Fixed-wing unmanned aerial vehicles(UAVs) fly in a complex environment,which leads to multiple uncertainties in the flight control ***,nonlinear disturbances are inevitable in the dynamics of fixed-wing *** this paper...
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ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
Fixed-wing unmanned aerial vehicles(UAVs) fly in a complex environment,which leads to multiple uncertainties in the flight control ***,nonlinear disturbances are inevitable in the dynamics of fixed-wing *** this paper,we focus on neural network-based adaptive attitude control for fixed-wing UAVs subjected to stochastic multiple uncertainties and state *** the control scheme,radial basis function neural networks are used to approximate unknown nonlinear uncertainties,which can effectively reduce the adverse impact caused by unknown time-varying disturbances and random *** the signals in the closed-loop system are allowed to be semi-globally uniformly ultimately bounded,and the state constraints are guaranteed by establishing a stochastic Lyapunov *** results show the effectiveness of the proposed control scheme in this paper.
Optimal control for distributed parameter systems is much more difficulty than for lumped parameter systems, and various approximation techniques have been widely studied, but the greatest problem of the approximation...
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Optimal control for distributed parameter systems is much more difficulty than for lumped parameter systems, and various approximation techniques have been widely studied, but the greatest problem of the approximation methods is the accuracy and the computation time. In this paper, the piecewise multiple Chebyshev polynomials which were defined by the authors have been studied in depth, and a new approach-- PMCP approximation technique has been proposed to solve the optimal control for distributed parameter systems. By using PMCP approximation technique, the distributed parameter systems have been converted into N interactive lumped linear sub-systems which usually have higher dimensions, while we take the idea of hierarchical control to design the optimal controllers. In order to simplify the design and to avoid solving the continuous-time Riccati equations in the development of the first level optimal control strategies, the PMCP approximation method has again been adopted, and a well-posed algorithm has been obtained. The simulation results are satisfactory.
Two recent dynamic friction models, the LuGre model and a model by Bliman and Sorine, are investigated and compared. Comparisons are made of captured friction phenomena, behaviour at zero crossings of the velocity and...
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Two recent dynamic friction models, the LuGre model and a model by Bliman and Sorine, are investigated and compared. Comparisons are made of captured friction phenomena, behaviour at zero crossings of the velocity and computational issues. Limit cycles in position control of a simple servo with dynamic friction are investigated by means of single input describing function analysis. Both models give reasonable results in the describing function analysis. The LuGre model exhibits a richer behaviour in terms of friction phenomena. The Bliman-Sorine model can be problematic to use because of poor damping properties at zero crossings of the velocity.
This paper considers imbalance problems arising in Energy Management in Smart Grids (SG) as discrete-time stochastic linear systems subject to chance constraints, and proposes a Model Predictive control (MPC) approach...
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ISBN:
(数字)9781728171005
ISBN:
(纸本)9781728171012
This paper considers imbalance problems arising in Energy Management in Smart Grids (SG) as discrete-time stochastic linear systems subject to chance constraints, and proposes a Model Predictive control (MPC) approach to solve them. It is well-known that handling the closed-loop constraint feasibility of such systems is in general difficult due to the presence of a potentially unbounded uncertainty source. To overcome such a difficulty, we propose two new ideas. We first reformulate the chance constraint using the so-called Conditional Value at Risk (CVaR), which is known to be the tightest convex approximation for chance constraints. We then relax the CVaR constraint using a penalty function depending on a coefficient parameter. An optimal solution is therefore obtained by solving a single unconstrained problem which, intuitively, takes into consideration a risk of the system trajectories in an undesirable state. A case study using an academic example is presented to estimate the a-posteriori probability of the coefficient parameter in order to show when such a penalty function is exact by means of probabilistic constraint fulfillment.
This paper proposed a novel recovery algorithm for sparse wideband signals. Conventional recovery methods are mainly required the frequency support as a prior, or are based on some compressed sensing (CS) recovery alg...
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This paper proposed a novel recovery algorithm for sparse wideband signals. Conventional recovery methods are mainly required the frequency support as a prior, or are based on some compressed sensing (CS) recovery algorithms, such as simultaneous orthogonal matching pursuit (SOMP), which identifies a single spectrum band one time, and couldn't refine the selected spectrum band that is wrong. The proposed algorithm adopts a backtracking strategy. In this strategy, the proposed algorithm can maintain the correct frequency supports and refine the wrong ones during next iteration. The expectation is that the recursive refinements of the estimate of the frequency support set will lead a higher success rate than SOMP. Simulation results demonstrate the proposed algorithm outperforms SOMP.
An approach for model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. A fuzzy model (Takagi-Sugeno type) of the nominal process provides characteristic feat...
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An approach for model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. A fuzzy model (Takagi-Sugeno type) of the nominal process provides characteristic features like time constants and static gains in the actual region of operation. Comparing these with features derived by recursive parameter estimation leads to significant symptoms which indicate the state of the system. The practical applicability is illustrated on an industrial scale thermal plant. Here, nine different faults can be detected and isolated continuously over all ranges of operation.
The paper proposes the design of an integrated vehicle control system for in-wheel electric vehicle, which is able to track road geometry with a predefined reference velocity. In the design the lateral and longitudina...
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ISBN:
(纸本)9789633131862
The paper proposes the design of an integrated vehicle control system for in-wheel electric vehicle, which is able to track road geometry with a predefined reference velocity. In the design the lateral and longitudinal dynamics are combined using the in-wheel motors and the steering system. The design methodology of the hierarchical control is proposed. The required control signals are calculated by applying high-level controllers, which are designed using a robust control method. For the control design the model is augmented with weighting functions specified by the performance demands. The actuators generating the necessary control signals in order to achieve the requirements for which low-level tracking controllers are designed.
The aerospace engine is the most important part of the spacecraft, and it is related to whether the spacecraft can operate efficiently and stably. According to statistics, more than 90% of aerospace accidents are caus...
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This paper develops a control strategy based on immersion and invariance(I&I) adaptive methodology for a class of multi-input multi-output(MIMO) systems in the presence of parametric uncertainty, input saturation,...
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This paper develops a control strategy based on immersion and invariance(I&I) adaptive methodology for a class of multi-input multi-output(MIMO) systems in the presence of parametric uncertainty, input saturation, and external disturbance. To avoid the analytic calculation in the backstepping process, a highgain auxiliary system is constructed to compensate for the effect of command filter error. The first-order command filters are also employed in the construction procedure of the I&I adaptive law to simplify its design and remove the structural conditions on the regressors. A filter-based disturbance observer is developed to counteract the effect of the external disturbance produced by a partially known exogenous *** overcome the input saturation nonlinearity, a smooth function is introduced to approximate the input saturation with an extended state and a bounding estimation law. Stringent analysis guarantees the stability of closed-loop system. Finally, simulated examples confirm the effectiveness of the suggested method.
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