Output feedback stabilization for uncertain systems: constrainedRiccati approach

In this paper, the problem of output feedback stabilization of linear systems with mismatched uncertainties is investigated. A new approach to the design of output feedback controller is proposed, and the respective output feedback gains are obtained through the solution of a Riccati equation with linear constraints. Solvability conditions for such a constrained Riccati problem are derived, and a systematic algorithm for obtaining an output feedback controller, which guarantees the system state is globally exponentially stable, is provided     

Anti-windup compensator synthesis for systems with saturation actuators

In this paper we synthesize linear and nonlinear output feedback dynamic compensators for plants with saturating actuators. Our approach is direct in the sense that it accounts for the saturation nonlinearity throughout the design procedure as distinct from traditional design techniques that first obtain a linear controller for the ‘unsaturated’ plant and then employ controller modification. We utilize fixed-structure techniques for output feedback compensation while specifying the structure and order of the controller. In the full-order case the controller gains are given by LQG-type Riccati equations that account for the saturation nonlinearity.     

A new static output feedback approach to the suboptimal mixed H2/H∞ problem

In this paper we propose a new approach to solve the static output feedback suboptimal mixed H₂/H_∞ control problem using a state fixed‐structure feedback design. We formulate the static output feedback problem as a constrained static state feedback problem and obtain three coupled design equations: one Riccati equation, one Lyapunov equation, and a gain equation. We will prove the equivalence of the proposed solution to the existing solution. A very simple iterative algorithm is then presented to solve the design equations for the stabilizing output feedback gain that minimizes an upper bound of H₂ norm while satisfying the H_∞ disturbance attenuation requirement. A unique feature of the new approach is that it admits the Kalman gain as an initial stabilizing gain to start the above iterative solution procedure, which is computationally attractive and advantageous compared to the direct approach, as the latter has to deal with the difficult algorithm initialization problem. Some illustrative numerical examples are given to demonstrate the effectiveness of the approach. Copyright © 2004 John Wiley & Sons, Ltd.     

Robust output feedback control of polynomial growth nonlinear systems with measurement uncertainty

Even in the presence of uncertainty in both state and output equations, we prove that global asymptotic stabilization is still possible by output feedback for a family of uncertain nonlinear systems dominated by a triangular system with a polynomial output‐dependent growth rate. In contrast to the linear growth requirement in the recent work the nonlinear perturbations in this paper are allowed to satisfy a linear growth condition with a polynomial output‐dependent rate. To handle simultaneously the polynomial nonlinearities and unknown parameter in the system output, we propose a high‐gain estimator with a dynamic gain that is updated online through a Riccati‐type dynamic equation. Then, an estimator‐based controller is designed by a recursive algorithm that makes it possible to assign the controller gains step by step. The globally stabilizing output‐feedback controller developed in this paper is robust with respect to uncertainties in the system dynamics and output equations.     

Hybrid static output feedback stabilization of two-dimensional LTI systems: a geometric method

For two-dimensional linear time-invariant (LTI) systems which are not stabilizable via a single static output feedback, we propose a hybrid stabilization strategy based on a geometric method. More precisely, we design two static output feedback gains and a switching law between the feedback gains so that the entire closed-loop system is asymptotically stable. The proposed switching law is composed of output-dependent switching and time-controlled switching. We demonstrate the hybrid control method with various examples for different cases.     

Design of robust-optimal output feedback controllers for linear uncertain systems using LMI-based approach and genetic algorithm

This paper considers the robust-optimal design problems of output feedback controllers for linear systems with both time-varying elemental (structured) and norm-bounded (unstructured) parameter uncertainties. Two new sufficient conditions are proposed in terms of linear-matrix-inequalities (LMIs) for ensuring that the linear output feedback systems with both time-varying elemental and norm-bounded parameter uncertainties are asymptotically stable, where the mixed quadratically-coupled parameter uncertainties are directly considered in the problem formulation. A numerical example is given to show that the presented sufficient conditions are less conservative than existing ones reported recently. Then, by integrating the hybrid Taguchi-genetic algorithm (HTGA) and the proposed LMI-based sufficient conditions, a new integrative approach is presented to find the output feedback controllers of the linear systems with both time-varying elemental and norm-bounded parameter uncertainties such that the control objective of minimizing a quadratic integral performance criterion subject to the stability robustness constraint is achieved. A design example of the robust-optimal output feedback controller for the AFTI/F-16 aircraft control system with the time-varying elemental parameter uncertainties is given to demonstrate the applicability of the proposed new integrative approach.     

An iterative Q‐learning scheme for the global stabilization of discrete‐time linear systems subject to actuator saturation

In this paper, we propose a model‐free algorithm for global stabilization of linear systems subject to actuator saturation. The idea of gain‐scheduled low gain feedback is applied to develop control laws that avoid saturation and achieve global stabilization. To design these control laws, we employ the framework of parameterized algebraic Riccati equations (AREs). Reinforcement learning techniques are developed to find the solution of the parameterized ARE without requiring any knowledge of the system dynamics. In particular, we present an iterative Q‐learning scheme that searches for a low gain parameter and iteratively solves the parameterized ARE using the Bellman equation. Both state feedback and output feedback algorithms are developed. It is shown that the proposed scheme achieves model‐free global stabilization under bounded controls and convergence to the optimal solution of the ARE is achieved. Simulation results are presented that confirm the effectiveness of the proposed method.     

