Simon G . Fabri | University of Malta (original) (raw)

Uploads

Papers by Simon G . Fabri

Dual control laws offer important advantages for adaptive systems. The majority of dual control p... more Dual control laws offer important advantages for adaptive systems. The majority of dual control publications consider only discrete-time systems. This paper proposes a novel dual adaptive controller for nth order, parametric uncertain continuous-time linear systems that exhibits the desirable properties of discrete-time dual control in continuous-time too. These properties are verified through Monte Carlo tests.

Communications and control engineering series, 2001

ABSTRACT

IFAC Proceedings Volumes, Aug 1, 1998

A stochastic approach is used to control a multi-modal class of jump nonlinear stochastic systems... more A stochastic approach is used to control a multi-modal class of jump nonlinear stochastic systems whose underlying functions are unknown and which can change arbitrarily in time. Gaussian radial basis function neural networks are used to set up a number of local models, each characterising the different nonlinear plant modes. Being unknown. these different modes are identified on-line during control operation without resorting to a separate estimation phase. This entails detecting the occurrence of a mode change during operation. Since no information on the number of possible modes is assumed known. a self-organizing scheme is used to allocate automatically an appropriate number of local models in real time. Function identification, mode change detection and control signal generation are all based on probabilistic techniques utilising concepts of Kalman filtering, the multiple model algorithm and dual control. Simulations are given to show the effectiveness of the system for tracking a reference input. despite jumps in the unknown plant dynamics.

The use of composite adaptive l a ws for control of the ane class of nonlinear systems having unk... more The use of composite adaptive l a ws for control of the ane class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in better system performance. To ensure global stability despite the inevitable network approximation errors, the control law is augmented with a l o w gain sliding mode component and deadzone adaptation is used for the indirect part of the composite law. The stability of the system is analyzed and the eectiveness of the method is demonstrated by simulation.

Springer eBooks, 2001

ABSTRACT

IEEE Transactions on Neural Networks, Sep 1, 1996

Applied Mechanics Reviews, Oct 16, 2002

Springer eBooks, 2001

The local representation properties and the possibility of predetermining the basis function para... more The local representation properties and the possibility of predetermining the basis function parameters in Gaussian RBF networks, makes them ideal candidates for implementing functional adaptive control. However these attractive features are somewhat tarnished by the curse of dimensionality problem associated with GaRBF networks when used for high dimensional spaces.

IFAC Proceedings Volumes, Aug 1, 1998

A neural-adaptive control system for a class of discrete-time nonlinear plants is proposed. Neura... more A neural-adaptive control system for a class of discrete-time nonlinear plants is proposed. Neural networks and adaptation are required to ensure stability and asymptotic convergence of the tracking error. in spite of the nonlinear and unknown plant dynamics. An augmented error adaptive approach is taken. The effect of the inevitable imperfect approximation accuracy of the neural network on system stability is taken into consideration by using dead-zone adaptation. Stability and convergence proofs are presented together with a simulation example.

IFAC Proceedings Volumes, Jun 1, 1995

A:n. adaptive control technique, wing dynamic atructure Gawaian radial basia function neural netw... more A:n. adaptive control technique, wing dynamic atructure Gawaian radial basia function neural networks, that grow in time based on the location of the ayatem'a atate in apace, ia preaented for the affine clasa of nonlinear ayatem. having unknown or partially known dynamica. The method reaulta in a network that ia 'economic' in tenna of network aize, for casea where the atate apans only a amall aubaet of atate•apace, by utilizing leaa basia functions than would have been the case if basia functions were centred on diacrete locations covering the whole, relevant region of atate.apace. Additionally, the ayatem ia augmented with aliding control ao as to ensure global atability if and when the atate movea outside the region of at ate-apace apanned by the basia functions, and to ensure robwtneaa to diaturbancea that ariae due to the network inherent approximation erron.

