In this paper, output feedback control is investigated for a class of uncertain non-affine nonlinear discrete-time systems. Feedback linearization is employed and a novel dynamic linear observer is built based on the measured output information, which can be used to estimate the unknown feedback linearization error. The proposed control is robust to the modeling errors and of great significance in engineering practice due to its simple linear control architecture. Moreover, the fix-point problem usually met in feedback linearization design for non-affine nonlinear system is solved under a loose condition. Simulation results show the effectiveness of the proposed control approach.

In this paper, output feedback control is investigated for a general class of uncertain non-affine nonlinear systems in discrete time.Control system design employs feedback linearization, coupled with a novel filter which is built to estimate the feedback linearization error.Output feedback control is then developed to stabilize the systems by utilizing the estimation.In the control design, implicit function theorem and the mean value theorem are exploited to handle the difficulty of non-affine appearance of the control input.The proposed control is of great significance in engineering practice due to its linear control architecture, high dynamic performance, clear physical meanings and robustness to the modeling errors.

In this paper, output feedback control is presented for a general class of uncertain nonaffine nonlinear systems, that does not rely on state estimation. Under the condition that only the system output is available for feedback, a dynamic linear filter is built to estimate unknown nonlinearities, and an output feedback controller is developed to stabilize the systems by utilizing the estimation to compensate for the unknown nonlinearities. One important feature of the proposed control is that the controller is developed under mild conditions with simple control algorithms, which is of great significance in engineering practice. Simulation results show the effectiveness of the control approach.

In this paper, a novel systematic design procedure is presented for a class of uncertain nonlinear systems. Such design procedure can remove the control input terms which contain the unknown nonlinearities as the control coefficients, and provides the following advantages: it not only avoids a possible singularity problem completely, but also simplifies the control design process. Moreover, the proposed design procedure can provide simple control structure under the relaxed conditions, which is easy to implement and can be applied to a wider class of systems.

In this paper, robust adaptive neural network control is investigated for a class of multi-input-multi-output (MIMO) pure-feedback nonlinear system with unknown nonlinearities. The unknown nonlinearities could be come from unmodeled dynamics, modeling errors, or nonlinear time-varying uncertainties. Based on the backstepping design technique and the universal approximation property of the neural network (NN), robust adaptive control is synthesized by employing a single NN to approximate the lumped uncertain nonlinearities. The proposed control can eliminate the circularity problem completely, and guarantees semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop and convergence of the tracking error to an arbitrarily small residual set.

In this paper, robust control design is presented for a general class of uncertain non-affine nonlinear systems.The design employs feedback linearization, coupled with two high-gain observersthe first to estimate the feedback linearization error based on the full state information; the second to estimate the unmeasured states of the system when only the system output is available for feedback.All the signals in the closed loop are guaranteed to be uniform ultimate bounded and the output of the system is proven to converge to a small neighborhood of the origin.The proposed approach not only handles the difficulty in controlling non-affine nonlinear systems, but also simplifies the stability analysis of the closed loop due to its simple control structure.

Robust control design is presented for a general class of uncertain non-affine nonlinear systems. The design employs feedback linearization, coupled with two high-gain observers: the first to estimate the feedback linearization error based on the full state information and the second to estimate the unmeasured states of the system when only the system output is available for feedback. All the signals in the closed loop are guaranteed to be uniformly ultimately bounded (UUB) and the output of the system is proven to converge to a small neighborhood of the origin. The proposed approach not only handles the difficulty in controlling non-affine nonlinear systems but also simplifies the stability analysis of the closed loop due to its linear control structure. Simulation results show the effectiveness of the approach.

This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

This paper propose output feedback control for a class of multi-input-multi-output (MIMO) uncertain nonlinear discrete-time systems. System uncertain nonlinearities are estimated online by constructing a linear disturbance observer, and output feedback control is developed based on the estimation to compensate the nonlinearities. An obvious advantage of the proposed control is that it possesses simple linear control algorithm, which is of great significance in engineering.