Computation of optimal output feedback gains for linear multivariable systems

For linear systems with quadratic performance indices, it is shown that the optimal output feedback gains can be computed using gradient techniques. Unlike previous algorithms, this approach avoids the solution of nonlinear matrix equations while appearing to ensure convergence. Computational results for a fourth-order system are presented.     

Feedback gain optimization in decentralized eigenvalue assignment

This paper develops a new design procedure for minimizing the norm of a decentralized output feedback matrix which assigns a user specified set of eigenvalues. Two distinct approaches which can be meshed together as a unified design tool are derived. Assuming one has computed a decentralized feedback matrix which assigns a desired spectrum, the first approach describes an iterative algorithm which reduces an algebraic cost function (e.g. the Frobenius norm) of the feedback gains while maintaining the desired spectrum. This algorithm allows for small movements in the eigenvalues. The iteration step is based on the first order variational behaviour of the eigenvalue-eigenvector equations. The second algorithm modifies a continuation method for decentralized eigenvalue assignment to include an optimizing factor. Numerical considerations for the design procedures are discussed. An example showing the improvement possible by the application of the procedure is also given.     

Output feedback of Markov jump linear systems with no mode observation: An automotive throttle application

The note presents an output feedback control strategy for Markov jump linear systems with no mode observation. Based on minimizing a finite‐time quadratic cost, we derive an algorithm that generates output feedback gains that satisfy a necessary optimality condition. These gains can be computed off‐line relying only on the initial condition of the system. This result expands a previous one from the literature that considered state‐feedback only. To illustrate the usefulness of the approach, real‐time laboratory experiments were performed to control an automotive electronic throttle valve subject to Markov‐driven voltage fluctuations. Copyright © 2015 John Wiley & Sons, Ltd.     

基于反步法的耦合分数阶反应扩散系统边界输出反馈控制

针对具有空间依赖耦合系数的分数阶反应扩散系统,利用反步法设计了基于观测器的边界输出反馈控制器,证明了观测增益和控制增益核函数矩阵方程的适定性.针对误差系统和输出反馈的闭环系统,利用分数阶Lyapunov方法分析了系统的Mittag-Leffler稳定性,且利用Wirtinger不等式改进了耦合系统稳定的条件.当系统具有空间依赖的耦合系数时,难以求得控制增益和观测增益核函数的解析解,为此,给出了核函数偏微分方程的数值解方法.数值仿真验证了理论结果.     

Robust consensus control with guaranteed rate of convergence using second-order Hurwitz polynomials

This paper considers homogeneous networks of general, linear time-invariant, second-order systems. We consider linear feedback controllers and require that the directed graph associated with the network contains a spanning tree and systems are stabilisable. We show that consensus with a guaranteed rate of convergence can always be achieved using linear state feedback. To achieve this, we provide a new and simple derivation of the conditions for a second-order polynomial with complex coefficients to be Hurwitz. We apply this result to obtain necessary and sufficient conditions to achieve consensus with networks whose graph Laplacian matrix may have complex eigenvalues. Based on the conditions found, methods to compute feedback gains are proposed. We show that gains can be chosen such that consensus is achieved robustly over a variety of communication structures and system dynamics. We also consider the use of static output feedback.     

Receding horizon output feedback control for constrained uncertain systems using periodic invariance

In this article, we consider a receding horizon output feedback control (RHOC) method for linear discrete-time systems with polytopic model uncertainties and input constraints. First, we derive a set of estimator gains and then we obtain, on the basis of the periodic invariance, a series of state feedback gains stabilising the augmented output feedback system with these estimator gains. These procedures are formulated as linear matrix inequalities. An RHOC strategy is proposed based on these state feedback and state estimator gains in conjunction with their corresponding periodically invariant sets. The proposed RHOC strategy enhances the performance in comparison with the case in which static periodic gains are used, and increases the size of the stabilisable region by introducing a degree of freedom to steer the augmented state into periodically invariant sets.     

Robust output regulation problem for linear time-delay systems

In this paper, we study the robust output regulation problem for linear systems with input time-delay. By extending the internal model design method to linear time-delay systems, we have established solvability conditions for the problem by both dynamic state feedback control and dynamic output feedback control. The advantages of internal model approach over the feedforward design approach are that it can handle perturbations of the uncertain parameters in the plant and the control law, and it does not need to solve the regulator equations.     

