Control of a chain of integrators subject to actuator saturation and disturbances

In this paper, we study control of a chain of integrators under actuator saturation and non‐additive disturbances. We shall show that boundedness of the states can be ensured if the disturbances are matched and integral‐bounded, misaligned and magnitude‐bounded, or a combination of the two, using either a static or a dynamic low‐gain state feedback. This result is an extension of our earlier work on double integrator. Copyright © 2011 John Wiley & Sons, Ltd.     

Leader-following control of perturbed second-order integrator systems with binary relative information

In this paper, we study the leader-following control problem for second-order integrator systems with bounded input disturbances using only binary relative position and velocity information. The leader is allowed to be dynamically evolving with a bounded acceleration which is unknown to any of the followers. With the proposed distributed controller, we prove that despite the fact that only very coarse relative information is available, robust finite-time leader-following control can be achieved by properly choosing the control gains. As an extension, robust finite-time formation control can also be achieved with the proposed control strategy using only binary relative information. Two simulation examples are provided to illustrate the results.     

Strong stabilisation and decay estimate for unbounded bilinear systems

In this article, we deal with distributed bilinear systems, where the operator of control is supposed to be unbounded in the sense that it is bounded from the state space into some extension. Then we give sufficient conditions for strong stabilisation. Also the rate of convergence of the state is estimated. An illustrating example is given.     

The fusion process of interval opinions based on the dynamic bounded confidence

In this paper, we propose a novel opinion dynamics model that is based on bounded confidence and termed interval opinion dynamics with the dynamic bounded confidence. In this opinion dynamics model, the agents express their opinions in numerical intervals, and the bounded confidences vary in a specified interval as time varies (i.e., dynamic bounded confidence). Based on several theoretical analyses of the proposed opinion dynamics, we propose conditions that are sufficient to form a consensus or fragmentations among the agents. Moreover, we also design several simulation experiments to investigate the effects of the dynamic bounded confidence and interval widths on the proposed opinion dynamics and to illustrate the differences between the proposed model and the original opinion dynamics with bounded confidence.     

An efficient algorithm for optimal control of PWA systems with polyhedral performance indices

We present an algorithm for the computation of explicit optimal control laws for piecewise affine (PWA) systems with polyhedral performance indices. The algorithm is based on dynamic programming (DP) and represents an extension of ideas initially proposed in Kerrigan and Mayne [(2003). Optimal control of constrained, piecewise affine systems with bounded disturbances. In Proceedings of the 41st IEEE conference on decision and control, Las Vegas, Nevada, USA, December], and Baoti? et al. [(2003). A new algorithm for constrained finite time optimal control of hybrid systems with a linear performance index. In Proceedings of European control conference, Cambridge, UK, September]. Specifically, we show how to exploit the underlying geometric structure of the optimization problem in order to significantly improve the efficiency of the off-line computations. An extensive case study is provided, which clearly indicates that the algorithm proposed in this paper may be preferable to other schemes published in the literature.     

Design of robust model-based controllers via parametric programming

In this paper a method is presented for deriving the explicit robust model-based optimal control law for constrained linear dynamic systems. The controller is derived off-line via parametric programming before any actual process implementation takes place. The proposed control scheme guarantees feasible operation in the presence of bounded input uncertainties by (i) explicitly incorporating in the controller design stage a set of feasibility constraints and (ii) minimizing the nominal performance, or the expectation of the performance over the uncertainty space. An extension of the method to problems involving target point tracking in the presence of persistent disturbances is also discussed. The general concept is illustrated with two examples.     

Constrained linear systems with hard constraints and disturbances: An extended command governor with large domain of attraction

This paper considers a nonlinear feedback control policy that is an extension of those provided by command governors and reference governors. As in these control approaches it applies to discrete-time linear systems with hard constraints and set bounded disturbances. The control policy retains the main properties of traditional governors, such as straightforward direct implementation and finite-settling-time response to arbitrarily specified set points. Its principal advantage over traditional governors is a significantly larger domain of attraction, that may compete in size with those obtained by dynamic programming. Connections to model predictive control are made. Numerical examples illustrate advantageous features of the proposed approach.     

