Rate of convergence for the Legendre pseudospectral optimal control of feedback linearizable systems

Pseudospectral (PS) computational methods for nonlinear constrained optimal control have been applied to many industrial-strength problems, notably, the recent zero-propellant-maneuvering of the international space station performed by NASA. In this paper, we prove a theorem on the rate of convergence for the optimal cost computed using a Legendre PS method. In addition to the high-order convergence rate, two theorems are proved for the existence and convergence of the approximate solutions. Relative to existing work on PS optimal control as well as some other direct computational methods, the proofs do not use necessary conditions of optimal control. Furthermore, we do not make coercivity type of assumptions. As a result, the theory does not require the local uniqueness of optimal solutions. In addition, a restrictive assumption on the cluster points of discrete solutions made in existing convergence theorems is removed.     

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.     

A switching adaptive controller for feedback linearizable systems

One of the main open problems in the area of adaptive control of linear-in-the-parameters feedback linearizable systems is the computation of the feedback control law when the identification model becomes uncontrollable. The authors propose a switching adaptive control strategy that overcomes this problem. The proposed strategy is applied to nth-order feedback linearizable systems in canonical form. The closed-loop system is proved to be globally stable in the sense that all the closed-loop signals are bounded and the tracking error converges arbitrarily close to zero. No assumptions are made about the type of nonlinearities of the system, except that such nonlinearities are smooth. However, the proposed controller requires knowledge of the sign and lower bound of the input vector field     

A constrained optimal control of nonlinear systems

A new method for optimal control of nonlinear systems with input constraint is discussed in this paper. The system is optimized by minimizing a quadratic performance index. Considering this problem as a nonlinear programming problem, the necessary and sufficient conditions for optimal control are derived, which are later simplified to an integral equation. This integral equation becomes a necessary condition. The existence of a solution of this integral equation is studied, and a method of solving it is discussed. A numerical example is worked out at the end.     

Singularity for static-state feedback linearizable bilinear systems

This paper deals with the problem of singularities for a class of static-state feedback linearizable bilinear systems. For this class of nonlinear systems, the decoupling matrix is singular on an algebraic surface (which contains the origin), and this relates the static state feedback linearization to the difficult problem of the completeness of the trajectories of the closed-loop system and/or the boundedness of the feedback laws. In this work, we give a sufficient condition under which all the trajectories of the closed-loop system are complete and the used feedback law is bounded on each of these trajectories     

Exponential tracking of feedback linearizable non-linear control systems with uncertainties

This paper studies the robust output tracking problem of feedback linearizable non-linear control systems with uncertainties. Utilizing the input-output feedback linearization technique and the Lyapunov method for non-linear state feedback synthesis, a robust globally exponential output tracking controller design methodology for a broad class of non-linear control systems with uncertainties is developed. The underlying theoretical approaches are the differential geometry approach and the composite Lyapunov approach. One utilizes the parametrized coordinate transformation to transform the original non-linear system with uncertainties into a singularly perturbed model with uncertainties and the composite Lyapunov approach is then applied for output tracking. To demonstrate the practical applicability, the paper has investigated a pendulum control system.     

A second-order feedback method for optimal control computations

A second-order method for computing optimal control is described. The method computes control corrections from the state perturbations through feedback coefficients. On each successive trajectory a test is made for a conjugate point, and the algorithm automatically forms a control correction when such points occur. Unlike some earliermethods, terminal constraints of arbitrary dimensions are handled by the method. Numerical results for two cases of a non-linear example based on van der Pol's equation are included.     

Dynamic anti-windup scheme for feedback linearizable nonlinear control systems with saturating inputs

This paper proposes a dynamic compensation scheme for input-constrained feedback linearizable nonlinear systems to cope with the windup phenomenon. Given a dynamic feedback linearizing controller designed without considering its input constraint, an additional dynamic compensator is proposed to account for the constraint. This dynamic anti-windup is based on the minimization of a reasonable performance index. The proposed strategy is a nonlinear extended version of [Park, J.-K., & Choi, C.-H. (1995). Dynamic compensation method for multivariable control systems with saturating actuators. IEEE Transactions on Automatic Control, 40(9), 1635–1640] with simplified derivation of an optimization solution under relaxed assumptions. The parameter matrices and structure of the solution are explicitly decided by mathematical optimization for infinite horizon without tuning of design parameters unlike previous schemes. During input saturation, the role of the anti-windup scheme with the proposed dynamic feedback compensator is to maintain the controller states to be exactly the same as those without input saturation. The local asymptotic stability and the total stability of the resulting systems are proved. The usefulness of the proposed design method is illustrated by comparative simulations for a constrained control system.     

