A Data-Driven Approach for UAV Tracking Control

2017 ◽  
Author(s):  
Soumya Vasisht ◽  
Mehran Mesbahi
Keyword(s):  
Linear Models ◽  
Measurement Data ◽  
Numerical Data ◽  
Linear Quadratic Regulator ◽  
Data Driven ◽  
Target System ◽  
Linear Quadratic ◽  
Visual Sensing ◽  
Data Driven Approach ◽  
Improved Performance

This paper presents a simple data-driven approach to improve ground target tracking by an unmanned aerial vehicle (UAV) for certain classes of target trajectories from learned local linear models. The UAV is assumed to be a small fixed-wing aircraft equipped with a gimbaled camera for visual sensing. We attempt to build a controller from measurement data by building an augmented Linear Quadratic Regulator (LQR) system from an approximated linear operator that indirectly captures the properties of the target system. We evaluate the relative performance improvement gained by this data-driven approach versus the standard target following LQR system and provide bounds for this improved performance. We also consider the effect of sensor noise on the tracking performance resulting from the noisy and erroneous datasets. We demonstrate the performance of this method on a range of numerical data representing different classes of target trajectories.

scholarly journals A data-driven approach for optimal design of integrated air quality monitoring network in a chemical cluster

2018 ◽  
Vol 5 (9) ◽  
pp. 180889 ◽  
Author(s):  
Zhengqiu Zhu ◽  
Bin Chen ◽  
Sihang Qiu ◽  
Rongxiao Wang ◽  
Yiping Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Measurement Data ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Data Driven ◽  
Atmospheric Environment ◽  
Illustrative Case ◽  
Air Quality Monitoring ◽  
Detection Capability ◽  
Data Driven Approach

The chemical industry is of paramount importance to the world economy and this industrial sector represents a substantial income source for developing countries. However, the chemical plants producing inside an industrial district pose a great threat to the surrounding atmospheric environment and human health. Therefore, designing an appropriate and available air quality monitoring network (AQMN) is essential for assessing the effectiveness of deployed pollution-controlling strategies and facilities. As monitoring facilities located at inappropriate sites would affect data validity, a two-stage data-driven approach constituted of a spatio-temporal technique (i.e. Bayesian maximum entropy) and a multi-objective optimization model (i.e. maximum concentration detection capability and maximum dosage detection capability) is proposed in this paper. The approach aims at optimizing the design of an AQMN formed by gas sensor modules. Owing to the lack of long-term measurement data, our developed atmospheric dispersion simulation system was employed to generate simulated data for the above method. Finally, an illustrative case study was implemented to illustrate the feasibility of the proposed approach, and results imply that this work is able to design an appropriate AQMN with acceptable accuracy and efficiency.

scholarly journals Optimal Attack against Autoregressive Models by Manipulating the Environment

2020 ◽  
Vol 34 (04) ◽  
pp. 3545-3552
Author(s):  
Yiding Chen ◽  
Xiaojin Zhu
Keyword(s):  
Predictive Control ◽  
Linear Models ◽  
Nonlinear Models ◽  
Linear Quadratic Regulator ◽  
Black Box ◽  
Combine System ◽  
Linear Quadratic ◽  
Time Series Forecast ◽  
Forecast Models ◽  
Adversarial Attack

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks.

Flight Control System Design for Propulsion-Controlled Aircraft

2005 ◽  
Vol 219 (4) ◽  
pp. 329-340 ◽  
Author(s):  
Y Ochi
Keyword(s):  
Flight Control ◽  
Linear Models ◽  
Control Method ◽  
Linear Quadratic Regulator ◽  
State Feedback Control ◽  
Longitudinal Motion ◽  
Linear Quadratic ◽  
Engine Thrust ◽  
Model Following Control ◽  
Flight Controls

The loss of an aircraft's primary flight controls can lead to a fatal accident. However, if the engine thrust is available, controllability and safety can be retained. This article describes flight control using engine thrust only when an aircraft has lost all primary flight controls. This is a kind of flight control reconfiguration. For safe return, the aircraft must first descend to a landing area, decelerate to a landing speed, and then be capable of precise flight control for approach and landing. For these purposes, two kinds of controllers are required: a controller for descent and deceleration and a controller for approach and landing. The former controller is designed for longitudinal motion using a model-following control method, based on a linear quadratic regulator. The latter is designed by an H∞ state-feedback control method for both longitudinal and lateral-directional motions. Computer simulation is conducted using linear models of the Boeing 747. The results indicate that flight path control, including approach and landing, is possible using thrust only; however, speed control proves more difficult. However, if the horizontal stabilizer is available, the airspeed can be reduced to a safe landing speed.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
High Fidelity ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
System P ◽  
Single Machine Infinite Bus

In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik N Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
High Fidelity ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Single Machine Infinite Bus

Abstract In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik N Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Synchronous Generator ◽  
Pole Placement ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Single Machine Infinite Bus

Abstract In this manuscript we present a high fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the higher fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

Data-Driven Backstepping Control of Underactuated Mechanical Systems

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Jingwen Huang ◽  
Tingting Zhang ◽  
Jian-Qiao Sun
Keyword(s):  
Mechanical Systems ◽  
Linear Quadratic Regulator ◽  
Backstepping Control ◽  
Data Driven ◽  
Underactuated Mechanical Systems ◽  
Linear Quadratic ◽  
Lqr Control ◽  
Whole Process ◽  
Model Free ◽  
Low Pass

This paper studies control problems of underactuated mechanical systems with model uncertainties. The control is designed with the method of backstepping. The first-order low-pass filters are used to estimate the unknown quantities and to avoid the “explosion of terms.” A novel method is also proposed to implement the control without the knowledge of the control coefficient, which makes the whole process of backstepping control data-driven. The stability of the proposed control in the Lyapunov sense is studied. It is numerically and experimentally validated, and compared with the well-known model-based linear quadratic regulator (LQR) control. The data-driven backstepping control is found to provide comparable performances to that of the LQR control with the advantage of being model-free and robust.

scholarly journals Robust LQR Control for PWM Converters with Parameter-Dependent Lyapunov Functions

2020 ◽  
Vol 10 (21) ◽  
pp. 7534
Author(s):  
Nedia Aouani ◽  
Carlos Olalla
Keyword(s):  
Synthesis Method ◽  
Linear Quadratic Regulator ◽  
Rate Of Change ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Linear Quadratic ◽  
Pulse Width Modulated ◽  
Pwm Converters ◽  
Improved Performance ◽  
Parameter Dependent

This paper presents a novel framework for robust linear quadratic regulator (LQR)-based control of pulse-width modulated (PWM) converters. The converter is modeled as a linear parameter-varying (LPV) system and the uncertainties, besides their rate of change, are taken into account. The proposed control synthesis method exploits the potential of linear matrix inequalities (LMIs), assuring robust stability whilst obtaining non-conservative results. The method has been validated in a PWM DC–DC boost converter, such that it has been shown, with the aid of simulations, that improved robustness and improved performance properties can be achieved, with respect to previously proposed approaches.

scholarly journals Feedback Control Methods for a Single Machine Infinite Bus System

2020 ◽  
Author(s):  
Pratik Vernekar ◽  
Zhongkui Wang ◽  
Andrea Serrani ◽  
Kevin Passino
Keyword(s):  
Single Machine ◽  
Linear Models ◽  
Control Strategies ◽  
Linear Quadratic Regulator ◽  
Pole Placement ◽  
High Fidelity ◽  
Nonlinear Feedback ◽  
Linear Quadratic ◽  
Reduced Order ◽  
Single Machine Infinite Bus

In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

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