Risk sensitive control of Markov processes in countable state space

In this paper we consider infinite horizon risk-sensitive control of Markov processes with discrete time and denumerable state space. This problem is solved by proving, under suitable conditions, that there exists a bounded solution to the dynamic programming equation. The dynamic programming equation is transformed into an Isaacs equation for a stochastic game, and the vanishing discount method is used to study its solution. In addition, we prove that the existence conditions are also necessary.     

A nonsmooth strict bounded real lemma

In the theory of linear H^∞ control, the strict bounded real lemma plays a critical role because it provides a connection between the stabilizing solutions to the H^∞ Riccati equations and the stability and disturbance attenuation of the closed-loop system. Nonlinear versions of the strict bounded real lemma are also important in nonlinear H^∞ control theory. In this paper, we investigate the extension of the linear strict bounded real lemma and its smooth nonlinear generalization to cases where the solutions of the associated nonlinear PDE are not necessarily differentiable.     

Distributed optimal consensus of multiple double integrators under bounded velocity and acceleration

This paper studies a distributed optimal consensus problem for multiple double integrators under bounded velocity and acceleration. Assigned with an individual and private convex cost which is dependent on the position, each agent needs to achieve consensus at the optimum of the aggregate cost under bounded velocity and acceleration. Based on relative positions and velocities to neighbor agents, we design a distributed control law by including the integration feedback of position and velocity errors. By employing quadratic Lyapunov functions, we solve the optimal consensus problem of double-integrators when the fixed topology is strongly connected and weight-balanced. Furthermore, if an initial estimate of the optimum can be known, then control gains can be properly selected to achieve an exponentially fast convergence under bounded velocity and acceleration. The result still holds when the relative velocity is not available, and we also discuss an extension for heterogeneous Euler-Lagrange systems by inverse dynamics control. A numeric example is provided to illustrate the result.     

Stochastic receding horizon control with output feedback and bounded controls

We study the problem of receding horizon control for stochastic discrete-time systems with bounded control inputs and incomplete state information. Given a suitable choice of causal control policies, we first present a slight extension of the Kalman filter to estimate the state optimally in mean-square sense. We then show how to augment the underlying optimization problem with a negative drift-like constraint, yielding a second-order cone program to be solved periodically online. We prove that the receding horizon implementation of the resulting control policies renders the state of the overall system mean-square bounded under mild assumptions. We also discuss how some quantities required by the finite-horizon optimization problem can be computed off-line, thus reducing the on-line computation.     

Simultaneous external and internal stabilization of linear systems with input saturation and non-input-additive sustained disturbances

In this paper, we study simultaneous external and internal stabilization of the linear system under input saturation and non-input additive sustained disturbances. For systems that are asymptotic null controllable with bounded control, it is shown that a nonlinear dynamic feedback controller can be designed so that the closed-loop states remain bounded for any initial condition and for two classes of sustained disturbances, and that the equilibrium in the absence of disturbances is globally asymptotically stable.     

水下机器人的神经网络自适应控制

研究了水下机器人神经网络直接自适应控制方法,采用Lyapunov稳定性理论,证明了存在有界外界干扰和有界神经网络逼近误差条件下,水下机器人控制系统的跟踪误差一致稳定有界.为了进一步验证该水控制方法的正确性和稳定性,利用水下机器人实验平台进行了动力定位实验、单自由度跟踪实验和水平面跟踪实验等验证实验.     

基于模糊干扰观测器的自适应二阶动态滑模控制

针对一类存在不确定性和外干扰的非线性系统滑模控制的抖振问题,本文首先证明了以高斯函数为隶属度函数的模糊基向量对状态向量的偏导在任意情况下有界,解决了模糊辨识同二阶动态滑模结合的关键问题.然后设计了二阶动态Terminal滑模,有效克服了滑模抖振,且能保证滑模面上的滑动在有限时间内收敛.再基于模糊干扰观测器的输出设计自适应鲁棒补偿项.基于李雅普诺夫理论证明了系统稳定性.最后将本文提出的控制方案用于近空间飞行器姿态角跟踪仿真,并分析了高阶动态滑模收敛时间增加的问题.结果表明本文提出的控制方案跟踪速度快、精度高,且有效去除了抖振.与常规Terminal滑模相比,二阶动态Terminal滑模增加的收敛时间非常有限,适于工程应用.     

