作者：
Na WangWen-Shuo LiLei GuoHui-Lian HanSchool of Instrumentation Science and Opto-Electronics Engineering
Beihang UniversityBeijing100191China School of Automation Science and Electrical EngineeringBeihang UniversityBeijing100191China National Key Laboratory on Aircraft Control TechnologyBeihang UniversityBeijing100191China National key laboratory of aerospace intelligent control technologyBeijing aerospace automatic control instituteBeijing100854China
This paper investigates the problem of nonlinear sliding mode control for flexible air-breathing hypersonic vehicles (FAHVs) with *** modeling the flexible effects produced by the rigid-flexible coupling terms as a ki...
详细信息
ISBN:
(纸本)9781479946983
This paper investigates the problem of nonlinear sliding mode control for flexible air-breathing hypersonic vehicles (FAHVs) with *** modeling the flexible effects produced by the rigid-flexible coupling terms as a kind of disturbance and including in the new control-design model with multiple disturbances,a robust coupling observer is proposed to estimate these flexible effects.A novel composite hierarchical controller is also provided,which combines a robust coupling-observer-based compensator and a dynamic-inversion-based sliding mode *** addition,the uniformly ultimately boundedness of composite closed-loop system is confirmed by using Lyapunov *** results on a full nonlinear model of FAHVs demonstrate that the proposed composite controller is more effective than traditional dynamic-inversion-based sliding mode controller.
This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinea...
详细信息
This paper is concerned with the identification problems of linear parameter varying (LPV) systems with randomly missing output data. Since one local linearized model cannot capture the global dynamics of the nonlinear industrial process, the multiple-model LPV model in which the global model is constructed by smoothly weighted combination of multiple local models is considered here. The problem of missing output variables data is commonly encountered in practice. In order to handle the multiple-model identification problems of LPV systems with incomplete data, the local model is taken to have a finite impulse response (FIR) model structure and the generalized expectation-maximization (EM) algorithm is adopted to estimate the unknown parameters of the global LPV model. To avoid the problems of ill-conditioned matrices and high sensitivity of parameters to noise, the prior information on the coefficients of each local FIR model is employed to construct the prior probability of unknown parameters. Then the maximum a posteriori (MAP) estimates of the global model parameters are derived via the generalized EM algorithm. The numerical example is presented to demonstrate the effectiveness of the proposed method.
This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as th...
详细信息
This paper is concerned with identification of nonlinear systems with a noisy scheduling variable, and the measurement of the system has an unknown time delay. Auto regressive exogenous (ARX) models are selected as the local models, and multiple local models are identified along the process operating points. The dynamics of a nonlinear system are represented by associating a normalized exponential function with each of the ARX models; therein, the normalized exponential function is acted as the probability density function. The parameters of the ARX models and the exponential functions as well as the unknown time delay are estimated simultaneously under the expectation maximization (EM) algorithm using the retarded input-output data. A CSTR example is given to verify the proposed identification approach.
Improvement of the overall efficiency of energy infrastructure is one of the main anticipated benefits of the deployment of smart grid technology. Advancement in energy storage technology and two-way communication in ...
详细信息
ISBN:
(纸本)9781479932757
Improvement of the overall efficiency of energy infrastructure is one of the main anticipated benefits of the deployment of smart grid technology. Advancement in energy storage technology and two-way communication in the electric network are indispensable components to achieve such a vision, while efficient pricing schemes and appropriate storage management are also essential. In this paper, we propose a novel pricing scheme which permits one to indirectly control the energy storage devices in the grid to achieve a more desirable aggregate demand profile that meets a particular target of the grid operator such as energy generation cost minimization and carbon emission reduction. Such a pricing scheme can potentially be applied to control the behavior of energy storage devices installed for integration of intermittent renewable energy sources that have permission to grid connection and will have broader applications as an increasing number of novel and low-cost energy storage technologies emerge.
It is a challenging topic how to achieve the real-time tracking of fast video object under complex environment. In this paper, a scheme and its corresponding implementing algorithms of real-time tracking of fast video...
详细信息
This paper proposes a new asymptotic attitude tracking controller for an underactuated 3-degree-of-freedom （DOF） laboratory helicopter system by using a nonlinear robust feedback and a neural network （NN） feedforw...
