Application of discrete Chebyshev polynomials to the optimal control of digital systems

The shift transformation matrix for discrete Chebyshev polynomials is introduced in this study. The discrete variational principle combined with the idea of penalty function is taken to construct the modified discrete Euler-Lagrange equations. Then, the discrete Chebyshev series are applied to simplify the modified equations into a set of linear algebraic ones for the approximations of state and control variables of digital systems. It is seen that this technique is quite straightforward and simple, and computing time can be saved considerably.     

带有持续扰动非线性系统的前馈-反馈最优控制

研究具有外界持续扰动作用下非线性系统的最优控制问题,提出了一种设计前馈一反馈最优控制器的逐次逼近算法．利用该算法可将在扰动作用下的非线性系统的最优控制问题转化为求解线性非齐次两点边值序列的问题．得到的最优控制律由解析的线性前馈-反馈项和伴随向量序列极限形式的非线性补偿项组成．通过截取非线性补偿序列的有限项,可得到前馈-反馈次优控制律．仿真结果表明,该方法抑制外部持续扰动的鲁棒性优于经典反馈最优控制．     

超混沌系统同步非线性反馈控制

在将超混沌系统的控制、同步及反同步问题统一处理的基础上,给出了一种可实现超混沌系统同步的非线性状态反馈方法。该方法以非线性系统的线性化方法和极点配置理论为基础,把控制律分解为非线性和线性两部分之和,使超混沌系统得以控制、同步和反同步。对超混沌Newton-Leipnik系统的仿真实验表明了控制策略的有效性。     

受扰非线性离散系统的前馈反馈最优控制

利用逐次逼近法研究含外部扰动的非线性离散系统的线性二次型前馈反馈最优控制问题.首先将系统的最优控制问题转化为非线性两点边值问题族.其次,构造了该问题族的由精确线性项和非线性补偿项组成的解序列,并证明了解序列一致收敛到系统的最优解.最后,通过截取最优控制序列解中非线性补偿项的有限项,得到系统的前馈反馈次优控制(FFSOC)律及设计算法.仿真算例表明,该算法容易实现,且对抑制外部扰动的鲁棒性优于经典的反馈次优控制(FSOC).     

Global optimal feedback control for general nonlinear systems with nonquadratic performance criteria

Optimal control of general nonlinear nonaffine controlled systems with nonquadratic performance criteria (that permit state- and control-dependent time-varying weighting parameters), is solved classically using a sequence of linear- quadratic and time-varying problems. The proposed method introduces an “approximating sequence of Riccati equations” (ASRE) to explicitly construct nonlinear time-varying optimal state-feedback controllers for such nonlinear systems. Under very mild conditions of local Lipschitz continuity, the sequences converge (globally) to nonlinear optimal stabilizing feedback controls. The computational simplicity and effectiveness of the ASRE algorithm is an appealing alternative to the tedious and laborious task of solving the Hamilton–Jacobi–Bellman partial differential equation. So the optimality of the ASRE control is studied by considering the original nonlinear-nonquadratic optimization problem and the corresponding necessary conditions for optimality, derived from Pontryagin's maximum principle. Global optimal stabilizing state-feedback control laws are then constructed. This is compared with the optimality of the ASRE control by considering a nonlinear fighter aircraft control system, which is nonaffine in the control. Numerical simulations are used to illustrate the application of the ASRE methodology, which demonstrate its superior performance and optimality.     

Asymptotic output stabilization for nonlinear systems via dynamical variable-structure feedback control

In this article, the problem of asymptotic output stabilization in nonlinear controlled systems is approached from the perspective of dynamical sliding-mode control. The proposed controller is based on Fliess's Generalized Observability Canonical Form, recently derived from the differential algebraic approach to system dynamics.     

Suboptimal feedback control for nonlinear systems

A method is presented for synthesizing suboptimal feedback control laws for nonlinear systems optimized with respect to a quadratic performance index. The proposed method allows the designer to easily calculate a second-order approximation to the optimal control.     