Communications and control engineering series, 2001

During the mid-to-late 1960’s there emerged a new state estimation and control methodology for ha... more During the mid-to-late 1960’s there emerged a new state estimation and control methodology for handling the adaptive Incomplete State Information (ISI) problem [150, 166]. This methodology is known as Multiple Model Adaptive Estimation/Control (MMAE/C) or Partitioned Adaptive Filtering/Control(PAF/C). It originally appeared as a response to the fact that the reformulation of the adaptive ISI problem in terms of an augmented state (as explained in Section 6.3) yields a set of nonlinear equations, even if the original system were linear. Although this technique seems attractive, because it enables the uncertain parameters to be treated as part of the augmented state vector, estimation and control of nonlinear equations is not a simple task. This was explained in Chapter 6 when it was noted that in general, suboptimal solutions of the nonlinear ISI problem still remain computationally intensive.

Proceedings of the ... Annual Conference of the IEEE Industrial Electronics Society, Nov 1, 2006

Abstract—This paper presents a novel functional-adaptive dynamic con-troller for trajectory track... more Abstract—This paper presents a novel functional-adaptive dynamic con-troller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed in discrete-time and employs a multilayer percep-tron neural network for the estimation of the robot's nonlinear ...

IFAC-PapersOnLine, 2020

Traffic conditions in signalized junctions are highly dynamic and may be subject to abrupt change... more Traffic conditions in signalized junctions are highly dynamic and may be subject to abrupt changes due to unanticipated traffic incidents or network obstructions. These abrupt changing conditions are represented as different regimes or modes where each mode is represented by its own distinct model, forming a set of multiple models. At any instance in time, only one model of the set has the potential of representing the physical system dynamics at that time. However the dynamics may arbitrarily jump over to a different regime when an abnormal condition arises. Furthermore, it might be impossible to identify these models a priori. Hence, a multiple model approach is developed to self-detect these abrupt changes, identify which member of the set best represents the actual system and automatically self-configure and add a new model to the set when a previously unmodelled regime arises. This approach makes use of a real-time joint (dual) estimation algorithm to estimate traffic state variables such as queue lengths and traffic flow, as well as model parameters such as turning ratios, saturation flow values and noise covariance resulting from unmodelled dynamics and measurement errors. The proposed algorithm is validated through simulations on signalized 3-arm and 4-arm junctions with typical day-today traffic conditions including several network irregularities occuring at different times of the day such as arm closures as a result of traffic incidents. This work is aimed to form part of adaptive control loops for traffic light systems that are able to autonomously adjust to changing traffic conditions so as to ensure efficient vehicle flows.

International Journal of Control, Feb 10, 2015

A novel dual adaptive controller for extremum control of stochastic and uncertain nonlinear Hamme... more A novel dual adaptive controller for extremum control of stochastic and uncertain nonlinear Hammerstein systems is proposed. The design is based on the innovations dual control cost function originally developed for conventional adaptive control of linear systems. However, the design process is extensively modified and developed so as to cater for the extremum control scenario. This is a more challenging problem because the reference input is itself a nonlinear function of the unknown system parameters, rather than an independent and predefined external reference signal. As in all dual adaptive schemes, it leads to a control law that balances out the need for caution, due to parameter uncertainty, with the conflicting requirement of probing that acts to quickly reduce parameter uncertainty. The proposed controller's performance is analysed through extensive Monte Carlo simulation trials and compared with several other non-dual adaptive extremum controllers. It is shown that the novel extremum innovations dual controller is superior to other types of adaptive control systems that are based on a certainty equivalence assumption. In addition, a novel statistical measure is introduced that yields a more objective evaluation of the Monte Carlo simulation results.

Dual control laws offer important advantages for adaptive systems. The majority of dual control p... more Dual control laws offer important advantages for adaptive systems. The majority of dual control publications consider only discrete-time systems. This paper proposes a novel dual adaptive controller for nth order, parametric uncertain continuous-time linear systems that exhibits the desirable properties of discrete-time dual control in continuous-time too. These properties are verified through Monte Carlo tests.

