Design of 2-D stable analog and recursive digital filters using properties of the derivative of even or odd Parts of Hurwitz polynomials

In this paper, a method for the design of 2-D analog and recursive digital filters is presented. Starting from a structure in the analog domain, suitable even or odd parts of two-variable Hurwitz polynomials are generated. This enables 2-variable very strictly Hurwitz polynomials (VSHP) to be obtained,² thus avoiding non-essential singularities of the second kind. Thus it will ensure a stable 2-D recursive digital filter obtained by the use of bilinear transformations. Examples are given to illustrate the method.     

A special class of multiplierless digital filters

This paper deals with the design of discrete low-pass filters, using a special class of polynomials of the second and third order, with coefficients which are powers of two. To take advantage of the characteristics of these polynomials, the discrete integrator is used as a basic component. The filters so derived are multiplierless resulting in hardware economy. Simulation indicates that the filters obtained are equally good or better than those obtained by the application of the impulse invariance method.     

A dynamic output feedback control law for elastic joint robots via feedback-passivity approach

Motivated by recent dynamic output feedback passivation results, a new set-point control law is presented for an elastic joint robot when the velocity measurements are not available. The proposed methodology designs an additional dynamics with which the parallel-connected system is feedback passive. That is, the composite nonlinear robot system has relative degree one with a new output and its zero-dynamics subsystem becomes the virtual closed-loop system with a simple proportional-derivative (PD) control law. This approach provides an alternative way of replacing the role of the velocity measurements for the PD law. With the proposed control law, the transfer function of the additional system has the form of sG(s) with a strictly positive real (SPR) G(s). Robustness analysis is also given with regard to uncertainties on the robot parameters. The performance of the proposed control law is illustrated in the simulation studies of a manipulator with three revolute elastic joints.     

Non-fragile guaranteed cost control for uncertain stochastic nonlinear time-delay systems

This paper deals with the problem of non-fragile guaranteed cost control for a class of uncertain stochastic nonlinear time-delay systems. The parametric uncertainties are assumed to be time-varying and norm bounded. The time-delay factors are unknown and time-varying with known bounds. The aim of this paper is to design a memoryless non-fragile state feedback control law such that the closed-loop system is stochastically asymptotically stable in the mean square for all admissible parameter uncertainties and the closed-loop cost function value is not more than a specified upper bound. A new sufficient condition for the existence of such controllers is presented based on the linear matrix inequality (LMI) approach. Then, a convex optimization problem is formulated to select the optimal guaranteed cost controller which minimizes the upper bound of the closed-loop cost function. Numerical example is given to illustrate the effectiveness of the developed techniques.     

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.     

Boundary stabilization and state estimation of ODE-transport PDE with in-domain coupling

This paper deals with the exponential stabilization of first order ODE-transport PDE coupled at the boundary point. A state feedback boundary control law has been formulated with the help of the backstepping method. The main novelty of this paper is that the stabilization of the coupled system is discussed by Lyapunov theory and the appropriate observer gain is designed by using the linear matrix inequalities (LMIs). An anti-collocated observer design for the corresponding dual system is also presented. The state feedback boundary controller, observer design and the stabilization of the closed-loop system are discussed in detail with illustrative numerical examples.     

A combined time and frequency domain method for model reduction of discrete systems

A new combined time and frequency domain method for the model reduction of discrete systems in z-transfer function is presented. First, the z-transfer functions are transformed into the w-domain by the bilinear transformation, z = (1+w)/(1?w). Then, four model reduction methods—Routh approximation, Hurwitz polynomial approxima- tion, stability equation, and retaining dominant poles—are used respectively to reduce the order of the denominator polynomials in the w-domain. Least squares estimate is then used to find the optimal coefficients in the numerator polynomials of the reduced models so that the unit step response errors are reduced to a minimum. The advantages of the proposed method are that both frequency domain and time domain characteristics of the original systems can be preserved in the reduced models, and the reduced models are always stable provided the original models are stable.     