Increased transient performance for the adaptive control of feedback linearizable systems using multiple models

We consider a class of minimum-phase non-linear systems with large parametric uncertainties. The non-linear dynamics is assumed to be linearly parameterized in terms of the unknown parameters. A novel scheme which utilizes multiple models in a model reference adaptive control (MRAC) framework is proposed to improve the transient performance of the adaptive scheme. The proposed approach makes use of fixed models from a compact and partitioned parameter space and resets the parameter update dynamics to the model which gives a negative jump to the control Lyapunov function. The overall stability of closed loop system under the switching is preserved based on the Lyapunov approach. A simulation study is given in order to demonstrate the efficient use of the algorithm.     

Remarks on nonlinear planar control systems which are linearizable by feedback

The possibility that certain nonlinear systems can be linearized by the nonlinear feedback group has recently attracted a great deal of attention in the literature. At least for mechanical systems, the apparent ubiquity of linearizable systems is often accounted for by the claim that scalar control systems defined on R² are generically linearizable. Since there are several possible interpretations of the term ‘generic’ and since, until now, no rigorous proof of genericity for any use of the term has been offered, I thought it worthwhile to investigate in what sense, if any, this ‘folklore theorem’ is valid. Contrary to the prevailing intuition, linearizable systems fail to be dense in any topology for which evaluation of vector fields at points is a continous operation. This includes, for example, both the weak and strong C^∞-topologies.What, then, are the global properties of the class of linearizable systems? In this note, it is also shown that this class has no interior points in any reasonable, complete topology making feedback operations continuous. In particular, linearizable systems in R² are far from being structurally stable in the weak C^∞-topology. In the other hand, one of the main results presented here is that globally linearizable systems are open in the strong, or Whitney, topology. Thus, globally linearizable planar systems are structurally stable. If the well-known local conditions actually implied global linearizability, this result would be trivial. Recent work of Boothby shows, however, that global linearization is far more subtle. For this reason, in the course of the proof it is necessary to modify the existing global linearization criteria. In particular, a ‘nonvanishing’ criterion for irjectivity of smooth maps on R″, sharpening the so-called ‘ratio condition’, is presented. This result appears to be of independent interest and leads to improvements of the global linearization condition in R″, due to Hunt, Su and Meyer, and of the global non-linear observability conditions in R″, due to Kou, Elliott and Tarn.     

Adaptive control of linearizable systems

The authors give some initial results on the adaptive control of minimum-phase nonlinear systems which are exactly input-output linearizable by state feedback. Parameter adaptation is used as a technique to make robust the exact cancellation of nonlinear terms, which is called for in the linearization technique. The application of the adaptive technique to control of robot manipulators is discussed. Only the continuous-time case is considered; extensions to the discrete-time and sampled-data cases are not obvious     

Adaptive control of feedback linearizable systems: a modelling error compensation approach

An adaptive output feedback controller for single input feedback linearizable systems is proposed. The output derivatives are estimated with a state high-gain observer, and the matched uncertainties are handled using a modelling error compensation method. Compared with existing adaptive methods, this approach avoids overparameterizations yielding an (n+1)-order compensator, where n is the system dimension. Semiglobal stability is proven with the aid of existing results on the stability of controlled nonlinear systems with high-gain state observation. Copyright © 1999 John Wiley & Sons, Ltd.     