First-order corrections in optimal feedback control of singularly perturbed nonlinear systems

In this paper first-order correction terms, developed by using the method of matched asymptotic expansions, are incorporated in the feedback solution of a class of singularly perturbed nonlinear optimal control problems frequently encountered in aerospace applications. This improvement is based on an explicit solution of the integrals arising from the first-order matching conditions and leads to correct the initial values of the slow costate variables in the boundary layer. Consequently, a uniformly valid feedback control law, corrected to the first-order, can be synthesized. The new method is applied to an example of a constant speed minimum-time interception problem. Comparison of the zeroth- and first-order feedback control laws to the exact optimal solution demonstrates that first-order corrections greatly extend the domain of validity of the approximation obtained by singular perturbation methods.     

New approach to the optimal control of time-varying linear systems via Chebyshev series

The general expression of the shifted Chebyshev series approximation for any two arbitrary functions has been presented by Chou and Horng (1985 a). The expression is recursive and useful in time-varying and non-linear systems. Due to the property, the design of gains for the time-varying optimal control system with a quadratic performance measure is studied by directly solving the Riccati equation in this paper. The method considered is much simpler than other design techniques. An example is illustrated. Only a small number, m = 4, of Chebyshev series is needed to produce a satisfactory outcome.     

Robust optimal control during feedback disruption for nonlinear systems controlled by an observer-based output feedback controller

In this paper, a robust optimal control problem during feedback disruption is considered for a class of nonlinear systems which have been controlled by an observer-based output feedback controller. It is shown that during feedback disruption, there exists an optimal control input which keeps both system states and observer errors within a specified bound for the longest time. Then, it is shown that such an optimal control input can be practically implemented by using a bang-bang control input in terms of control performance. One numerical and one practical examples are given for clear illustration.     

Analysis and optimal control of linear time-varying systems via general orthogonal polynomials

The operational matrix consisting of the product of two time functions, and the operational matrices for forward or backward integration consisting of general orthogonal polynomials are derived, respectively, for the analysis and optimal control of linear time-varying systems with a quadratic performance measure. The present results include results obtained via Chebyshev, Legendre, Laguerre, Jacobi, Hermite and ultraspherical polynomials as special cases.     

Analysis and optimal control of time-varying linear systems via shifted Legendre polynomials

This paper extends the application of shifted Legendre polynomial expansion to time-varying systems. The extension is achieved through representing the product of two shifted Legendre series in a new shifted Legendre series. With this treatment of the product of two time functions, the operational properties of the shifted Legendre polynomials are fully applied to the analysis and optimal control of time-varying linear systems with quadratic performance index.     

Adaptive neural decentralized control for strict feedback nonlinear interconnected systems via backstepping

An approximation based adaptive neural decentralized output tracking control scheme for a class of large-scale unknown nonlinear systems with strict-feedback interconnected subsystems with unknown nonlinear interconnections is developed in this paper. Within this scheme, radial basis function RBF neural networks are used to approximate the unknown nonlinear functions of the subsystems. An adaptive neural controller is designed based on the recursive backstepping procedure and the minimal learning parameter technique. The proposed decentralized control scheme has the following features. First, the controller singularity problem in some of the existing adaptive control schemes with feedback linearization is avoided. Second, the numbers of adaptive parameters required for each subsystem are not more than the order of this subsystem. Lyapunov stability method is used to prove that the proposed adaptive neural control scheme guarantees that all signals in the closed-loop system are uniformly ultimately bounded, while tracking errors converge to a small neighborhood of the origin. The simulation example of a two-spring interconnected inverted pendulum is presented to verify the effectiveness of the proposed scheme.     

基于ISS的非线性纯反馈系统的自适应动态面控制

研究一类具有未知死区的非线性纯反馈系统的自适应控制问题．基于输入状态稳定理论和小增益定理,提出一种自适应动态面控制方案．该方案有效地减少了可调参数的数目,避免了传统后推设计中由于需要对虚拟控制反复求导而导致的计算复杂性．理论分析证明了闭环系统是半全局一致终结有界的．     

Model reduction via Chebyshev polynomials

Model reduction of a linear system characterized by transfer function is obtained by matching the Chebyshev spectra of the unit step responses of the original system and reduced model. In order to preserve the stable property of the original system, a combined method which uses conventional stable model reduction methods to determine the coefficients of the denominator polynomial of the reduced model and uses the Chebyshev spectrum matching to determine the coefficients of the numerator polynomial is proposed. Both methods are computer application oriented.     