This paper investigates the state feedback stabilization of linear time-invariant(LTI) systems with input time delay.The existence of time delay transforms the closed-loop characteristic equation into a transcendental equation.By investigating the eigenvalues of the time-delay system,two vectors related to the system parameters and the feedback gain respectively can be obtained from the real part and the imaginary part of the transcendental equation.The amplitude and phase relations of these two vectors indicate the crossing situation of the root locus on the imaginary axis.Furthermore,the time delay is dependent on the system parameters and the feedback gain explicitly in this criterion,so that the state feedback stability controller can be designed more expediently.Finally,numerical examples illustrate the correctness and effectiveness of the proposed algorithm.

Double-linear output feedback control strategy based on model bias separation is proposed for a class of uncertain discrete-time systems.The dynamic output feedback controller is firstly designed to stabilize the nominal system(i.e.,in the case of uncertainty equals zero);the compensator is then adopted for real-time compensating the impact of uncertainty,by obtaining the model bias information online by constructing a reduced-order observer.The control strategy is adopted the linear control construction,which is convenient to realize in practical control applications with good engineering significance.The sufficient condition for exponential stability of the closed-loop has been proved.The simulation results show the effectiveness of the proposed control strategy.

Regional pole assignment is studied for a class of linear systems via static output feedback. The shortage of the optimization problem subject to a set of bilinear matrix inequalities(BMIs) is analyzed. Then the algorithm of static output feedback pole assignment based on the regional attractors is proposed. A set of attractors in the satisfactory region is determined firstly. After that, the optimization direction of the output feedback matrix can be obtained via the one-dimensional searching method of variable polling. And a feasible solution can be attained by iterative computation. Computational results are presented demonstrating the effectiveness of the provided algorithm.

Nonlinearity and uncertainty are main factors which restrict the system performance.Bilinear output feedback control strategy based on model bias separation is proposed,which is the integration of a conventional PID controller and a linear compensator.The sufficient condition for system exponential stability is proposed.The bilinear output feedback control obtains the model bias information online,and adopts the simple bilinear control structure,so it has good engineering significance.The simulation results show the effectiveness of the proposed bilinear output feedback control strategy.

For controlling the triple inverted pendulum, a bilinear control system based on model bias separation is proposed. The bilinear controller is the integration of a conventional linear controller and a linear compensator which obtains the model bias information online, and adopts the linear compensator for the nonlinearities and uncertainties. The sufficient condition for system exponential asymptotic stability has been proved. The simulation results show the effectiveness of the proposed bilinear control strategy.

This paper investigates the robust stability of uncertain linear neutral systems with time-varying discrete delay. A delay-dependent stability criterion is obtained and formulated in the form of a linear matrix inequality. Two numerical examples are given to indicate significant improvements over some existing results.

Bilinear control strategy based on model bias separation is proposed,and it is applied to the yaw control of small-scale unmanned helicopters.The system model is divided into two parts:linear model and uncertainty.The information of uncertainty is online obtained by the novel structure of model bias separation.The controller is the integration of a conventional linear controller with a linear compensator.The simulation results verify the effectiveness of the proposed bilinear control strategy.

非线性和不确定性是制约控制系统性能的主要因素,为此出现了许多方法都试图克服这些因素的影响,本文提出一种基于偏差分离的双线性控制策略,在线获取模型偏差信息,并采用简单的双线性控制结构,具有很好的工程意义,论文证明了双线性控制系统指数渐近稳定的充分条件,仿真结果表明所提出的双线性控制策略的有效性.

研究了一类具有不确定结构中立型时滞系统的鲁棒稳定性问题.通过构造Lyapunov泛函,结合线性矩阵不等式(LMI),引进适当的矩阵变量,对具有结构不确定性的中立型时滞系统的鲁棒稳定性进行了分析和研究,得到了系统鲁棒渐进稳定的充分条件.并举例说明了该结果是可行的,且具有较小的保守性.