Global stabilization of a class of delay discrete-time nonlinear systems via state and output feedback

This paper deals with stabilization of a class of delay discrete-time nonlinear systems through state and output feedback. We provide an explicit bounded state feedback law as an extension of the Jurdjevic-Quinn method, from nonlinear theory, to this class of systems. Next, we present a useful and systematic approach to design an observer for the same class of systems. Then, we show how the global stabilization problem via dynamic output feedback can be solved by using the two previous results. Finally, numerical examples are given to illustrate the effectiveness of the proposed design method.     

Decentralized adaptive output-feedback control for a class of nonlinear large-scale systems with unknown time-varying delayed interactions

In this paper, the authors investigate a decentralized adaptive output-feedback controller design for large-scale nonlinear systems with input saturations and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions including all states of subsystems. This point is a main contribution of this paper compared with previous output-feedback control approaches which assume that the time-delayed bounding functions only depend on measurable output variables. The bounding functions are compensated by using appropriate Lyapunov–Krasovskii functionals and the function approximation technique based on neural networks. The observer dynamic surface design technique is employed to design the proposed memoryless local controller for each subsystem. In addition, we prove that all signals in the closed-loop system are semiglobally uniformly bounded and control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.     

Suppression of bounded exogenous disturbances: A linear dynamic output controller

In the paper, we study a problem of constructing a linear dynamic output controller for suppressing bounded exogenous perturbations in a linear control system. We propose an approach based on a method of invariant ellipsoids and technique of linear matrix inequalities. A control of gyroplatform and two-mass system is given as an example.     

Neural-Network-Based Decentralized Adaptive Control for a Class of Large-Scale Nonlinear Systems With Unknown Time-Varying Delays

A decentralized adaptive methodology is presented for large-scale nonlinear systems with model uncertainties and time-delayed interconnections unmatched in control inputs. The interaction terms with unknown time-varying delays are bounded by unknown nonlinear bounding functions related to all states and are compensated by choosing appropriate Lyapunov–Krasovskii functionals and using the function approximation technique based on neural networks. The proposed memoryless local controller for each subsystem can simply be designed by extending the dynamic surface design technique to nonlinear systems with time-varying delayed interconnections. In addition, we prove that all the signals in the closed-loop system are semiglobally uniformly bounded, and the control errors converge to an adjustable neighborhood of the origin. Finally, an example is provided to illustrate the effectiveness of the proposed control system.      

Flexible Links Manipulators: from Modelling to Control

This paper relates recent results obtained in the field of modelling and control of flexible link manipulators and proposes an investigation of the problem raised by this type of systems (at least in the planar case). First, adopting the modal floating frame approach and the Newton–Euler formalism, we propose an extension of the models for control to the case of fast dynamics and finite deformations. This dynamic model is based on a nonlinear generalisation of the standard Euler–Bernoulli kinematics. Then, based on the models recalled we treat the end-effector tracking problem for the one-link case as well as for the planar multi-link case. For the one-link system, we propose two methods, the first one is based on causal stable inversion of linear non-minimum phase model via output trajectory planning. The other one is an algebraic scheme, based on the parametrization of linear differential operators. For the planar multi-link case the control law proposed is based on causal stable inversion over a bounded time domain of nonlinear non-minimum phase systems. Numerical tests are presented together with experimental results, displaying the well behaved of these approaches.     

Adaptive robust stabilization of dynamic nonholonomic chained systems

In this article, the stabilization problem is investigated for dynamic nonholonomic systems with unknown inertia parameters and disturbances. First, to facilitate control system design, the nonholonomic kinematic subsystem is transformed into a skew‐symmetric form and the properties of the overall systems are discussed. Then, a robust adaptive controller is presented in which adaptive control techniques are used to compensate for the parametric uncertainties and sliding mode control is used to suppress the bounded disturbances. The controller guarantees the outputs of the dynamic subsystem (the inputs to the kinematic subsystem) to track some bounded auxiliary signals which subsequently drive the kinematic subsystem to the origin. In addition, it can also be shown all the signals in the closed loop are bounded. Simulation studies on the control of a unicycle wheeled mobile robot are used to show the effectiveness of the proposed scheme. © 2001 John Wiley & Sons, Inc.