Approximate optimal control for a class of nonlinear discrete-time systems with saturating actuators

Luo, Yanhong; Zhang, Huaguang

doi:10.1016/j.pnsc.2008.03.006

Cited by 22 publications

(10 citation statements)

References 16 publications

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“…When uncertainty and disturbance are involved, the dynamics of one-link manipulator can be expressed as the following form: 3 , and g 3 (x 3 ) = 1/M. It is apparent that the numerical example (75) is suitable for the case of system (1).…”

Section: Simulation Studymentioning

confidence: 99%

“…If the input saturation is ignored in the control design, the closed-loop control performance will be severely degraded, and instability may occur. In recent years, there have been extensive studies on various systems with input saturation in [1]- [3]. Neural network (NN)-based near-optimal control was developed for a class of discretetime affine nonlinear systems with control constraints in [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation

Chen

Tao

Jiang

2015

IEEE Trans. Neural Netw. Learning Syst.

415

224

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In this paper, a dynamic surface control (DSC) scheme is proposed for a class of uncertain strict-feedback nonlinear systems in the presence of input saturation and unknown external disturbance. The radial basis function neural network (RBFNN) is employed to approximate the unknown system function. To efficiently tackle the unknown external disturbance, a nonlinear disturbance observer (NDO) is developed. The developed NDO can relax the known boundary requirement of the unknown disturbance and can guarantee the disturbance estimation error converge to a bounded compact set. Using NDO and RBFNN, the DSC scheme is developed for uncertain nonlinear systems based on a backstepping method. Using a DSC technique, the problem of explosion of complexity inherent in the conventional backstepping method is avoided, which is specially important for designs using neural network approximations. Under the proposed DSC scheme, the ultimately bounded convergence of all closed-loop signals is guaranteed via Lyapunov analysis. Simulation results are given to show the effectiveness of the proposed DSC design using NDO and RBFNN.

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Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation

Chen

Tao

Jiang

2015

IEEE Trans. Neural Netw. Learning Syst.

415

224

View full text Add to dashboard Cite

show abstract

“…4, and the performance index values at different orders are listed in Table 2. From Table 2, the relative error 7 6 7…”

Section: Examplementioning

confidence: 99%

“…Recently, the digital techniques has been developing rapidly, many control methods of discrete-time systems have been used in practical engineering [6,7]. Some researchers discretized continuous systems with timedelays into equivalent discrete form in order to deal with the time-delays by discrete methods [8,9].…”

Section: Introductionmentioning

confidence: 99%

Observer-based optimal output tracking for discrete-time systems with multiple state and input delays

Wang

Tang

2009

Int. J. Control Autom. Syst.

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The optimal output tracking control (OOTC) problem for a class of discrete-time systems with state and input delays is addressed. An augmented system is constructed such that the OOTC problem can be transformed into a two-point boundary value (TPBV) problem with both advance and delay terms from the necessary optimality conditions. The successive approximating method recently developed is extended to obtain an approximate solution of the TPBV problem, which is then used to obtain a feedforward and feedback tracking controller. An observer is designed for the uncertain reference input such that the feedforward controller is physically realizable. Simulations show the results are effective even with long time-delays.

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“…Consequently, it is very necessary to take the input constraints into account during the attitude controller design [15][16][17][18][19][20][21]. For the linear system, predictive control [22] and optimal control [23] have been applied to treat problems with input constraints, whereas the control system must be determined a priori and no disturbances are considered yet. The problem of input constraints for aerospace application is considered in reference [24], in which an anti-windup control scheme was proposed for the large-angle attitude control of a spacecraft under actuator saturation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive quaternion-based output feedback control for flexible spacecraft attitude tracking with input constraints

Xiao

2011

Proceedings of the Institution of Mechanical Engineers, Part I:

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This work mainly explores a quaternion-based output feedback control scheme for the flexible spacecraft attitude tracking problem in which not only the unavailability of the body angular velocity measurements is considered, but also external disturbances and uncertain inertia parameters as well as input constraints are explicitly taken into account. The proposed controller incorporates a pseudo velocity filter to account for the unmeasured angular velocity, and the neural network technique is implemented to approximate the system uncertainties of the flexible spacecraft in the synthesis of the control algorithms. It is shown that this developed control method ensures the uniformly ultimate boundedness of the attitude tracking error and the control input can rigorously enforce actuator magnitude constraints. Numerical simulation results are also presented which not only highlight ensuring closed-loop performance benefits from the control law derived here, but also illustrate its robustness in the face of external disturbances when compared with conventional approaches for spacecraft attitude tracking control.

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Approximate optimal control for a class of nonlinear discrete-time systems with saturating actuators

Cited by 22 publications

References 16 publications

Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation

Dynamic Surface Control Using Neural Networks for a Class of Uncertain Nonlinear Systems With Input Saturation

Observer-based optimal output tracking for discrete-time systems with multiple state and input delays

Adaptive quaternion-based output feedback control for flexible spacecraft attitude tracking with input constraints

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