13,804 research outputs found
Predictive Second Order Sliding Control of Constrained Linear Systems with Application to Automotive Control Systems
This paper presents a new predictive second order sliding controller (PSSC)
formulation for setpoint tracking of constrained linear systems. The PSSC
scheme is developed by combining the concepts of model predictive control (MPC)
and second order discrete sliding mode control. In order to guarantee the
feasibility of the PSSC during setpoint changes, a virtual reference variable
is added to the PSSC cost function to calculate the closest admissible set
point. The states of the system are then driven asymptotically to this
admissible setpoint by the control action of the PSSC. The performance of the
proposed PSSC is evaluated for an advanced automotive engine case study, where
a high fidelity physics-based model of a reactivity controlled compression
ignition (RCCI) engine is utilized to serve as the virtual test-bed for the
simulations. Considering the hard physical constraints on the RCCI engine
states and control inputs, simultaneous tracking of engine load and optimal
combustion phasing is a challenging objective to achieve. The simulation
results of testing the proposed PSSC on the high fidelity RCCI model show that
the developed predictive controller is able to track desired engine load and
combustion phasing setpoints, with minimum steady state error, and no
overshoot. Moreover, the simulation results confirm the robust tracking
performance of the PSSC during transient operations, in the presence of engine
cyclic variability.Comment: 6 pages, 5 figures, 2018 American Control Conferance (ACC), June
27-29, 2018, Milwaukee, WI, USA. [Accepted in Jan. 2018
Explicit Reference Governor for Continuous Time Nonlinear Systems Subject to Convex Constraints
This paper introduces a novel closed-form strategy that dynamically modifies
the reference of a pre-compensated nonlinear system to ensure the satisfaction
of a set of convex constraints. The main idea consists of translating
constraints in the state space into constraints on the Lyapunov function and
then modulating the reference velocity so as to limit the value of the Lyapunov
function. The theory is introduced for general nonlinear systems subject to
convex constraints. In the case of polyhedric constraints, an explicit solution
is provided for the large and highly relevant class of nonlinear systems whose
Lyapunov function is lower-bounded by a quadratic form. In view of improving
performances, further specializations are provided for the relevant cases of
linear systems and robotic manipulators.Comment: Submitted to: IEEE Transactions on Automatic Contro
Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
In many nonlinear control problems, the plant can be accurately described by
a linear model whose operating point depends on some measurable variables,
called scheduling signals. When such a linear parameter-varying (LPV) model of
the open-loop plant needs to be derived from a set of data, several issues
arise in terms of parameterization, estimation, and validation of the model
before designing the controller. Moreover, the way modeling errors affect the
closed-loop performance is still largely unknown in the LPV context. In this
paper, a direct data-driven control method is proposed to design LPV
controllers directly from data without deriving a model of the plant. The main
idea of the approach is to use a hierarchical control architecture, where the
inner controller is designed to match a simple and a-priori specified
closed-loop behavior. Then, an outer model predictive controller is synthesized
to handle input/output constraints and to enhance the performance of the inner
loop. The effectiveness of the approach is illustrated by means of a simulation
and an experimental example. Practical implementation issues are also
discussed.Comment: Preliminary version of the paper "Direct data-driven control of
constrained systems" published in the IEEE Transactions on Control Systems
Technolog
Comparing Kalman Filters and Observers for Power System Dynamic State Estimation with Model Uncertainty and Malicious Cyber Attacks
Kalman filters and observers are two main classes of dynamic state estimation
(DSE) routines. Power system DSE has been implemented by various Kalman
filters, such as the extended Kalman filter (EKF) and the unscented Kalman
filter (UKF). In this paper, we discuss two challenges for an effective power
system DSE: (a) model uncertainty and (b) potential cyber attacks. To address
this, the cubature Kalman filter (CKF) and a nonlinear observer are introduced
and implemented. Various Kalman filters and the observer are then tested on the
16-machine, 68-bus system given realistic scenarios under model uncertainty and
different types of cyber attacks against synchrophasor measurements. It is
shown that CKF and the observer are more robust to model uncertainty and cyber
attacks than their counterparts. Based on the tests, a thorough qualitative
comparison is also performed for Kalman filter routines and observers.Comment: arXiv admin note: text overlap with arXiv:1508.0725
Plug-and-Play Decentralized Model Predictive Control
In this paper we consider a linear system structured into physically coupled
subsystems and propose a decentralized control scheme capable to guarantee
asymptotic stability and satisfaction of constraints on system inputs and
states. The design procedure is totally decentralized, since the synthesis of a
local controller uses only information on a subsystem and its neighbors, i.e.
subsystems coupled to it. We first derive tests for checking if a subsystem can
be plugged into (or unplugged from) an existing plant without spoiling overall
stability and constraint satisfaction. When this is possible, we show how to
automatize the design of local controllers so that it can be carried out in
parallel by smart actuators equipped with computational resources and capable
to exchange information with neighboring subsystems. In particular, local
controllers exploit tube-based Model Predictive Control (MPC) in order to
guarantee robustness with respect to physical coupling among subsystems.
Finally, an application of the proposed control design procedure to frequency
control in power networks is presented.Comment: arXiv admin note: text overlap with arXiv:1210.692
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