一种鲁棒的自适应跟踪控制器:稳定性和瞬态性能

对于系统 (R(s) +μΔ(s) ) y =Z(s)u +d ,通过把基于backstepping控制律和规范化自适应律结合起来设计了一种鲁棒的自适应跟踪控制器 ,这种控制器同时具有这两种设计方法的优点 .证明了闭环系统的所有信号有界 ,同时跟踪误差满足∫t+Tt ｜e(τ)｜2 dτ≤c(‖^bm‖2 ∞ +σ +μ2 +d0 2 )T+c.进一步 ,若bm 已知 ,则 |e|≤O 1λ( m +d0 ) + mc01d0+1f0+o(1) .

巡航控制系统及自适性巡航控制系统

巡航控制系统可控制汽车自动以恒定的速度行驶 ,有利于减轻驾驶员的操作负担、节省燃油和提高驾驶舒适性。介绍了巡航控制系统及基于其上的自适性巡航控制系统的组成。

机器人关节电液位置伺服系统的自适应控制

本文根据Lyapunov稳定性理论,提出了一种简单而有效的模型参考自适应控制方案(MRAC),用以改进机器人电液位置伺服系统的性能。试验表明,MRAC控制器能大大提高系统的动态响应性能和定位精度,并使系统伺服跟踪能力显著改善。

基于全参数在线辨识的鲁棒自校正控制

受控系统中 ,模型结构参数 (模型时延及模型阶次 )和模型参数是自适应类控制的基础 ,采样这些参数 ,并即时送给控制器 ,是实现自适应控制的前提 ,也是优化该类算法的重要依据。笔者给出了对上述自适应控制的有关参数的在线辩识方法 ,并基于此 ,改进了经典的自适应类控制算法。算法中引入了对结构参数 (模型阶次 )的智能辩识方法和鲁棒极点配置原理 ,从而构成了鲁棒自校正控制 ,使算法得以进一步优化。整个控制方案给出了自适应类控制的新模式。

Adaptive Nonlinear PID Control and Rule-Based Compensation for Systems with Backlash

The control of dynamic nonlinear systems with unknown backlash was considered. By using an efficient approach to estimate the unknown backlash parameters, a rule? based backlash compensator was presented for canceling the effect of backlash. Adaptive nonlinear PID controller together with rule? based backlash compensator was developed and a satisfactory tracking performance was achieved. Simulation results demonstrated the effectiveness of the proposed method.

Novel disturbance compensating dynamic positioning of dredgers based on adaptive backstepping

In order to deal with the dynamic positioning system control problems of dredgers working under strong dredging reaction or harsh environments,an adaptive backstepping method is proposed.Disturbances are estimated and compensated for by the adaptive method without extra sensors on dredging equipment,and the control mechanism is simplified.Adaptive control is used to compensate for the reaction and environmental disturbances on the dredger,so the dredger can maintain the desired position with a minimum error and shock.The proposed adaptive robust controller guarantees the global asymptotic stability of the closed-loop system and rapid position tracking of the dredger.The simulation results show that the proposed controller has superior performance in position tracking and robustness to large disturbances.

Application of Generalized Predictive Adaptive Control Algorithm in the Design of Missile Control System

To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.

FUZZY ADAPTIVE CONTROL OF FLEXIBLE-LINK ROBOT MANIPULATOR

A fuzzy adaptive control method is proposed for a flexible robot manipulator. Due to the structure characteristics of the flexible manipulator, the vibration modes must be controlled to realize the high-precision tip position. The Lagrangian principle is utilized to model the dynamic function of the single-degree flexible manipulator incorporating the assumed modes method. Simulation results of the fuzzy adaptive control method in the location control and the trajectory tracking with different tip disturbances are presented and compared with the results of the classic PD control. It shows that the controller can obtain the stable and robust performance.

Direct adaptive fuzzy control based on integral-type Lyapunov function

A new scheme of direct adaptive fuzzy controller for a class of nonlinear systems with unknown triangular control gain structure is proposed. The design is based on the principle of sliding mode control and the approximation capability of the first type fuzzy systems. By introducing integral-type Lyapunov function and adopting the adaptive compensation term of optimal approximation error, the closed-loop control system is proved to be globally stable, with tracking error converging to zero. Simulation results demonstrate the effectiveness of the approach.

ADAPTIVE TRACKING CONTROL FOR A CLASS OF NONLINEAR COMPOSITE SYSTEMS *

In this paper, the problem of adaptive tracking control for a class of nonlinear large scale systems with unknown parameters entering linearly is discussed. Based on the theory of input output linearization of nonlinear systems, direct adaptive control schemes are presented to achieve bounded tracking. The proposed control schemes are robust with respect to the uncertainties in interconnection structure as well as subsystem dynamics. A numerical example is given to illustrate the efficiency of this method.

Identification and Control of Dynamical Systems Using Modified Neural Networks

Aim To study the identification and control of nonlinear systems using neural networks. Methods A new type of neural network in which the dynamical error feedback is used to modify the inputs of the network was employed to reduce the inherent network approximation error. Results A new identification model constructed by the proposed network and stable filters was derived for continuous time nonlinear systems, and a stable adaptive control scheme based on the proposed networks was developed. Conclusion Theory and simulation results show that the modified neural network is feasible to control a class of nonlinear systems.

