Tracking control of mechanical systems via sliding Lagrangian

According to a classical theorem in mathematical physics, a mechanical system is completely defined by its Lagrangian. This property is utilized to design the tracking control of manipulators via a new family of sliding surfaces referred to as sliding Lagrangian surfaces and which exhibit some interesting features. They are physically meaningful. They create forces of reaction of low magnitude. They are intimately related to the second variation of the mechanical Lagrangian, in such a manner that one can refine the design by using a neighbouring-optimal control involving linear dynamic and quadratic cost. One can also combine the approach with the variable structure technique to achieve a good robustness with a low chattering phenomenon.     

Haptic force control based on impedance/admittance control aided by visual feedback

This paper demonstrates a haptic device for interaction with a virtual environment. The force control is added by visual feedback that makes the system more responsive and accurate. There are two popular control methods widely used in haptic controller design. First, is impedance control when user motion input is measured, and then, the reaction force is fed back to the operator. The alternative method is admittance control, when forces exerted by user are measured and motion is fed back to the user. Both, impedance and admittance control are also basic ways for interacting with a virtual environment. In this paper, several experiments were performed to evaluate the suitability of force-impedance control for haptic interface development. The difference between conventional application of impedance control in robot motion control and its application in haptic interface development is investigated. Open loop impedance control methodology is implemented for static case and a general-purpose robot under open loop impedance control was developed as a haptic device, while a closed loop model based impedance control was used for haptic controller design in both static and dynamic case. The factors that could affect to the performance of a haptic interface are also investigated experimentally using parametric studies. Experimental results for 1 DOF rotational motion and 2 DOF planar translational motion systems are presented. The results show that the impedance control aided by visual feedback broaden the applicability of the haptic device and makes the system more responsive and accurate.

Tracking in nonlinear differential-algebraic control systems with applications to constrained robot systems

We consider the design of a feedback control law for control systems described by a class of nonlinear differential-algebraic equations so that certain desired outputs track given reference inputs. The nonlinear differential-algebraic control system being considered is not in state variable form. Assumptions are introduced and a procedure is developed such that an equivalent state realization of the control system described by nonlinear differential-algebraic equations is expressed in a familiar normal form. A nonlinear feedback control law is then proposed which ensures, under appropriate assumptions, that the tracking error in the closed loop differential-algebraic system approaches zero exponentially. Applications to simultaneous contact force and position tracking in constrained robot systems with rigid joints, constrained robot systems with joint flexibility, and constrained robot systems with significant actuator dynamics are discussed.     

基于RBF神经网络的轮式移动机器人轨迹跟踪控制

针对一类非完整移动机器人的轨迹跟踪控制系统,提出一种基于RBF神经网络的滑模控制与转矩控制相结合的智能控制方法。该方法同时考虑机器人运动学和动力学模型,通过RBF神经网络进行移动机器人运动过程学习,与速度误差结合构成力矩控制器,可保证闭环误差系统一致最终渐进稳定。采用基于李亚普诺夫(Lyapunov)稳定性理论的判稳方法,证明整个闭环控制系统的稳定性。仿真结果表明,该控制方案具有较强的鲁棒性。     

基于BP神经网络的腱驱动灵巧手的自适应阻抗控制

考虑到机器人手指与物体接触以及外部扰动的情况,提出一个基于BP神经网络的自适应阻抗控制器。这种修正策略通过BP神经网络和阻抗控制结合完成,保证了系统的稳定性,并且提高系统对灵巧手指与外界工作环境接触时,接触力的自适应能力。实验结果表明,在存在外部扰动的情况下,所设计的基于BP神经网络的自适应阻抗控制器具有较好的控制效果。     

基于动态参数的移动机器人轨迹跟踪控制

针对移动机器人轨迹跟踪控制问题,建立了机器人运动学模型,设计了基于Lyapunov稳定理论的轨迹跟踪控制器,该控制器的性能取决于其参数的取值.采用人工神经网络来动态地调解参数的大小,使控制器获得最优的性能.粒子群优化算法具有收敛速度快,需要调节的参数少等优点,但优化过程中容易发生早熟收敛,使优化陷入局部极小值.通过引入模拟退火算法、交叉算子和变异算子,设计了一种改进的粒子群优化算法,对人工神经网络的参数进行优化计算.最后,仿真计算结果表明了该方法的有效性.     

