基于径向基函数神经网络的移动机器人多变量固定时间编队控制

针对带多不确定性的一组非完整移动机器人的编队控制收敛问题,提出了基于径向基函数神经网络的移动机器人多变量固定时间领航者-跟随者编队控制算法.RBFNN补偿了系统所受的多不确定性,并消除了鲁棒控制的抖振现象.基于固定时间理论和Lyapunov方法进行了控制算法设计,使所提出的控制方法保证了编队控制系统中的所有信号全局固定时间收敛,在任意系统初始条件下,在通过参数设计的固定时间内,使机器人编队达到期望编队.仿真结果显示了所提出算法的有效性.     

基于互补滑模控制和迭代学习控制的永磁直线同步电动机速度控制

为满足永磁直线同步电动机(PMLSM)伺服系统高速度高精度的要求,抑制不确定性对系统性能的影响,提出一种互补滑模控制(CSMC)和迭代学习控制(ILC)相结合的控制方法.该方法结合了CSMC强鲁棒性的优点和ILC跟踪精度高的特点,以CSMC中积分滑模面为基础设计新型迭代学习律,既可利用ILC对系统未建模动态进行估计,抑制端部效应、齿槽效应和摩擦力等周期不确定性的影响,又可利用CSMC减小参数变化和外部扰动等非周期不确定性对系统的影响,从而提高控制器的收敛速度和收敛精度,保证系统具有较强的速度跟踪性能.实验结果表明,该方法有效地提高了系统的动态响应能力,改善了速度跟踪精度.     

Robust vertex‐dependent H∞ and H2 estimation for stochastic linear systems

We consider linear continuous‐time systems with multiplicative noise and polytopic‐type parameter uncertainty, and we address the problems of H_∞ and H₂ filtering of these systems. These problems are solved by applying a vertex‐dependent Lyapunov function that considerably reduces the overdesign associated with the classical design that is based on a single Lyapunov function for the whole parameter range. A new approach of the Finsler lemma is used that decreases the overdesign entailed in the usual derivation of the robust estimation problem. The developed theory is also extended to the robust gain scheduling case where online measurement is used to improve the estimation. Two examples are given that demonstrate the tractability and applicability of the design methods.     

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

In this brief, this paper deals with a robust adaptive iterative learning control (ILC) problem for a flexible manipulator attached to a moving vehicle with uncertainties. To begin with, considering the infinite dimensionality of the flexible distributed parameter system, a coupled ordinary differential equation and partial differential equation model is established without any discretization. Then, it is followed by a presentation of an adaptive ILC strategy, which can drive the vehicle and joint to the desired positions based on a proportional‐derivative feedback structure with unmodeled dynamics and external disturbances. The deformation of the flexible manipulator can also be suppressed simultaneously under the proposed control laws. By using Lyapunov's direct method, the stability of the closed‐loop system is demonstrated. The simulation results are provided to illustrate the effectiveness of the proposed control laws.     

Adaptive architectures for control of uncertain dynamical systems with actuator and unmodeled dynamics

In adaptive control of uncertain dynamical systems, it is well known that the presence of actuator and/or unmodeled dynamics in feedback loops can yield to unstable closed‐loop system trajectories. Motivated by this standpoint, this paper focuses on the analysis and synthesis of multiple adaptive architectures for control of uncertain dynamical systems with both actuator and unmodeled dynamics. Specifically, we first analyze model reference adaptive control architectures with standard, hedging‐based, and expanded reference models for this class of uncertain dynamical systems and develop sufficient stability conditions. We then synthesize a robustifying term for the latter architecture and analytically show that this term can allow for a relaxed sufficient stability condition. The proposed theoretical treatments involve Lyapunov stability theory, linear matrix inequalities, and matrix mathematics. Finally, we compare the resulting sufficient stability conditions of the considered adaptive control architectures on a benchmark mechanical system subject to actuator and unmodeled dynamics.     