Robust adaptive controller design for a class of nonlinear systems with unknown high frequency gains

In this note, a robust adaptive control scheme is proposed for a class of nonlinear systems that have unknown multi-plicative terms. Unlike previous results, except for the unknown control directions, we do not require a priori bounds on the unknown parameters. We also allow the unknown parameters to be time-varying provided that they are bounded. Our proposed robust adaptive controller is designed to identify on-line the unknown control directions and is a switching type controller, in which the controller parameters are tuned in a switching manner via a switching logic. Global stability of the closed-loop systems have been proved.

Design and implementation of an efficient SDRAM controller for HDTV decoder

A high performance SDRAM controller for HDTV decoder is designed. MB-based （ macro block） address mapping, adaptive-precharge and command interleaving are adopted in this controller. MB-based address mapping reduces the precharge operations of the video processing unit in one access; adaptive- precharge avoids unnecessary precharge operations; while command interleaving inserts the precharge and activate commands of the next access into the command sequence of the current access, thus reduces the no operation （NOP） cycles. Combination of these three schemes effectively improves the SDRAM performance. Compared with precharge-all scheme, adaptive-precharge and command interleaving reduce the SDRAM overhead cycles by 70% and increases SDRAM performance by up to 19.2% in the best case. This controller has been implemented in an AVS SoC and the frequency is 200MHz.

Adaptive control using interval Type-2 fuzzy logic for uncertain nonlinear systems

A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.

Design of robust fuzzy controller for ship course-tracking based on RBF network and backstepping approach

This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results.

Study on rate ripple and its adaptive suppression method in inertia guidance test equipment

This paper discusses causes of the rate ripple in inertia guidance test equipment IGET, systematically analyses their effects an the rate ripple in IGTE. The analysis result shows： The rate ripple caused by the periodic errors of inductosyn and angular encoder is higher at high speed than that caused by magnetic ripple torque and friction torque, and it cannot be eliminated by adjusting control parameters of the system. And based on the nonlinear adaptive control system theory, the paper puts forward a new control system scheme to eliminate the rate ripple caused by the periodic errors of inductosyn and angular encoder, develops the adaptive control rules and makes simulation and test. Experimental result shows a significant improvement on those tables for the period disturbs under the system scheme designed. By this plan, with the input of rate 200°/s, the rate ripple falls from 5°/s to 0. 4°/s within about 6s adaptive adjustment time, being a twelfth of before adaptation, which can not be reached by common classical controls. The experimental results conform with the simulation, which proves the validity and practicability of the plan.

Adaptive synchronization of a class of chaotic systems via variable structure control

The synchronization problem under two cases is considered. One is that the bound on the uncertainty existing in the controller is known, the other is that the bound is unknown. In the latter case, the simple adaptation laws for upper bound on the norm of the uncertainty is proposed. Using this adaptive upper bound, a variable structure control is designed. The proposed method does not guarantee the convergence of the adaptive upper bound to the real one but makes the system asymptotically stable.

Neuro-fuzzy predictive control for nonlinear application

Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to reduce the model errors caused by changes of the process under control. To cope with the difficult problem of nonlinear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

Adaptive backstepping control for levitation system with load uncertainties and external disturbances

To explore the precise dynamic response of the levitation system with active controller, a maglev guide way-electromagnet-air spring-cabin coupled model is derived firstly. Based on the mathematical model, it shows that the inherent nonlinearity, inner coupling, misalignments between the sensors and actuators, load uncertainties and external disturbances are the main issues that should be solved in engineering. Under the assumptions that the loads and external disturbance are measurable, the backstepping module controller developed in this work can tackle the above problems effectively. In reality, the load is uncertain due to the additions of luggage and passengers, which will degrade the dynamic performance. A load estimation algorithm is introduced to track the actual load asymptotically and eliminate its influence by tuning the parameters of controller online. Furthermore,considering the external disturbances generated by crosswind, pulling motor and air springs, the extended state observer is employed to estimate and suppress the external disturbance. Finally, results of numerical simulations illustrating closed-loop performance are provided.

Adaptive terminal sliding mode control for high-order nonlinear dynamic systems

An adaptive terminal sliding mode control (SMC) technique is proposed to deal with the tracking problem for a class of high-order nonlinear dynamic systems. It is shown that a function augmented sliding hyperplane can be used to develop a new terminal sliding mode for high-order nonlinear systems. A terminal SMC controller based on Lyapunov theory is designed to force the state variables of the closed-loop system to reach and remain on the terminal sliding mode, so that the output tracking error then converges to zero in finite time which can be set arbitrarily. An adaptive mechanism is introduced to estimate the unknown parameters of the upper bounds of system uncertainties. The estimates are then used as controller parameters so that the effects of uncertain dynamics can be eliminated. It is also shown that the stability of the closed-loop system can be guaranteed with the proposed control strategy. The simulation of a numerical example is provided to show the effectiveness of the new method.