Communications and control engineering series, 2001

ABSTRACT

IFAC Proceedings Volumes, Aug 1, 1998

A stochastic approach is used to control a multi-modal class of jump nonlinear stochastic systems... more A stochastic approach is used to control a multi-modal class of jump nonlinear stochastic systems whose underlying functions are unknown and which can change arbitrarily in time. Gaussian radial basis function neural networks are used to set up a number of local models, each characterising the different nonlinear plant modes. Being unknown. these different modes are identified on-line during control operation without resorting to a separate estimation phase. This entails detecting the occurrence of a mode change during operation. Since no information on the number of possible modes is assumed known. a self-organizing scheme is used to allocate automatically an appropriate number of local models in real time. Function identification, mode change detection and control signal generation are all based on probabilistic techniques utilising concepts of Kalman filtering, the multiple model algorithm and dual control. Simulations are given to show the effectiveness of the system for tracking a reference input. despite jumps in the unknown plant dynamics.

The use of composite adaptive l a ws for control of the ane class of nonlinear systems having unk... more The use of composite adaptive l a ws for control of the ane class of nonlinear systems having unknown dynamics is proposed. These dynamics are approximated by Gaussian radial basis function neural networks whose parameters are updated by a composite law that is driven by both tracking and estimation errors. This is motivated by the need to improve the speed of convergence of the unknown parameters, hence resulting in better system performance. To ensure global stability despite the inevitable network approximation errors, the control law is augmented with a l o w gain sliding mode component and deadzone adaptation is used for the indirect part of the composite law. The stability of the system is analyzed and the eectiveness of the method is demonstrated by simulation.

Springer eBooks, 2001

ABSTRACT

IEEE Transactions on Neural Networks, Sep 1, 1996

Applied Mechanics Reviews, Oct 16, 2002

Springer eBooks, 2001

The local representation properties and the possibility of predetermining the basis function para... more The local representation properties and the possibility of predetermining the basis function parameters in Gaussian RBF networks, makes them ideal candidates for implementing functional adaptive control. However these attractive features are somewhat tarnished by the curse of dimensionality problem associated with GaRBF networks when used for high dimensional spaces.

IFAC Proceedings Volumes, Aug 1, 1998

A neural-adaptive control system for a class of discrete-time nonlinear plants is proposed. Neura... more A neural-adaptive control system for a class of discrete-time nonlinear plants is proposed. Neural networks and adaptation are required to ensure stability and asymptotic convergence of the tracking error. in spite of the nonlinear and unknown plant dynamics. An augmented error adaptive approach is taken. The effect of the inevitable imperfect approximation accuracy of the neural network on system stability is taken into consideration by using dead-zone adaptation. Stability and convergence proofs are presented together with a simulation example.

IFAC Proceedings Volumes, Jun 1, 1995

A:n. adaptive control technique, wing dynamic atructure Gawaian radial basia function neural netw... more A:n. adaptive control technique, wing dynamic atructure Gawaian radial basia function neural networks, that grow in time based on the location of the ayatem'a atate in apace, ia preaented for the affine clasa of nonlinear ayatem. having unknown or partially known dynamica. The method reaulta in a network that ia 'economic' in tenna of network aize, for casea where the atate apans only a amall aubaet of atate•apace, by utilizing leaa basia functions than would have been the case if basia functions were centred on diacrete locations covering the whole, relevant region of atate.apace. Additionally, the ayatem ia augmented with aliding control ao as to ensure global atability if and when the atate movea outside the region of at ate-apace apanned by the basia functions, and to ensure robwtneaa to diaturbancea that ariae due to the network inherent approximation erron.

Communications and control engineering series, 2001

During the mid-to-late 1960’s there emerged a new state estimation and control methodology for ha... more During the mid-to-late 1960’s there emerged a new state estimation and control methodology for handling the adaptive Incomplete State Information (ISI) problem [150, 166]. This methodology is known as Multiple Model Adaptive Estimation/Control (MMAE/C) or Partitioned Adaptive Filtering/Control(PAF/C). It originally appeared as a response to the fact that the reformulation of the adaptive ISI problem in terms of an augmented state (as explained in Section 6.3) yields a set of nonlinear equations, even if the original system were linear. Although this technique seems attractive, because it enables the uncertain parameters to be treated as part of the augmented state vector, estimation and control of nonlinear equations is not a simple task. This was explained in Chapter 6 when it was noted that in general, suboptimal solutions of the nonlinear ISI problem still remain computationally intensive.

