The proposed adaptive control algorithm combines the recursive least-squares system identification algorithm and the generalized predictive control (GPC) design algorithm, referred to as recursive generalized predicti...
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The proposed adaptive control algorithm combines the recursive least-squares system identification algorithm and the generalized predictive control (GPC) design algorithm, referred to as recursive generalized predictive control (RGPC.) In the GPC design process, the prediction horizon and control horizon are the constants to be chosen. Two new parameters are defined to describe the effects of the prediction and control horizons and those parameters provide the effective ranges of the horizons. The RGPC algorithm adjusts the control penalty based on the stability of a closed-loop system model. A time-varying algorithm for the control penalty allows to design an aggressive controller. The multi-sampling rate algorithm is added between the system identification and the control design in order to design a higher order controller. The RGPC algorithm is applied to three different systems: a cantilevered beam, a sound enclosure. and an optical jitter suppression tesibed. (C) 2003 Published by Elsevier Ltd.
This Note focuses on continuous traditional and high-order sliding mode control for controlling the motion of one satellite as it follows a defined path around another satellite that is orbiting the Earth robustly to ...
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This Note focuses on continuous traditional and high-order sliding mode control for controlling the motion of one satellite as it follows a defined path around another satellite that is orbiting the Earth robustly to model uncertainties and external disturbances. The problem with any satellite formation control is that all orbiting bodies are subject to forces that tend to force the satellites out of their stable Keplerian orbits. These forces include gravitational perturbations, atmospheric drag, and solar radiation pressure. Recently Yeh et al. defined an effective robust method of controlling satellites using sliding mode control (SMC)4 while minimizing fuel consumption.
A new control technique based on a neural network, is proposed here for control of AC servo motors. The PID control is widely used in servo systems as it has simple structure, safety and reliability. However, it has c...
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A new control technique based on a neural network, is proposed here for control of AC servo motors. The PID control is widely used in servo systems as it has simple structure, safety and reliability. However, it has certain problems in a complex system, resulting in imperfect action in the presence of uncertain parameters. To solve these problems, a new hybrid control algorithm of the PID controller is proposed, which could prove the adequacy of the proposed control algorithm through simulation and experiments after driving the AC servo motor system using neural network PID controller.
A two-dimensional Mach 2.2 internal compression inlet with 97% total pressure recovery has been designed using viscid-inviscid computational tools. Losses are minimized by careful boundary-layer management combined wi...
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A two-dimensional Mach 2.2 internal compression inlet with 97% total pressure recovery has been designed using viscid-inviscid computational tools. Losses are minimized by careful boundary-layer management combined with shape design for weak shocks. The resulting inlet has reduced stability to unstart in the face of atmospheric and engine-borne disturbances, necessitating the use of an active stabilization bleed system that recovers the disturbance-rejection capabilities required of modern inlets. Atmospheric disturbances that the inlet may encounter during supersonic flight are characterized. Two separate physical mechanisms for unstart are identified, and active control algorithms to prevent these forms of unstart are designed and demonstrated using one- and two-dimensional unsteady Euler simulations. The resulting actively stabilized inlet can withstand flight velocity, temperature, and angle-of-attack perturbations consistent with atmospheric flight.
The problem of adaptive control over multidimensional nonlinear dynamic objects with the use of a neural network model is considered. To train the model, a recurrent least-squares method with exponential weighing of i...
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The problem of adaptive control over multidimensional nonlinear dynamic objects with the use of a neural network model is considered. To train the model, a recurrent least-squares method with exponential weighing of information and, to control an object, the multidimensional Kaczmarz algorithm are used. The results of an experimental investigation of the approach proposed are given.
Optimal power distribution control algorithm is investigated for parallel HEVs. In order to find the optimal control algorithm, optimization problem is definied, which finds the optimal control variables to minimize t...
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ISBN:
(纸本)0780394356
Optimal power distribution control algorithm is investigated for parallel HEVs. In order to find the optimal control algorithm, optimization problem is definied, which finds the optimal control variables to minimize the fuel consumption while satisfying the constraint equations. Scale factors in weighting the motor usage are selected as the control variables on how to distribute the demanded vehicle power into the electric motor and the internal combustion engine. Dynamic models of the HEV powertrain are represented with state space equations. The optimization problem is solved for the single shaft parallel HEV and double shaft parallel HEV using the HEV performance simulator developed in MATLAB Simulink and the mathematical optimization algorithm, SQP which is based on FORTRAN for multi-variables design optimization. It is found that the optimal control algorithm of the double shaft HEV provides better fuel economy than the single shaft HEV since the recuperation energy due to the regenerative braking can be increased by the engine off and clutch operation.
In the procedure of steady- state hierarchical optimization for large- scale industrial processes, it is often necessary that the control system responds to a sequence of step function- type control decisions with dis...
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In the procedure of steady- state hierarchical optimization for large- scale industrial processes, it is often necessary that the control system responds to a sequence of step function- type control decisions with distinct magnitudes. In this paper a set of iterative learning controllers are de- centrally embedded into the procedure of the steady- state optimization. This generates upgraded sequential control signals and thus improves the transient performance of the discrete-time large- scale systems. The convergence of the updating law is derived while the intervention from the distinction of the scales is analysed. Further, an optimal iterative learning control scheme is also deduced by means of a functional derivation. The effectiveness of the proposed scheme and the optimal rule is verified by simulation.
Actuator saturation is one of the major issues of flight control in the high-angle-of-attack region. A saturation control scheme for linear parameter-varying (LPV) systems from an antiwindup control perspective is pre...
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Actuator saturation is one of the major issues of flight control in the high-angle-of-attack region. A saturation control scheme for linear parameter-varying (LPV) systems from an antiwindup control perspective is presented. The proposed control approach is advantageous from the implementation standpoint because it can be thought of as an augmented control algorithm to the existing control system. Moreover, the synthesis condition for an antiwindup compensator is formulated as a linear matrix inequality optimization problem and can be solved efficiently. We have applied the LPV antiwindup controller to an F-16 longitudinal autopilot control system design and compared it with the thrust vectoring control scheme. The nonlinear simulations show that an LPV antiwindup controller improves flight quality and offers advantages over thrust vectoring in a high-angle-of-attack region.
Human controlled robots with variable parameters are considered, which are intended to solve manipulation problems when fast transportation operations are combined with high precision ones. In this paper, an issue of ...
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ISBN:
(纸本)0780392744
Human controlled robots with variable parameters are considered, which are intended to solve manipulation problems when fast transportation operations are combined with high precision ones. In this paper, an issue of the influence of robot dynamics on the system performance is investigated. Two algorithms are developed to solve the problem and their analysis is performed.
An adaptive double-stage PMD compensator capable of compensating first- and second-order PMD simultaneously is experimentally researched by introducing genetic algorithm for the first time. In order to verify the effe...
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ISBN:
(纸本)0819460524
An adaptive double-stage PMD compensator capable of compensating first- and second-order PMD simultaneously is experimentally researched by introducing genetic algorithm for the first time. In order to verify the effect of the double-stage PMD compensator, an experimental optical transmission system at a bit rate of 10 Gbit/s is setup. In experiments, degree of polarization (DOP) of received optical signal is adopted for PMD monitoring. Experimental results show that genetic algorithm is effective and powerful in first- and second-PMD compensation. The DOP of received optical signals after PMD compensation can reach a value larger than 0.97 within 180 milliseconds during which genetic algorithm runs 50 iterations. PMD measurement indicates that the first- and second-order PMD in transmission link can simultaneously reach the minima after PMD compensation at the operation wavelength.
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