28 research outputs found
Interval Prediction for Continuous-Time Systems with Parametric Uncertainties
The problem of behaviour prediction for linear parameter-varying systems is
considered in the interval framework. It is assumed that the system is subject
to uncertain inputs and the vector of scheduling parameters is unmeasurable,
but all uncertainties take values in a given admissible set. Then an interval
predictor is designed and its stability is guaranteed applying Lyapunov
function with a novel structure. The conditions of stability are formulated in
the form of linear matrix inequalities. Efficiency of the theoretical results
is demonstrated in the application to safe motion planning for autonomous
vehicles.Comment: 6 pages, CDC 2019. Website:
https://eleurent.github.io/interval-prediction
Fault tolerant control of uncertain dynamical systems using interval virtual actuators
This is the peer reviewed version of the following article: Rotondo D, Cristofaro A, Johansen TA. Fault tolerant control of uncertain dynamical systems using interval virtual actuators. Int J Robust Nonlinear Control. 2018;28:611–624, which has been published in final form at https://doi.org/10.1002/rnc.3888. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this paper, a model reference fault tolerant control strategy based on a reconfiguration of the reference model, with the addition of a virtual actuator block, is presented for uncertain systems affected by disturbances and sensor noise. In particular, this paper (1) extends the reference model approach to the use of interval state observers, by considering an error feedback controller, which uses the estimated bounds for the error between the real state and the reference state, and (2) extends the virtual actuator approach to the use of interval observers, which means that the virtual actuator is added to the control loop to preserve the nonnegativity of the interval estimation errors and the boundedness of the involved signals, in spite of the fault occurrence. In both cases, the conditions to assure the desired operation of the control loop are provided in terms of linear matrix inequalities. An illustrative example is used to show the main characteristics of the proposed approach.Peer ReviewedPostprint (author's final draft
Fault tolerant control of uncertain dynamical systems using interval virtual actuators
This is the peer reviewed version of the following article: Rotondo D, Cristofaro A, Johansen TA. Fault tolerant control of uncertain dynamical systems using interval virtual actuators. Int J Robust Nonlinear Control. 2018;28:611–624, which has been published in final form at https://doi.org/10.1002/rnc.3888. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.In this paper, a model reference fault tolerant control strategy based on a reconfiguration of the reference model, with the addition of a virtual actuator block, is presented for uncertain systems affected by disturbances and sensor noise. In particular, this paper (1) extends the reference model approach to the use of interval state observers, by considering an error feedback controller, which uses the estimated bounds for the error between the real state and the reference state, and (2) extends the virtual actuator approach to the use of interval observers, which means that the virtual actuator is added to the control loop to preserve the nonnegativity of the interval estimation errors and the boundedness of the involved signals, in spite of the fault occurrence. In both cases, the conditions to assure the desired operation of the control loop are provided in terms of linear matrix inequalities. An illustrative example is used to show the main characteristics of the proposed approach.Peer ReviewedPostprint (author's final draft
Distributed Set-Based Observers Using Diffusion Strategy
Distributed estimation is more robust against single points of failure and
requires less communication overhead compared to the centralized version. Among
distributed estimation techniques, set-based estimation has gained much
attention as it provides estimation guarantees for safety-critical applications
and copes with unknown but bounded uncertainties. We propose two distributed
set-based observers using interval-based and set-membership approaches for a
linear discrete-time dynamical system with bounded modeling and measurement
uncertainties. Both algorithms utilize a new over-approximating zonotopes
intersection step named the set-based diffusion step. We use the term diffusion
since our intersection of zonotopes formula resembles the traditional diffusion
step in the stochastic Kalman filter. Our new zonotopes intersection takes
linear time. Our set-based diffusion step decreases the estimation errors and
the size of estimated sets and can be seen as a lightweight approach to achieve
partial consensus between the distributed estimated sets. Every node shares its
measurement with its neighbor in the measurement update step. The neighbors
intersect their estimated sets constituting our proposed set-based diffusion
step. We represent sets as zonotopes since they compactly represent
high-dimensional sets, and they are closed under linear mapping and Minkowski
addition. The applicability of our algorithms is demonstrated by a localization
example. All used data and code to recreate our findings are publicly availabl