Geometric properties of linearizable control systems

In this paper the distributions associated with a non-linear system of the form $$\frac{{dx}}{{dt}} = f(x) + \sum\limits_{\alpha = 1}^m {u_\alpha (t)g_\alpha (x)} ,f(0) = 0andx \in U_0 \subset R^n$$ are studied in relation to nonlinear state feedback $$u(x,v) = \hat a(x) + \hat S(x)v$$ withâ, u, v ∈ R ^m and ? a nonsingularm×m matrix withâ, ? functions ofx. Bothf andg are vector fields onU ₀, generally assumed to be real analytic. Two nested families of distributions {G ^j } and {M ^j } associated with the system are examined with emphasis on generic points ofU ₀, where it is shown that the usual conditions for feedback linearizability contain some redundancy. A characterization of state linearizability in terms of invariant factors of the equivalent linear form are given, and a criterion in terms of the distributions for a type of partial linearization is found.     

基于PC的张力控制系统

张力控制是造纸、塑料、纺织工业等卷绕自动化生产线中的关键技术。本文从墙纸生产过程的实际出发，研制了一套基于PC张力控制系统。经过实际生产运行证明，该系统张力控制稳定，实时性好，运行可靠。实现了墙纸生产过程的最优控制。     

Fundamental properties of reset control systems

Reset controllers are linear controllers that reset some of their states to zero when their input is zero. We are interested in their feedback connection with linear plants, and in this paper we establish fundamental closed-loop properties including stability and asymptotic tracking. This paper considers more general reset structures than previously considered, allowing for higher-order controllers and partial-state resetting. It gives a testable necessary and sufficient condition for quadratic stability and links it to both uniform bounded-input bounded-state stability and steady-state performance. Unlike previous related research, which includes the study of impulsive differential equations, our stability results require no assumptions on the evolution of reset times.     

基于能量转换的仿人机器人摆动腿落地改进阻抗控制方法

针对没有缓冲装置的双足仿人机器人在快速运动中摆动腿落地会对机器人身体稳定性带来影响的问题,结合阻抗控制的控制特点,从能量转换的角度进行分析,提出了基于能量转换的仿人机器人摆动腿落地改进阻抗控制方法。以阻抗控制方法为基础,通过对机器人摆动腿落地时末端速度以及落地后质心速度的分析,结合在摆动腿落地这一过程中能量的转换关系,对机器人摆动腿各关节的力进行控制,从而达到机器人摆动腿缓冲落地的目的。最后,将此方法应用于机器人Nao,实验证明,机器人摆动腿落地时与地面的瞬时作用力减小,达到了缓冲落地的效果,同时也起到了预先控制的作用。     

基于神经网络的自适应模糊控制系统

针对啤酒发酵过程中罐内温度控制问题,研究神经网络对模糊控制规则的优化方法,利用径向基函数神经网络对模糊控制规则进行优化,提高其自适应能力。以啤酒生产过程中主发酵阶段的数据作为输入样本,通过径向基函数神经网络进行学习训练,校正模糊控制规则,优化模糊控制器。优化前与优化后响应特性曲线的比较结果表明, RB F神经网络学习能力强,收敛速度快;模糊控制规则的完备性和一致性明显改善,控制器的响应速度快、超调量小、稳定性强、控制效果好。     

Knowledge based fuzzy control of systems

A method of fuzzy control of systems based on a concept of human thinking is investigated. The fuzzy controller consists of a knowledge base containing a number of time responses of a process. The elements of this base are cells in which an input and a corresponding output of a system are stored. The cells are organized according to different types of relations. The result is a knowledge structure which contains a model of the process to be controlled. The following phases are distinguished: the learning phase, during which the relation between the imput and the output of a system is learned and the knowledge structure is constructed; and a phase in which the knowledge structure is applied to control the process. The method also provides an indication of the reliability of the control action     

基于电力载波通信的自动控制系统研究

随着电力载波技术的不断发展，电力载波通信的优势越加明显。本文介绍一套以LM1893集成芯片实现电力载波通信，Pc机做上位机进行信息的综合处理，单片机做下位机控制用电设备的自动控制系统。以串行通信的方式实现PC机RS232串口与单片机的多机通信，并把本文介绍的设计方法应用于教室节能系统加以实现，达到了设计效果。     