H∞ control via measurement feedback for affine nonlinear systems

This paper summarizes some recent results on the problem of disturbance attenuation via measurement feedback, with internal stability, for an affine nonlinear system. The solution of the problem is shown to be related to the existence of solutions of a pair of Hamilton-Jacobi inequalities in n independent variables, associated with state feedback and, respectively, output-injection design.     

On the convergence of nonlinear optimal control using pseudospectral methods for feedback linearizable systems

We consider the optimal control of feedback linearizable dynamic systems subject to mixed state and control constraints. In contrast to the existing results, the optimal controller addressed in this paper is allowed to be discontinuous. This generalization requires a substantial modification to the existing convergence analysis in terms of both the framework as well as the notion of convergence around points of discontinuity. Although the nonlinear system is assumed to be feedback linearizable, the optimal control does not necessarily linearize the dynamics. Such problems frequently arise in astronautical applications where stringent performance requirements demand optimality over feedback linearizing controls. We prove that a sequence of solutions obtained using the Legendre pseudospectral method converges to the optimal solution of the continuous‐time problem under mild conditions. Published in 2007 by John Wiley & Sons, Ltd.     

Integral sliding mode based optimal composite nonlinear feedback control for a class of systems

This paper presents an optimization method of designing the integral sliding mode （ISM） based composite nonlinear feedback （CNF） controller for a class of low order linear systems with input saturation. The optimal CNF control is first designed as a nominal control to yield high tracking speed and low overshoot. The selection of all the tuning parameters for the CNF control law is turned into a minimization problem and solved automatically by particle swarm optimization （PSO） algorithm. Subsequently, the discontinuous control law is introduced to reject matched disturbances. Then, the optimal ISM-CNF control law is achieved as the sum of the optimal CNF control law and the discontinuous control law. The effectiveness of the optimal ISM-CNF controller is verified by comparing with a step by step designed one. High tracking performance is achieved by applying the optimal ISM-CNF controller to the tracking control of the micromirror.     

Successive Chebyshev pseudospectral convex optimization method for nonlinear optimal control problems

This article aims at proposing a successive Chebyshev pseudospectral convex optimization method for solving general nonlinear optimal control problems (OCPs). First, Chebyshev pseudospectral discrete scheme is used to discretize a general nonlinear OCP. At the same time, a convex subproblem is formulated by using the first-order Taylor expansion to convexify the discretized nonlinear dynamic constraints. Second, a trust-region penalty term is added to the performance index of the subproblem, and a successive convex optimization algorithm is proposed to solve the subproblem iteratively. Noted that the trust-region penalty parameters can be adjusted according to the linearization error in iterative process, which improves convergence rate. Third, the Karush–Kuhn–Tucker conditions of the subproblem are derived, and furthermore, a proof is given to show that the algorithm will iteratively converge to the subproblem. Additionally, the global convergence of the algorithm is analyzed and proved, which is based on three key lemmas. Finally, the orbit transfer problem of spacecraft is used to test the performance of the proposed method. The simulation results demonstrate the optimal control is bang-bang form, which is consistent with the result of theoretical proof. Also, the algorithm is of efficiency, fast convergence rate, and high accuracy. Therefore, the proposed method provides a new approach for solving nonlinear OCPs online and has great potential in engineering practice.     

Output feedback hierarchical control for nonlinear systems

In this paper, we address the problem of hierarchical control for nonlinear systems and design two dynamic output feedback hierarchical control laws in a semiglobal sense and in a global sense, respectively. With the controllers applied to a class of nonlinear systems, some transient and steady properties of the system output trajectories can be satisfied simultaneously. Furthermore, the corresponding result in the global sense for linear systems is naturally derived. Finally, the effectiveness of our approach is illustrated by a single‐link robot arm system. Copyright © 2017 John Wiley & Sons, Ltd.