Stabilization of positive Takagi–Sugeno fuzzy discrete-time systems with multiple delays and bounded controls

This paper deals with the problem of stabilization by state feedback control of Takagi–Sugeno (T–S) fuzzy discrete-time systems with multiple fixed delays while imposing positivity in closed-loop. The obtained results are presented under linear programming (LP) form. In particular, the synthesis of state feedback controllers is first solved in terms of Linear programming for the unbounded controls case. This result is then extended to the stabilization problem by nonnegative controls, and stabilization by bounded controls. The stabilization conditions are derived using the single Lyapunov–Krasovskii functional (LKF). An example of a real plant is studied to show the advantages of the design procedure. A comparison between linear programming and LMI approaches is presented.     

Stabilization and passification of distributed-order fractional linear systems using methods of preservation

This paper studies the stabilization and passification of a class of distributed-order linear time-invariant systems, by using methods of preservation in the frequency domain. Results about preservation of stability and passivity of classical linear time-invariant systems are extended to one more general family of matrix functions. Based on these results, a new approach to the problems of stabilization and passification of distributed-order linear time-invariant systems is presented. Also a result that extends the known techniques for pole placement of classical linear time-invariant systems to the new class of distributed-order linear time-invariant systems is given. Examples are given to show the validity of theoretical results.     

Adaptive output feedback control of large-scale nonlinear time-delay systems with uncertain output equations

For a class of large-scale nonlinear time-delay systems with uncertain output equations, the problem of global state asymptotic regulation is addressed by output feedback. The class of systems under consideration are subject to feedforward growth conditions with unknown growth rate and time delays in inputs and outputs. To deal with the system uncertainties and the unknown delays, a novel low-gain observer with adaptive gain is firstly proposed; next, an adaptive output feedback delay-free controller is constructed by combining Lyapunov-Krasovskii functional with backstepping algorithm. Compared with the existing results, the controllers proposed are capable of handling both the uncertain output functions and the unknown time delays in inputs and outputs. With the help of dynamic scaling technique, it is shown that the closed-loop states converge asymptotically to zero, while the adaptive gain is bounded globally. Finally, the effectiveness of our control schemes are illustrated by three examples.     

Modified Realization Technique for Digital Filters Derived from Analog Counterparts

A simple realization scheme for one-dimensional and two-dimensional recursive digital filters derived from analog reference transfer functions is presented. The method is based on proper predistortion of the analog transfer function to obtain a new Hurwitz polynomial. Analog-to-digital transformations, such as the bilinear transformation, are then applied to the resulting predistorted reference transfer function to obtain the discrete version of the system. It is shown that a proper choice of the predistorting function will yield digital realizations which are free of the delay-free loops and in most cases are near minimal. To illustrate the simplicity and efficiency of the technique, examples of 1-D and 2-D cases are worked out. The proposed scheme can readily be extended to include the multi-dimensional case.     

Lyapunov-based design of locally collocated controllers for semi-linear parabolic PDE systems

The design problem of collocated feedback controllers is addressed in this paper for a class of semi-linear distributed parameter systems described by parabolic partial differential equation (PDE), where a finite number of local actuators and sensors are intermittently distributed in space. A Lyapunov direct method for the exponential stability analysis of the resulting closed-loop system is first presented for the system, in which the first mean value theorem for integration and the Wirtinger's inequality are employed. The corresponding stabilization condition is then derived through the analysis result. Finally, the proposed design method is implemented on the feedback control of a fisher equation and its effectiveness is evaluated through simulation results.     

Observer-based boundary control of semi-linear parabolic PDEs with non-collocated distributed event-triggered observation

This paper deals with the problem of boundary control for a class of semi-linear parabolic partial differential equations (PDEs) with non-collocated distributed event-triggered observation. A semi-linear Luenberger PDE observer with an output error based event-triggering condition is constructed by using the event-triggered observation to exponentially track the PDE state. By the estimated state, a feedback controller is proposed. It has been shown by the Lyapunov technique, and a variant of Poincaré–Wirtinger inequality that the resulting closed-loop coupled PDEs is exponentially stable if a sufficient condition presented in terms of standard linear matrix inequality (LMI) is satisfied. Moreover, a rigorous proof is provided for existence of a minimal dwell-time between two triggering times. Finally, numerical simulation results are given to show the effectiveness of the proposed design method.     