Robust control with uncertainty estimation for feedback linearizable systems: application to control of distillation columns

The goal of this paper is to describe a linearizing feedback adaptive control structure which leads to a high quality regulation of the output error in the presence of uncertainties and external disturbances. The controller consists of three elements: a nominal input–output linearizing compensator, a state observer and an uncertainty estimator, which provides the adaptive part of the control structure. In this way, the feedback controller, based on the disturbance observer, compensates for external disturbances and plant uncertainties. The effectiveness of the controller is demonstrated on a distillation column via numerical simulations. ©     

Adaptive actuator failure compensation for multivariable feedback linearizable systems

A feedback linearization‐based adaptive control scheme is developed for multivariable nonlinear systems with redundant actuators subject to uncertain failures. Such an adaptive controller contains a direct adaptive actuator failure compensator to compensate the uncertain actuator failure, a nonlinear feedback to linearize the nonlinear dynamics, and a linear feedback to stabilize the linearized system. The key new design feature is the estimation of both the failure patterns and the failure values, for direct adaptive actuator failure compensation, newly developed for multivariable feedback linearizable nonlinear systems. With direct control signal adaptation, the adaptive failure compensation design ensures closed‐loop stability and asymptotic output tracking in the presence of actuator failure uncertainties. Simulation results from an application to attitude control of a near‐space vehicle dynamic model are presented to verify the desired system performance with adaptive actuator failure compensation. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Low-sensitivity optimal feedback control for linear discrete-time systems

The problem of reducing sensitivity of discrete-time systems to parameter variations is considered. State and output feedback gains are obtained to minimize a quadratic criterion which includes sensitivity functions. Necessary conditions for optimality are derived and numerical examples are discussed.     

Adaptive fault‐tolerant control for feedback linearizable systems with an aircraft application

This paper investigates fault‐tolerant control (FTC) for feedback linearizable systems (FLSs) and its applications. The dynamic effects caused by the actuator faults on the feedback linearized model are firstly analyzed, which reveals that under actuator faults, the control input in the linearized model is affected by uncertain terms. In the framework of model reference control, the first FTC strategy is proposed as a robust controller, which achieves asymptotic tracking control of the FLS under actuator faults. A disadvantage of this strategy is that it relies on explicit information about several parameters in the actuator faults. This requirement is later relaxed by combining the robust FTC strategy with an adaptive technique to generate the adaptive FTC law, which is then improved to alleviate possible chattering of the actuator and estimation drifting of the adaptive parameter. Finally, the proposed FTC strategies are evaluated by reference command tracking control of a pendulum and an air‐breathing hypersonic vehicle under actuator faults. Simulation results demonstrate good tracking performance, which confirms effectiveness of the proposed strategies. Copyright © 2014 John Wiley & Sons, Ltd.     

Output feedback receding horizon control of constrained systems

This paper considers output feedback control of linear discrete-time systems with convex state and input constraints which are subject to bounded state disturbances and output measurement errors. We show that the non-convex problem of finding a constraint admissible affine output feedback policy over a finite horizon, to be used in conjunction with a fixed linear state observer, can be converted to an equivalent convex problem. When used in the design of a time-varying robust receding horizon control law, we derive conditions under which the resulting closed-loop system is guaranteed to satisfy the system constraints for all time, given an initial state estimate and bound on the state estimation error. When the state estimation error bound matches the minimal robust positively invariant (mRPI) set for the system error dynamics, we show that this control law is time-invariant, but its calculation generally requires solution of an infinite-dimensional optimization problem. Finally, using an invariant outer approximation to the mRPI error set, we develop a time-invariant control law that can be computed by solving a finite-dimensional tractable optimization problem at each time step that guarantees that the closed-loop system satisfies the constraints for all time.     

基于Gauss伪谱法的飞机最优目标瞄准控制

为实现战对抗时对逃逸目标的最优瞄准,提出了一种基于高斯伪谱法(GPM)的控制方法。建立了考虑敏捷性、多约束的飞机动态方程,推导了两阶段目标瞄准条件表达式,并设计优化指标,在此基础上将飞机最优瞄准概括为带约束终端时间未知的多阶最优控制问题。利用高斯伪谱法将此连续的边值最优控制问题离散并转化为等价的非线性规划(NLP)问题,通过遗传算法(GA)解算其初值,并应用序列二次规划(SQP)算法求解。仿真结果表明：所设计的控制方法能有效实现对目标的瞄准,满足武器发射条件。