详细信息
This paper proposes a new asymptotic attitude tracking controller for an underactuated 3-degree-of-freedom （DOF） laboratory helicopter system by using a nonlinear robust feedback and a neural network （NN） feedforward term. The nonlinear robust control law is developed through a modified inner-outer loop approach. The application of the NN-based feedforward is to compensate for the system uncertainties. The proposed control design strategy requires very limited knowledge of the system dynamic model, and achieves good robustness with respect to system parametric uncertainties. A Lyapunov-based stability analysis shows that the proposed algorithms can ensure asymptotic tracking of the helicopter＇s elevation and travel motion, while keeping the stability of the closed-loop system. Real-time experiment results demonstrate that the controller has achieved good tracking performance.
The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. It features a powerful geometric library that exten...
详细信息
The Multi-Parametric Toolbox is a collection of algorithms for modeling, control, analysis, and deployment of constrained optimal controllers developed under Matlab. It features a powerful geometric library that extends the application of the toolbox beyond optimal control to various problems arising in computational geometry. The new version 3.0 is a complete rewrite of the original toolbox with a more flexible structure that offers faster integration of new algorithms. The numerical side of the toolbox has been improved by adding interfaces to state of the art solvers and by incorporation of a new parametric solver that relies on solving linear-complementarity problems. The toolbox provides algorithms for design and implementation of real-time model predictive controllers that have been extensively tested.
This paper investigates the setpoints compensation for a class of complex industrial processes. Plants at the device layer are controlled by the local regulation controllers, and a multirate output feedback control ap...
详细信息
ISBN:
(纸本)9781467357692;9781467357678
This paper investigates the setpoints compensation for a class of complex industrial processes. Plants at the device layer are controlled by the local regulation controllers, and a multirate output feedback control approach for setpoint compensation is proposed such that the subsystems can reach the dynamically changed setpoints and the given economic objective can also be tracked via certain economic performance index (EPI). First, a sampled-data multivariable direct output feedback proportional integral (PI) controller is designed to regulate the performance of the subsystems. Second, the outputs and control inputs of the plants at the device layer are sampled at operation layer sampling time to form an EPI. Thus the multirate problem is solved by a lifting method. Third, static setpoints are generated by real time optimization (RTO) and the dynamic setpoints are calculated by the compensator according to the error between the EPI and objective at each operation layer step. Finally, a rougher flotation process model is employed to demonstrate the effectiveness of the proposed method.
Set-based estimation for nonlinear systems is a useful tool to handle sparse and uncertain data. The tool provides outer bounds on feasible parameter sets and reachable states, as well as provable inconsistency certif...
详细信息
ISBN:
(纸本)9781479901890
Set-based estimation for nonlinear systems is a useful tool to handle sparse and uncertain data. The tool provides outer bounds on feasible parameter sets and reachable states, as well as provable inconsistency certificates for entire parameter regions. In case of errors in the data such as outliers or incorrect a priori assumptions on variable uncertainties, set-based approaches can, however, lead to poor estimates or even rejection of a consistent model. We present a set-based approach to systematically identify outliers or incorrect variable uncertainty assumptions. The basic idea is to detect outliers by quantifying the influence they have on the inconsistency of an underlying feasibility problem. The results build on a set-based estimation framework that employs convex relaxations. Specifically we derive model consistency measures and sensitivity measures that combine the sensitivity information stored in the Lagrange dual variables. An algorithm is developed that iteratively detects outliers that contribute most to inconsistency. The algorithm terminates once the data and model are no longer proved inconsistent. The approach is illustrated by an example.
In this work we focus on unique diagnosability of parametric faults in the presence of measurement uncertainty and model mismatches. Specifically, we formulate a condition for diagnosability of parametric faults in a ...
详细信息
ISBN:
(纸本)9781467357159
In this work we focus on unique diagnosability of parametric faults in the presence of measurement uncertainty and model mismatches. Specifically, we formulate a condition for diagnosability of parametric faults in a set-based framework that allows for direct consideration of uncertainty. Based on this condition we present an approach for the analysis and certification of diagnosability. Furthermore, we propose an approach for the redesign of initially given fault classifications in the parameter space. Specifically we compute diagnosable subsets of initially given parameter sets in polynomial discrete-time fault candidates by comparing pairs of fault candidates. Furthermore, we demonstrate the presented approach for a numerical example.
暂无评论