A robust indirect adaptive regulation algorithm withself-excitation capability

A robust version of the adaptive control algorithm established by G. Kreisselmeier (1989) is presented. If the conventional dead-zone adaptive law is used in the adaptive system, it is troublesome that the size of the unmodeled dynamics, denoted by ∈, must be within the chosen dead-zone size for robustness. In this robust version, by introducing a positive design parameter, the robust stability can be achieved without this constraint. It is also shown that the plant output and control input will converge asymptotically within a certain bound. Moreover, in the ideal case, the plant output and control input will converge to zero under some condition     

A globally convergent pole placement indirect adaptive controller

A pole displacement indirect adaptive control algorithm is discussed for discrete-time linear deterministic plants with arbitrary zeros. The global convergence of the resulting closed-loop control system is achieved subject to the assumptions that the plant order and a nonzero lower bound on its degree of controllability are known. The problem of controllability of the plant model estimate is handled by using both a parameter correction and time-varying nonlinear feedback. A key property of the algorithm is that the plant estimate reaches a reasonable degree of controllability in a finite time, after which the parameter correction and the nonlinear feedback are no longer used     

Stable indirect adaptive neural controller for a class of nonlinear system

The paper presents an indirect adaptive neural control scheme for a general high-order nonlinear continuous system. In the proposed scheme a neural controller is constructed based on the single-hidden layer feedforward network (SLFN) for approximating the unknown nonlinearities of dynamic systems. A sliding mode controller is also incorporated to compensate for the modelling errors of SLFN. The parameters of the SLFN are modified using the recently proposed neural algorithm named extreme learning machine (ELM), where the parameters of the hidden nodes are assigned randomly. However different from the original ELM algorithm, the output weights are updated based on the Lyapunov synthesis approach to guarantee the stability of the overall control system, even in the presence of modelling errors which are offset using the sliding mode controller. Finally the proposed adaptive neural controller is applied to control the inverted pendulum system with two different reference trajectories. The simulation results demonstrate that good tracking performance is achieved by the proposed control scheme.     

An adaptive controller for nonlinear teleoperators

In Chopra et al (2008) [Chopra, N., Spong, M. W., & Lozano, R. (2008). Synchronization of bilateral teleoperators with time-delay. Automatica, 44(8), 2142-2148], an adaptive controller for teleoperators with time-delays, which ensures synchronization of positions and velocities of the master and slave manipulators, and does not rely on the use of the ubiquitous scattering transformation, is proposed. In this paper it is shown that this controller will tend to drive to zero the positions of the joints where gravity forces are non-zero. Hence, the scheme is, in general, applicable only to systems without gravity. We also prove in this paper that this limitation can be obviated, replacing the positions and velocities-that are used in the coordinating torques and the adaptation laws-by their errors. Simulation results illustrate the performance of both schemes.     

Takagi-Sugeno fuzzy model based indirect adaptive fuzzy observer and controller design

This paper proposes the design scheme of the alternative adaptive observer and controller based on the Takagi-Sugeno (T-S) fuzzy model. The T-S fuzzy modeling and the state feedback control technique are adopted for the simple structure. The proposed method maintains consistent performance in the presence of parameter uncertainties and incorporates linguistic fuzzy information from human operators. In addition, with the simple adaptive state feedback controller, it solves the singularity problem, which occurs in the inverse dynamics based on the feedback linearization method. Using Lyapunov theory and Lipschitz condition, the stability analysis is conducted, and the adaptive law is derived. The proposed method is applied to the stabilization problem of a flexible joint manipulator in order to guarantee its performance.     