Design of optimal strictly positive real controllers using numerical optimization for the control of flexible robotic systems

The design of optimal strictly positive real (SPR) controllers using numerical optimization is considered. We focus on how to parameterize the SPR controllers being optimized and the effect of parameterization. Minimization of the closed-loop H₂-norm is the optimization objective function. Various single-input single-output and multi-input multi-output controller parameterizations using transfer functions/matrices and state–space equations are considered. Depending on the controller form, constraints are enforced (i) using simple inequalities guaranteeing SPRness, (ii) in the frequency domain or, (iii) by implementing the Kalman–Yakubovich–Popov Lemma. None of the parameterizations we consider foster an observer-based controller structure. Simulated control of a single-link and a two-link flexible manipulators demonstrates the effectiveness of our proposed controller optimization formulations.     

Memoryless parameter-dependent control strategy of stochastic strict-feedback time delay systems

For a class of stochastic strict-feedback nonlinear systems subject to different time delay states, this paper mainly concerns the problem of global asymptotic stabilization. Two new control strategies that the memoryless parameter-dependent state feedback control and the memoryless parameter-dependent output feedback control are taken into consideration, respectively. By skillfully constructing the Lyapunov-Krasovskii (L-K) functional, taking the proper determined parameter and employing the stochastic nonlinear time delay system (SNTDS) stability theory, the global asymptotic stability of the stochastic closed-loop system can be achieved. The proposed output feedback control scheme is finally utilized for the control design of the one-link manipulator system and two-stage chemical reactor system, which can verify the availability of the control approach.     

Fault-tolerant practical tracking control for uncertain robotic system with general reference trajectory and output constraint

This paper is devoted to the fault-tolerant tracking control for a class of uncertain robotic systems under time-varying output constraints. Notably, both actuator fault and the disturbances are present while all the dynamic matrices are not necessarily to be parameterized by unknown parameters or have known nominal parts, and moreover, the reference trajectories as well as the output constraints functions are not necessarily twice continuously differentiable without any time derivatives of them being available for feedback. These remarkable characteristics greatly relax the corresponding assumptions of the related literature and in turn to bring the ineffectiveness of the traditional schemes on this topic. For this, a powerful adaptive control methodology is established by incorporating adaptive dynamic compensation technique into the backstepping framework based on Barrier Lyapunov functions. Then, an adaptive state feedback controller with the smart choices of adaptive law and virtual controls is designed which guarantees that all the states of the closed-loop system are bounded and the system output practically tracks the reference trajectory while not violates the output constraints.     

Adaptive neural network decentralized stabilization for nonlinear large scale interconnected systems with expanding construction

A backstepping-based adaptive neural network decentralized stabilization approach is presented for the expanding construction of a class of nonlinear large scale interconnected systems in this paper. The expanding construction of large scale interconnected systems is to add some new subsystems into the original system during the operation of the original system. For stabilization of the expanding system, it is more realistic to keep the decentralized control laws of the original subsystems unchanged. And the decentralized control laws of the new subsystems must be designed to stabilize both itself and the resultant large scale system. In this paper, neural networks are used to approximate the unknown nonlinear functions in the new subsystems and the unknown nonlinear interconnection functions. The decentralized control laws and the parameter adaptive laws of the new subsystems are designed by using backstepping technique for the expanding construction of the large-scale interconnected system. Based on Lyapunov stability theory, the uniform and ultimate boundedness of all signals in the closed-loop system is proved. Two illustrative examples show the feasibility of the presented approach.     

随机时滞非线性系统状态反馈镇定

针对一类严格反馈随机时滞非线性系统，提出了一种状态反馈镇定方案。在系统非线性函数满足线性增长条件的假设下，基于反推技术和占优方法设计了一个无记忆线性状态反馈控制器。通过构建一个四次Lyapunov-Krasoviskii泛函，证明了闭环系统在概率意义下全局渐近稳定，仿真实例说明了方案的可行性。     

一类不确定时延非线性大系统的分散控制

本文研究了一类具有关联延迟和系统参数不确定的非线性大系统的分散控制问题,系统的匹配/非匹配不确定参数范数有界。首先基于状态观测器设计时延独立的动态输出反馈控制律，并根据 稳定性理论推导并证明了在该控制律作用下系统稳定的充分条件。最后给出一个数值例子来说明本文结果的可行性，仿真结果表明设计出的控制器不仅使得闭环系统稳定而且保证系统不受参数不确定和时延的影响。