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In this paper, a robust control method is proposed for a class of fractional-order nonlinear system with unknown system parameter based on Lyapunov direct method. The Caputo definition is used to describe the fractional-order dynamic system. The controller is simple and enables asymptotically stability to be achieved without the computation of the conditional Lyapunov exponents. Finally, simulation...
In this paper, an off-policy reinforcement learning method is developed for the robust stabilizing controller design of discrete-time uncertain linear systems. The proposed robust control design consists of two steps. First, the robust control problem is transformed to an optimal control problem. Second, the off-policy RL method is used to design the optimal control policy which guarantees the robust...
This study presents a decoupled sliding mode controller for an inverted pendulum on a cart. The sliding mode control is a special type of variable structure control and it is applicable to the systems represented in the canonical form. For the systems which are not represented by the canonical form such as the inverted pendulum, the decoupled form of the sliding mode controller can be used. In this...
This paper utilizes the quantitative feedback theory (QFT) to design a robust controller for adaptive cutting feedrate control. The proposed algorithm could dynamically change the feedrate according to the spindle current which is highly related to the measured cutting force. The QFT controller could provide the constant cutting force even under different cutting condition. Hence, the machining efficiency...
Complex photonic circuits require advanced design and control tools to ensure their proper functionality even when subject to uncontrollable fabrication uncertainties and non-idealities. Here we show the use of optimization techniques to increase the robustness of a design to tolerances and an advanced tuning strategy to accurately control a fabricated photonic filter.
Robust belief revision methods are crucial in streaming data situations for updating existing knowledge (or beliefs) with new incoming evidence. Bayes conditioning is the primary mechanism in use for belief revision in data fusion systems that use probabilistic inference. However, traditional conditioning methods face several challenges due to inherent data/source imperfections in big-data environments...
In this paper a discrete-time static H∞ loop shaping control method for linear parameter-varying (LPV) systems is presented. The formulation aims to find a static parameter dependent stabilizing controller which yields guaranteed performance. Using quadratic H∞ performance, a set of sufficient conditions is provided in a two-step LMI (Linear Matrix Inequality) approach. The first step consists in...
This paper proposes a method for the design of a robust functional H∞ observer for a class of time delay bilinear systems. The considered system is affected by norm-bounded uncertainties in the system matrices and an unmeasured perturbations which are of finite energy. The unbiasedness conditions on the nominal part of the error dynamics of the proposed reduced order observer were given by calling...
Event-triggered output robust control algorithm is proposed in the paper. Controller synthesis is based on the consecutive compensator method. Event-trigger mechanism is based on the high-gain state observer. Developed controller provide input-to-state practical stability of unstable linear plants under conditions of parametric uncertainties and bounded external disturbances. Computer simulation confirm...
We consider linear, state-delayed, discrete-time systems with stochastic uncertainties in their state-space model. The problem of robust H∞ static output-feedback control is solved, for the stationary case, via the input-output approach by which the system is replaced by a non-retarded system with deterministic norm-bounded uncertainties. Based on the BRL result of the above systems, solutions are...
This paper presents a complete method for automatic and robust control configuration selection for linear systems which relies upon acquired process data under Gaussian noise excitation. The selection of the configuration is based on the estimation of the Interaction Measure named Participation Matrix. This estimation is derived with uncertainty bounds, which allows to determine online whether the...
Considering uncertainty in continuous production processes is key to compute short-time optimal schedules which can be trusted in practice. This paper proposes a two-step stochastic approach to the robust scheduling of several evaporation plants. This approach considers the possibility of reacting in the future once the uncertainty materializes. Each evaporator has different features (capacity, equipment,...
This paper presents a design of a robust tracking controller for a quadrotor UAV. A sliding mode controller (SMC) is employed in which the unmatched perturbations and uncertainties are estimated by a nonlinear disturbance observer (NDO). The NDO is employed to estimate the effect of wind perturbations and unknown aerodynamical forces. Several sliding surfaces are designed to explicitly depend on the...
This paper deals with the problem of robust state estimation for switched LPV continuous-time systems with measurable and unmeasurable scheduling parameters. The switching law is assumed to be uncontrollable but online available, while the unmeasurable varying parameters are assumed to be bounded with a priori known bounds. The proposed approach is based on switched interval observers which provide...
The problem of rejecting unknown sinusoidal disturbances in the output of an unknown LTI system which is also corrupted by wide-band noise has received a lot of interest in the recent years. Most of the successful efforts proposed up to now assume that the model of the LTI system is stable and is known exactly. This paper shows how an overparameterized classical robust model-reference adaptive control...
With the transformation to smarter cities and the development of technologies, a large amount of data is collected from sensors in real-time. This paradigm provides opportunities for improving transportation systems' performance by allocating vehicles towards mobility predicted demand proactively. However, how to deal with uncertainties in demand probability distribution for improving the average...
Imprecise input data imposes special challenges to workflow scheduling. This paper introduces a robust scheduler based on particle swarm optimisation, called RobWE, which considers uncertainties of available bandwidth when producing schedules for workflow ensembles. The proposed scheduler is also a flexible scheduler since it allows the replacement of its objective function according to the user's...
This paper presents multiple beacon vectors based robust detection schemes for cooperative spectrum sensing (CSS) in multiple-input multiple-output (MIMO) cognitive radio (CR) networks under channel state information (CSI) uncertainty. The inaccuracies in the estimate of the CSI are modeled as the standard ellipsoidal uncertainty set. We develop a multiple beacon vector based linear discriminant framework...
The output tracking control problem is considered for a class of uncertain nonlinear systems with time delays and dead-zone inputs. A simple design method is proposed so that a class of adaptive robust output tracking control laws can be synthesized. In particular, the synthesized controllers have a rather simple structure, and can be easily realized in practical engineering control systems. In this...
We propose a high-level road model information fusion framework to combine regulatory traffic elements, e.g. traffic signs, with lane geometry and digital map information for robust inference of lane-specific traffic rules. In this process, special care is given to adequately consider incomplete, uncertain, and inconsistent information sources with i) spatial, ii) existence, and iii) attribute uncertainties...
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