21,830 research outputs found
Adaptive Discrete Second Order Sliding Mode Control with Application to Nonlinear Automotive Systems
Sliding mode control (SMC) is a robust and computationally efficient
model-based controller design technique for highly nonlinear systems, in the
presence of model and external uncertainties. However, the implementation of
the conventional continuous-time SMC on digital computers is limited, due to
the imprecisions caused by data sampling and quantization, and the chattering
phenomena, which results in high frequency oscillations. One effective solution
to minimize the effects of data sampling and quantization imprecisions is the
use of higher order sliding modes. To this end, in this paper, a new
formulation of an adaptive second order discrete sliding mode control (DSMC) is
presented for a general class of multi-input multi-output (MIMO) uncertain
nonlinear systems. Based on a Lyapunov stability argument and by invoking the
new Invariance Principle, not only the asymptotic stability of the controller
is guaranteed, but also the adaptation law is derived to remove the
uncertainties within the nonlinear plant dynamics. The proposed adaptive
tracking controller is designed and tested in real-time for a highly nonlinear
control problem in spark ignition combustion engine during transient operating
conditions. The simulation and real-time processor-in-the-loop (PIL) test
results show that the second order single-input single-output (SISO) DSMC can
improve the tracking performances up to 90%, compared to a first order SISO
DSMC under sampling and quantization imprecisions, in the presence of modeling
uncertainties. Moreover, it is observed that by converting the engine SISO
controllers to a MIMO structure, the overall controller performance can be
enhanced by 25%, compared to the SISO second order DSMC, because of the
dynamics coupling consideration within the MIMO DSMC formulation.Comment: 12 pages, 7 figures, 1 tabl
MIMO First and Second Order Discrete Sliding Mode Controls of Uncertain Linear Systems under Implementation Imprecisions
The performance of a conventional model-based controller significantly
depends on the accuracy of the modeled dynamics. The model of a plant's
dynamics is subjected to errors in estimating the numerical values of the
physical parameters, and variations over operating environment conditions and
time. These errors and variations in the parameters of a model are the major
sources of uncertainty within the controller structure. Digital implementation
of controller software on an actual electronic control unit (ECU) introduces
another layer of uncertainty at the controller inputs/outputs. The
implementation uncertainties are mostly due to data sampling and quantization
via the analog-to-digital conversion (ADC) unit. The failure to address the
model and ADC uncertainties during the early stages of a controller design
cycle results in a costly and time consuming verification and validation (V&V)
process. In this paper, new formulations of the first and second order discrete
sliding mode controllers (DSMC) are presented for a general class of uncertain
linear systems. The knowledge of the ADC imprecisions is incorporated into the
proposed DSMCs via an online ADC uncertainty prediction mechanism to improve
the controller robustness characteristics. Moreover, the DSMCs are equipped
with adaptation laws to remove two different types of modeling uncertainties
(multiplicative and additive) from the parameters of the linear system model.
The proposed adaptive DSMCs are evaluated on a DC motor speed control problem
in real-time using a processor-in-the-loop (PIL) setup with an actual ECU. The
results show that the proposed SISO and MIMO second order DSMCs improve the
conventional SISO first order DSMC tracking performance by 69% and 84%,
respectively. Moreover, the proposed adaptation mechanism is able to remove the
uncertainties in the model by up to 90%.Comment: 10 pages, 11 figures, ASME 2017 Dynamic Systems and Control
Conferenc
Variance-constrained multiobjective control and filtering for nonlinear stochastic systems: A survey
The multiobjective control and filtering problems for nonlinear stochastic systems with variance constraints are surveyed. First, the concepts of nonlinear stochastic systems are recalled along with the introduction of some recent advances. Then, the covariance control theory, which serves as a practical method for multi-objective control design as well as a foundation for linear system theory, is reviewed comprehensively. The multiple design requirements frequently applied in engineering practice for the use of evaluating system performances are introduced, including robustness, reliability, and dissipativity. Several design techniques suitable for the multi-objective variance-constrained control and filtering problems for nonlinear stochastic systems are discussed. In particular, as a special case for the multi-objective design problems, the mixed H 2 / H ∞ control and filtering problems are reviewed in great detail. Subsequently, some latest results on the variance-constrained multi-objective control and filtering problems for the nonlinear stochastic systems are summarized. Finally, conclusions are drawn, and several possible future research directions are pointed out
A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control Algorithm for Dynamic Uncertain Systems
This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding
Mode Controller for the control of dynamic uncertain systems. The proposed
controller combines the advantages of Second order Sliding Mode Control, Fuzzy
Logic Control and Adaptive Control. The reaching conditions, stability and
robustness of the system with the proposed controller are guaranteed. In
addition, the proposed controller is well suited for simple design and
implementation. The effectiveness of the proposed controller over the first
order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based
simulations performed on a DC-DC Buck converter. Based on this comparison, the
proposed controller is shown to obtain the desired transient response without
causing chattering and error under steady-state conditions. The proposed
controller is able to give robust performance in terms of rejection to input
voltage variations and load variations.Comment: 14 page
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
Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey
Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Robust Adaptive Control of a Class of Nonlinear Strict-feedback Discrete-time Systems with Exact Output Tracking
10.1016/j.automatica.2009.07.025Automatica45112537-2545ATCA
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