An adaptive controller based on disturbance attenuation

This paper discusses the control of linear systems with uncertain parameters in the control coefficient matrix, under the influence of both process and measurement noise. A disturbance attenuation approach is used, and from this a multiplayer game problem is generated. First, the minimax formulation is presented, which represents an upper bound on the game cost criterion. Second, a dynamic programming approach is used to solve the game. It is necessary to significantly extend the method over earlier implementations, as the class of problems does not satisfy certain assumptions generally made. It is shown that for this class of problems, the controller determined from the dynamic programming approach is equivalent to the minimax controller. Therefore, the minimax controller is also a saddlepoint strategy for the differential game. Controller development appears to be much simpler from the dynamic programming standpoint. A simple scalar example is presented     

An adaptive controller which provides Lyapunov stability

An adaptive controller that can provide exponential Lyapunov stability for an unknown linear time-invariant (LTI) system is presented. The only required a priori information about the plant is that the order of an LTI stabilizing compensator be known, although this can be reduced to assuming only that the plant is stabilizable and detectable at the expense of using a more complicated controller. This result extends the work of M. Fu and B.R. Barmish (see ibid., vol.AC-31, p.1097-1103, Dec. 1986) in which it is shown that there exists an adaptive controller which provides exponential Lyapunov stability if it is assumed that an upper bound on the plant order is known and that the plant lies in a known compact set; it shows that adaptive stabilization is possible under very mild assumptions without large state deviations     

应用局部函数估计的自适应控制方法

针对一类非线性、不确定性系统结构,提出一种应用局部函数估计的自适应控制器设计方法,并分析该控制器的结构和系统稳定收敛性,最后通过仿真试验表明该控制器设计的简单实用和优越性能。     

An adaptive dead-zone inverse controller for systems with sandwiched dead-zones

This paper addresses adaptive control of sandwich non-linear systems having an unknown sandwiched dead-zone between the linear dynamic blocks, as illustrated by a hydraulic valve system. An adaptive hybrid control scheme for control of such sandwiched dead-zone systems is developed. The proposed control scheme employs an inner-loop discrete-time feedback design and an outer-loop continuous-time feedback design, combined with an adaptive dead-zone inverse to cancel the dead-zone effect for improving output tracking. Stability and tracking performance of the closed-loop control system are analysed. Simulation results are used to illustrate the effectiveness of the proposed adaptive dead-zone inverse controller.     

An indirect stochastic adaptive scheme with on-line choice ofweighting polynomials

An indirect adaptive control algorithm combining a quadratic cost measure of the Clarke-Gawthrop type and the classical control strategy of pole placement is presented. It updates the weighting polynomials of the cost function online in such a way that the closed-loop poles are located at prespecified positions. The convergence and stability of the adaptive control algorithm is also given without the assumption that the system is minimum phase     

An adaptive controller design for linear state-delay systems with actuator failures

In this paper, an adaptive fault tolerant controller is developed for a class of linear state delay systems against actuator failures. To design the controller, all parameters of the system are considered to be unknown, but the time delay value is assumed to be known. Actuator failures are characterized by some unknown system inputs are stuck at some unknown fixed values and at unknown time instants. The adaptive controller is designed based on SPR-Lyapunov approach for the cases with the relative degrees of one and two. Closed-loop system stability and asymptotic output tracking are proved using a suitable Lyapunov-Krasovskii functional for each case and the effectiveness of the proposed results has been illustrated through simulation studies.     

An adaptive partial state-feedback controller for RLED robotmanipulators

An adaptive partial state-feedback controller is designed for rigid-link electrically driven (RLED) robot manipulators. The controller is based on structural knowledge of the electromechanical dynamics of the RLED robot and measurements of link position and electrical winding current in each of the brushed DC link actuators. The proposed controller is designed to adapt for parametric uncertainty in the electromechanical dynamics while utilizing a dynamic filter to generate link velocity tracking error information. The controller, adaptation laws, and the pseudovelocity filter are designed via a Lyapunov-like approach, the benefit of which is that at the end of the design procedure the controller can be mathematically shown to produce semiglobal asymptotic link position tracking. The basic design approach can be extended to many types of multiphase motors     

An adaptive actuator failure compensation controller using outputfeedback

An adaptive control scheme using output feedback for output tracking is developed for systems with unknown actuator failures. Such actuator failures are characterized by some unknown inputs stuck at some unknown fixed values at unknown time instants. An effective output feedback controller structure is proposed for actuator failure compensation. When implemented with true matching parameters, the controller achieves desired plant-model output matching. When implemented with adaptive parameter estimates, the controller achieves asymptotic output tracking. A stable adaptive law is derived for parameter adaptation in the presence of parameter uncertainties. Closed-loop signal boundedness and asymptotic output tracking, despite the uncertainties in actuator failures and plant parameters, are ensured analytically and verified by simulation results     

An adaptive tracking controller using neural networks for a classof nonlinear systems

A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation example is performed in support of the proposed neural control scheme.     