Homogeneous observers, iterative design, and global stabilization of high-order nonlinear systems by smooth output feedback

We study the problem of global stabilization by smooth output feedback, for a class of n-dimensional homogeneous systems whose Jacobian linearization is neither controllable nor observable. A new output feedback control scheme is proposed for the explicit design of both homogeneous observers and controllers. While the smooth state feedback control law is constructed based on the tool of adding a power integrator, the observer design is new and carried out by developing a machinery, which makes it possible to assign the observer gains one-by-one, in an iterative manner. Such design philosophy is fundamentally different from that of the traditional "Luenberger" observer in which the observer gain is determined by observability. In the case of linear systems, our design method provides not only a new insight but also an alternative solution to the output feedback stabilization problem. For a class of high-order nonhomogeneous systems, we further show how the proposed design method, with an appropriate modification, can still achieve global output feedback stabilization. Examples and simulations are given to demonstrate the main features and effectiveness of the proposed output feedback control schemes.     

Robust linear output feedback controller for autonomous landing of a quadrotor on a ship deck

Ship deck landing control of a quadrotor requires certain robustness with respect to ship heave motion. Typical systems only provide relative height, therefore do not have relative heave rate information. In this paper, a linear output feedback control consisting of a full state feedback controller and a Luenberger observer is formulated. Invariant ellipsoid method is used to formulate an estimation of a bound on the response of a linear output feedback-controlled system subjected to external disturbances and measurement noise. The gains that result in a minimum bound are optimized using a gradient descent iterative approach proposed in this paper where the invariant ellipsoid condition is linearized into a tractable LMI condition. This approach is applied to a simulation of a quadrotor landing on a ship deck and results are compared with other gains. The gains selected using the proposed approach exhibits improved robustness to external disturbances and measurement noise.     

The use of model following methods to generate suboptimal feedback control†

Implementation of linear optimal control laws oftentimes requires that all system state variables be physically available for measurement and feedback. The well known impracticality of this requirement motivates the work described in this paper which is addressed to the use of model following methods in the synthesis of sub-optimal feedback controllers which do not require sensing all of the system state variables. The approach presented here consists of first computing a complete set of optimal feedback gains and thus generating optimum system response which is then used as a model toward which a suboptimal design can strive. A suboptimal control is postulated with zero feedback gains as desired, the remaining gains being as yet arbitrary. The average integrated difference between the model closed loop response and the suboptimal closed loop response is then minimized by selection of these remaining feedback gains. For a given feedback configuration, the method thus determines the suboptimal closed loop control yielding a response which is as close as possible in a given well-defined sense to the model response. Defining equations are derived and parameter optimisation methods are utilized to compute the free feedback gains. The method is particularly well suited to digital computation requiring only the use of standard parameter optimization algorithms as well as an algorithm to solve a set of bilinear matrix equations. An example involving regulation of the longitudinal modes of a VTOL flight vehicle is presented to illustrate the model following capabilities of the synthesis procedure.     

Design of an Optimal Fuzzy Sliding Mode Control using Scalar Sign Function

The purpose of this paper is to present a new design for an optimal fuzzy sliding mode control based on a modified parallel distributed compensator and using a scalar sign function method. The proposed fuzzy sliding mode control uses a modified parallel distributed compensator scheme to find the optimal gains. To do this, the control gains are not considered constant through the linearized subsystem. Among these, we find state feedback gains, which are determined in offline mode using some prescribed performance criteria. Moreover, the fuzzy sliding surface of the system is designed using a stable eigenvector and the scalar sign function. The advantages of the proposed design are minimum energy control effort, faster response, and zero steady‐state error. We analyze and test the performance and stability of the new optimal fuzzy sliding mode control using simulation results that show that the proposed approach is very effective.     

Global output feedback regulation of nonlinear time delay systems subject to sensor measurement faults

This article studied the global output feedback regulation problem for a class of uncertain nonlinear time delay systems subject to unknown measurement faults on sensors. Different from the existing works, we consider the unknown time‐varying delays on the system states and relax their conservative condition on nonlinear functions. By introducing two novel time‐varying gains, a new global output feedback regulation algorithm is proposed, which ensures control parameters can be chosen flexibly. The proposed linear‐like controller is independent of the unknown time‐varying delays. Moreover, it has a simple structure, which is convenient for the implementation in practice. Based on the Lyapunov stability theory, it is strictly proved that all signals of the resulting closed‐loop system are globally bounded with the designed controller. Finally, a simulation example is presented to illustrate the effectiveness of the proposed output feedback regulation algorithm.     

Frequency-domain subsystem identification with application to modeling human control behavior

We present a frequency-domain subsystem identification algorithm that identifies unknown feedback and feedforward subsystems that are interconnected with a known subsystem. This method requires only accessible input and output measurements, applies to linear time-invariant subsystems, and uses a candidate-pool approach to ensure asymptotic stability of the identified closed-loop transfer function. We analyze the algorithm in the cases of noiseless and noisy data. The main analytic result of this paper shows that the coefficients of the identified feedback and feedforward transfer functions are arbitrarily close to the true coefficients if the data noise is sufficiently small and the candidate pool is sufficiently dense. This subsystem identification approach has application to modeling the control behavior of humans interacting with and receiving feedback from a dynamic system. We apply the algorithm to data from a human-in-the-loop experiment to model a human’s control behavior.