Proceedings of the ... Annual Conference of the IEEE Industrial Electronics Society, Nov 1, 2006

Abstract—This paper presents a novel functional-adaptive dynamic con-troller for trajectory track... more Abstract—This paper presents a novel functional-adaptive dynamic con-troller for trajectory tracking of nonholonomic wheeled mobile robots. The controller is developed in discrete-time and employs a multilayer percep-tron neural network for the estimation of the robot's nonlinear ...

IFAC-PapersOnLine, 2020

Traffic conditions in signalized junctions are highly dynamic and may be subject to abrupt change... more Traffic conditions in signalized junctions are highly dynamic and may be subject to abrupt changes due to unanticipated traffic incidents or network obstructions. These abrupt changing conditions are represented as different regimes or modes where each mode is represented by its own distinct model, forming a set of multiple models. At any instance in time, only one model of the set has the potential of representing the physical system dynamics at that time. However the dynamics may arbitrarily jump over to a different regime when an abnormal condition arises. Furthermore, it might be impossible to identify these models a priori. Hence, a multiple model approach is developed to self-detect these abrupt changes, identify which member of the set best represents the actual system and automatically self-configure and add a new model to the set when a previously unmodelled regime arises. This approach makes use of a real-time joint (dual) estimation algorithm to estimate traffic state variables such as queue lengths and traffic flow, as well as model parameters such as turning ratios, saturation flow values and noise covariance resulting from unmodelled dynamics and measurement errors. The proposed algorithm is validated through simulations on signalized 3-arm and 4-arm junctions with typical day-today traffic conditions including several network irregularities occuring at different times of the day such as arm closures as a result of traffic incidents. This work is aimed to form part of adaptive control loops for traffic light systems that are able to autonomously adjust to changing traffic conditions so as to ensure efficient vehicle flows.

International Journal of Control, Feb 10, 2015

A novel dual adaptive controller for extremum control of stochastic and uncertain nonlinear Hamme... more A novel dual adaptive controller for extremum control of stochastic and uncertain nonlinear Hammerstein systems is proposed. The design is based on the innovations dual control cost function originally developed for conventional adaptive control of linear systems. However, the design process is extensively modified and developed so as to cater for the extremum control scenario. This is a more challenging problem because the reference input is itself a nonlinear function of the unknown system parameters, rather than an independent and predefined external reference signal. As in all dual adaptive schemes, it leads to a control law that balances out the need for caution, due to parameter uncertainty, with the conflicting requirement of probing that acts to quickly reduce parameter uncertainty. The proposed controller's performance is analysed through extensive Monte Carlo simulation trials and compared with several other non-dual adaptive extremum controllers. It is shown that the novel extremum innovations dual controller is superior to other types of adaptive control systems that are based on a certainty equivalence assumption. In addition, a novel statistical measure is introduced that yields a more objective evaluation of the Monte Carlo simulation results.

Haptics refers to a widespread area of research that focuses on the interaction between humans an... more Haptics refers to a widespread area of research that focuses on the interaction between humans and machine interfaces as applied to the sense of touch. A haptic interface is designed to increase the realism of tactile and kinesthetic sensations in applications such as virtual reality, teleoperation, and other scenarios where situational awareness is considered important, if not vital. This paper investigates the use of electric actuators and non-linear algorithms to provide force feedback to an input command device for providing haptics to the human operator. In particular, this work involves the study and implementation of a special case of feedback linearization known as inverse dynamics control and several outer loop impedance control topologies. It also investigates the issues concerned with force sensing and the application of model based controller functions in order to vary the desired inertia and the desired mass matrix. Results of the controllers’ abilities to display any desired impedance and provide the required kinesthetic constraint of virtual environments are shown on two experimental test rigs
designed for this purpose.