Repetitive control of Hamiltonian systems based on variational symmetry

This paper is concerned with repetitive control of Hamiltonian systems, which is based on iterative learning control utilizing the variational symmetry of those systems. Variational symmetry allows us to obtain an algorithm to solve a certain class of optimal control problems in a repetitive control framework. Therefore, the proposed method can deal with not only trajectory tracking control problems but also optimal trajectory generation problems, never before considered in a repetitive control framework. A convergence analysis of this algorithm is also discussed. Furthermore, some numerical simulations demonstrate the effectiveness of the proposed method.     

Cooperative exploration based on supervisory control of multi-robot systems

When multiple mobile robots cooperatively explore an unknown environment, the advantages of robustness and redundancy are guaranteed. However, available traditional economy approaches for coordination of multi-robot systems (MRS) exploration lack efficient target selection strategy under a few of situations and rely on a perfect communication. In order to overcome the shortages and endow each robot autonomy, a novel coordinated algorithm based on supervisory control of discrete event systems and a variation of the market approach is proposed in this paper. Two kinds of utility and the corresponding calculation schemes which take into account of cooperation between robots and covering the environment in a minimal time, are defined. Different moving target of each robot is determined by maximizing the corresponding utility at the lower level of the proposed hierarchical coordinated architecture. Selection of a moving target assignment strategy, dealing with communication failure, and collision avoidance are modeled as behaviors of each robot at the upper level. The proposed approach distinctly speeds up exploration process and reduces the communication requirement. The validity of our algorithm is verified by computer simulations.     

Linear control of nonlinear systems based on nonlinearity measures

In reality, virtually every process is a nonlinear system. Nevertheless, linear controller design methods have proved to be adequate in many applications. In practice, the linear controller design is usually done disregarding a possible nonlinear plant/linear model mismatch. In this work we introduce a general framework for the development of linear controllers for nonlinear systems based on nonlinearity measures. Nonlinearity measures are tools to assess the extent of a system’s inherent nonlinearity instead of just recognizing a system as being linear or nonlinear. Recent work shows that nonlinearity measures characterize the magnitude of the modeling error when an optimal linear model is used for the nonlinear system. The best linear model can then be used to design a linear controller that robustly stabilizes the linear system in presence of the nonlinear modeling error. A crucial point is that both, the best linear model and the modeling error, are determined for a specified region of operation, thus significantly increasing the class of applicable nonlinear systems. Examples demonstrate the (necessity and) effectiveness of the proposed approach.     

Input-output stability properties of networked control systems

Results on input-output L/sub p/ stability of networked control systems (NCS) are presented for a large class of network scheduling protocols. It is shown that static protocols and a recently considered dynamical protocol called try-once-discard belong to this class. Our results provide a unifying framework for generating new scheduling protocols that preserve L/sub p/ stability properties of the system if a design parameter is chosen sufficiently small. The most general version of our results can be used to treat NCS with data packet dropouts. The model of NCS and, in particular, of the scheduling protocol that we use appears to be novel and we believe that it will be useful in further study of these systems. The proof technique we use is based on the small gain theorem and it lends itself to an easy interpretation. We prove that our results are guaranteed to be better than existing results in the literature and we illustrate this via an example of a batch reactor.     

Symbolic control of linear systems based on symbolic subsystems

This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem into controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification.     

Some qualitative properties of sampled-data control systems

The authors consider sampled-data control systems which consist of an interconnection of a continuous-time nonlinear plant and a digital controller which has quantizers (but is otherwise linear), along with the required interface elements (A/D and D/A converters). In the present paper the authors study the qualitative effects of the quantizers and the plant nonlinearities of these systems     

Discretization behaviors of equivalent control based sliding-mode control systems

In this note, discretization behaviors of the equivalent control based sliding-mode control (SMC) systems are studied. Some inherent dynamical properties of the discretized second-order systems are first explored. Upper bounds for the system steady states are established. The system's steady-state behaviors are discussed. The analysis for the second-order systems is then extended to higher order systems. Simulations are presented to verify the theoretical results.