to demonstrate its effectiveness, a challenging master/slave teleoperating problem over the Internet is formulated. It involves a locally pre-compensated laboratory cart/rod Inverted Pendulum (IP) as a remote plant and the problem consists in its wide-range stabilization under constraints on the cart motor voltage and rod angle excursion, while providing optimized steady-state and transient cart position tracking performance.

Robust command governors are designed for discrete-time linear time-variant (LTV) systems with polytopic uncertainty models and subject to unknown bounded disturbances and pointwise-in-time input and state-related constraints.

The paper studies the problem of inferring the behaviour of linear feedback loops made up by an uncertain plant and given candidate controllers by using data from the actual operating loop. In such a context, it is shown that a convenient tool to work with is a distance measure, called normalized divergence, between the loop comprising the plant in feedback with the candidate controller, and the nominal “tuned-loop” related to the same candidate controller. It is shown that divergence can be in principle obtained via the concept of a virtual reference, and computed in real-time by suitably filtering an output prediction error. This result is of relevant practical value for on-line implementation and of paramount importance in switching supervisory control of uncertain plants, particularly in the case of a coarse candidate model distribution.

It is shown that a way for inferring the performance of a linear feedback loop made up by an unknown plant and a given controller designed according to a quadratic criterion (LQ, H 2 , H ∞ etc.), is to resort to the concept of a virtual reference. This allows one to define a closed-loop performance:related distance measure, called divergence , between the unknown plant and each of its nominal models to which the candidate controllers are tuned on. It is proved that the divergence can be simply obtained by suitably filtering an output prediction error. This result is of paramount importance in switching supervisory control of uncertain plants.

A supervision scheme, denoted hereafter to as a Command Governor (CG), is presented for on-line handling the reference signal to be applied to a compensated control system in order to enforce pointwise-in-time input and/or staterelated constraints. A CG is basically a memoryless nonlinear device which, on the base of current plant state and reference signal, produces its action by modifying the reference signal in order to avoid potential constraint violations. An application to a bank-to-turn autopilot design is presented where the CG is used for governing the demanded pitch acceleration in order to avoid violation of physical constraints on the elevator angle.

This paper shows how to modify a recently introduced reference governor (RG) scheme [1], when its computational time is comparable or greater than the sampling period. It is shown that all relevant RG properties still hold true in the presence of a significant computational delay, though a tracking performance degradation may occur. Simulations results are presented so as to exhibit the effects due to the computational delay.

This paper deals with temperature control in an industrial-scale distributed collector solar field. The need for adaptive control arises from the highly time-varying behaviour of both the plant dynamics and the solar radiation effects in the collector field due to atmospheric changes. The control problem consists of keeping constant the field outlet oil temperature by acting on the oil flow. The manipulated variable is chosen so as to minimize a receding horizon quadratic cost. The underlying control law is of model-based predictive control type, adaptively implemented by a MUSMAR algorithm, modified so as to exploit the information conveyed by the accessible disturbances. Experimental results are reported showing the significance on the achievable performance of the feedforward term depending on the accessible disturbances. © 1997 John Wiley & Sons, Ltd.

The design of stabilizing predictive controllers for stochastic discrete-time linear plants is formulated in a state-space framework. The resulting approach, named SSPC (Stabilizing Stochastic Predictive Control), extends straightforwardly to MIMO plants wherein the controlled output is not directly measurable. It is demonstrated that SSPC leads to well known input-output solutions, such as SIORHC (Stabilizing Input/Output Receding Horizon Control) or SGPC (Stable Generalized Predictive Control), when particular state-space realizations of a CARIMA model are adopted. An efficient algorithm for computing the SSPC control law is also presented.

A method based on conceptual tools of predictive control is described for solving tracking problems wherein pointwise-in-time input and/or state inequality constraints are present. It consists of adding to a primal compensated system a nonlinear device called command governor (CG) whose action is based on the current state, set-point and prescribed constraints. The overall system is proved to fulfill the constraints, be asymptotically stable, and exhibit an offset-free tracking behaviour, provided that an admissibility condition on the initial state is satisfied.

In this paper we deal with adaptive algorithms based on cheap control in the presence of neglected dynamics. Convergence analysis is carried out for a relative dead-zone of both discontinuous and continuous type with the aim of pointing out in an explicit fashion the main differences related to these two facilities.

Robustness properties of an adaptive predictive controller are presented. The algorithm is obtained via a receding horizon control strategy by minimizing a quadratic performance index under the constraint that the terminal output sequence matches the reference over a suitable number of steps. The performances of both the nonadaptive and an adaptive version of the algorithm are examined in the case of unmodeled plant dynamics. >

Stabilizing input-output receding horizon control (SIORHC) yields dynamic feedback compensators capable of stabilizing any stabilizable linear plant under sharp conditions. The guaranteed stabilizing property is insured by using output terminal constraints in addition to input terminal constraints. This note extends previous SIORHC results to CARMA plants. >

This paper addresses the problem of controlling an uncertain multi-input multi-output (MIMO) system by means of adaptive switching control schemes. In particular, the paper aims at extending the approach of multiple-model unfalsified adaptive switched control, so far restricted to single-input single-output systems, to a general multivariable setting. The proposed scheme relies on a data-driven “high-level” unit, called the supervisor, which at any time can switch on in feedback with the uncertain plant one controller from a finite family of candidate controllers. The supervisor performs routing and scheduling tasks by monitoring suitable test functionals which, based on the measured data, provide a measure of mismatch between the potential loop made up by the uncertain plant in feedback with the candidate controller and the nominal “reference loop” related to the same candidate controller.

In this paper, a multi-model unfalsified adaptive switching control scheme is proposed for controlling uncertain plants subject to time variations. In the adopted approach, the switching between the candidate controllers is orchestrated according to a hysteresis logic variant wherein the memory length is adaptively selected, on the basis of the exhibited plant I/O behavior, so that past recorded data can be safely discarded. To this end, novel model-based resetting conditions are introduced. The global stability of the resulting switched closed-loop system is guaranteed provided that, at every time instant, a stabilizing candidate controller exists and that the (possibly abrupt) changes in the plant model are infrequent.

The paper deals with I/O versions of receding horizon controllers based on the minimization of multistep quadratic costs with the constraint that the terminal state goes to zero. The resulting control law yields stable closed-loop systems under sharp conditions. Simulation results are presented to both verify the theoretical analysis and relate the new control law with GPC

In recent years, unfalsified adaptive switching supervisory control (UASSC) has emerged as an effective technique for tackling the problem of controlling uncertain plants only on the basis of the plant I/O data. The aim of this paper is to construct a novel switching logic, which, when combined with appropriate test functions, makes it possible to extend UASSC, so far restricted to time-invariant systems, to the case of systems whose dynamics are subject to infrequent but possibly large variations.