Centralized sliding mode frequency regulation approach for an uncertain islanded micro grid integrated with disturbance observer

This paper proposes a novel secondary frequency regulation technique for an uncertain islanded micro grid (MG). The major motivation of the work is to integrate the intrinsic robustness of the sliding mode control scheme with the disturbance observer to estimate and alleviate the unknown mismatched uncertainties caused by renewable resources and load variations. To this end, a dynamical sliding manifold is first utilized and then a control law is derived with Lyapunov's method which stabilizes the MG dynamics. Moreover, in order to ensure faster time domain responses of the closed‐loop system, we employ a power rate reaching law in our proposed control design. Thereafter, the performances of the introduced control strategy are tested on an islanded MG using MATLAB/Simulink, and robustness analysis is also carried out by considering five different case studies. Further, in contrast to the existing approaches such as robust H _∞ and robust PID control, the proposed strategy renders appealing time domain characteristics such as settling time, peak overshoot, and integral absolute frequency error.     

Quadratic Stabilization of Switched Uncertain Linear Systems: A Convex Combination Approach

We consider quadratic stabilization for a class of switched systems which are composed of a finite set of continuoustime linear subsystems with norm bounded uncertainties. Under the assumption that there is no single quadratically stable subsystem, if a convex combination of subsystems is quadratically stable, then we propose a state-dependent switching law, based on the convex combination of subsystems, such that the entire switched linear system is quadratically stable. When the state information is not available, we extend the discussion to designing an output-dependent switching law by constructing a robust Luenberger observer for each subsystem.      

A novel reliability-based topology optimization framework for the concurrent design of solid and truss-like material structures with unknown-but-bounded uncertainties

A new nonprobabilistic reliability-based topology optimization method for continuum structures with displacement constraints is proposed in this paper, in which the optimal layout consists of solid material and truss-like microstructure material simultaneously. The unknown-but-bounded uncertainties that exist in material properties, external loads, and safety displacements are considered. By utilizing the representative volume element analysis, rules of macro-micro stiffness performance equivalence can be confirmed. A solid material and truss-like microstructure material structure integrated design interpolation model is firstly constructed, in which design domain elements can be conducted to select solid material or truss-like microstructure material by a combination of the finite element method in the topology optimization process. Moreover, a new nonprobabilistic reliability measuring index, namely, the optimization feature distance is defined by making use of the area-ratio ideas. Furthermore, the adjoint vector method is employed to obtain the sensitivity information between the reliability measure and design variables. By utilizing the method of moving asymptotes, the investigated optimization problem can be iteratively solved. The effectiveness of the developed methodology is eventually demonstrated by two examples.     

Supervisory switching‐based prescribed performance control of unknown nonlinear systems against actuator failures

This article studies the fault‐tolerant control problem for unknown nonlinear strict‐feedback systems subject to actuator failures yet with dynamic redundancies. The prescribed performance control methodology is newly combined with a modification‐based supervisory switching strategy to solve the problem. To implement failure detection, the performance function is properly modified to synthesize a monitoring function to supervise the behavior of an error variable. Once a failure is detected, the current actuator is shut down and the backup actuator is switched in to execute the reconfigured control command. Compared with the existing results, (1) the postfailure and postswitching tracking performance is improved, other than uniform ultimate boundedness and (2) the dependence on extra robust control schemes (eg, adaptive or approximating structures) to deal with model uncertainties or the need to compute analytic derivatives of virtual control signals in the backstepping design is eliminated.     

Filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties

This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)‐based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns. The combination of GRBF‐based neural network and piecewise constant adaptive law relaxes hardware limitations (CPU). A filtering control law is designed to handle the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed‐loop system, the uniform performance bounds are derived for the states and control signals via comparison. To validate the theoretical findings, comparisons between the model reference adaptive control method and the proposed filtering adaptive neural network control architecture with the implementation of different sampling time are carried out.