An adaptive neural network admission controller for dynamicbandwidth allocation

In an access node to a hybrid-switching network (e.g., a base station handling the downlink in a cellular wireless network), the output link bandwidth is dynamically shared between isochronous (guaranteed bandwidth) and asynchronous traffic types. The bandwidth allocation is effected by an admission controller, whose goal is to minimize the refusal rate of connection requests as well as the loss probability of packets queued in a finite buffer. Optimal admission control strategies are approximated by means of backpropagation feedforward neural networks, acting on the embedded Markov chain of the connection dynamics. The case of unknown, slowly varying, input rates is explicitly considered. Numerical results are presented, comparing the approximation with the optimal solution obtained by dynamic programming.     

An adaptive impedance/force controller for robot manipulators

A solution to the adaptive control of constrained robots in the presence of uncertainty in the robot model parameters is presented. The controller design is based on a singular systems model representation and fixed controller design. The adaptive control law consists of the computed torque controller plus the introduction of the parameter estimates and an additional compensation through an extra signal. Some properties of the reduced form robot model are presented and exploited to prove the asymptotic tracking properties of the adaptive controller. Also, the inclusion of the impedance control objective allows the accommodation of tangential forces that may appear in the constrained task     

An adaptive fuzzy sliding mode controller for robotic manipulators

This paper proposes an adaptive fuzzy sliding mode controller for robotic manipulators. An adaptive single-input single-output (SISO) fuzzy system is applied to calculate each element of the control gain vector in a sliding mode controller. The adaptive law is designed based on the Lyapunov method. Mathematical proof for the stability and the convergence of the system is presented. Various operation situations such as the set point control and the trajectory control are simulated. The simulation results demonstrate that the chattering and the steady state errors, which usually occur in the classical sliding mode control, are eliminated and satisfactory trajectory tracking is achieved.     

An adaptive support vector regressor controller for nonlinear systems

In this study, a novel online support vector regressor (SVR) controller based on system model estimated by a separate online SVR is proposed. The main idea is to obtain an SVR controller based on an estimated model of the system by optimizing the margin between reference input and system output. For this purpose, “closed-loop margin” which depends on tracking error is defined, then the parameters of the SVR controller are optimized so as to optimize the closed-loop margin and minimize the tracking error. In order to construct the closed-loop margin, the model of the system estimated by an online SVR is utilized. The parameters of the SVR controller are adjusted via the SVR model of system. The stability of the closed-loop system has also been analyzed. The performance of the proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR) and a bioreactor, and the results show that SVR model and SVR controller attain good modeling and control performances.     

Connectionist expert system with adaptive learning capability

A neural network expert system called adaptive connectionist expert system (ACES) which will learn adaptively from past experience is described. ACES is based on the neural logic network, which is capable of doing both pattern processing and logical inferencing. The authors discuss two strategies, pattern matching ACES and rule inferencing ACES. The pattern matching ACES makes use of past examples to construct its neural logic network and fine-tunes itself adaptively during its use by further examples supplied. The rule inferencing ACES conceptualizes new rules based on the frequencies of use on the rule-based neural logic network. A new rule could be considered as a pattern matching example and be incorporated into pattern matching ACES     

A continuous-time adaptive controller with integral action

This note shows how a continuous-time adaptive control law can be constructed which automatically includes integral action. The control law is based on the internal model principle. Global convergence can be readily established for the algorithm using standard methods.