基于神经网络的鲁棒自适应控制

考虑摩擦及外界干扰的情况下 ,针对具有不确定性参数的机器人系统 ,提出一种基于神经网络动态补偿的鲁棒自适应控制策略 ,采用神经网络在线补偿控制器以克服系统的外部扰动、未建模动力学部分等非参数不定性带来的影响 ,从而提高了系统的动态性能和稳态精度 ,并对闭环系统稳定性进行了证明。仿真结果表明 ,所提方法具有良好的跟踪性能和较强的鲁棒性。

ROBUST ADAPTIVE SLIDING MODE CONTROL FOR NONLINEAR UNCERTAIN NEUTRAL DELAY SYSTEM

Robust stabilization for a class of nonlinear uncertain neutral system with time-varying delay is investigated. By applying the Lyapunov stability theorem, an adaptive sliding mode controller （ADSMC） is developed.Based on the sliding mode control technique, the controller can drive the system into a pre-specified sliding hyperplane to obtain the desired dynamic performance. Once the system dynamics reaches the sliding plane, the control system is insensitive to uncertainty. The adaptive technique can overcome the unknown upper bound of uncertainty so that the reaching condition can be satisfied. Furthermore, the controller does not include any delayed state,so such an ADSMC is memoryless. Finally, a numerical example is given to verify the validity of the developed memoryless ADSMC and the globally asymptotic stability is guaranteed for the control scheme.

ROBUST ADAPTIVE CONTROL SCHEME FOR IMPROVING LOW-SPEED PROFILE TRACKING PERFORMANCE OF HYBRID STEPPING MOTOR SERVO DRIVE

A robust adaptive control approach is presented to improve the performance of the control scheme proposed in the authors＇ previous work, aiming at producing a low ripple hybrid stepping motor servo drive for precision profile tracking at a low speed. In order to construct a completely integrated control design philosophy to reduce torque ripple and at the same time to enhance tracking performance, the properties of nonlinear uncertainties in the system dynamics are uncovered, and then incorporated into the design of the controller. The system uncertainties concerned with ripple dynamics and other external disturbances are composed of two categories. The first category of uncertainties with linear parameterization arising from the detention effect is dealt with by the wellknown adaptive control method. A robust adaptive method is used to deal with the second category of uncertainties resulting from the non-sinusoidal flux distribution. The μ-modification scheme is used to cease parameter adaptation by the robust adaptive control law, thus ensuring that the trajectory tracking error asymptotically converges to a pre-specified boundary. Experiments are performed with a typical hybrid stepping motor to test its profile tracking accuracy. Results confirm the proposed control scheme.

Robust adaptive dynamic surface control for nonlinear uncertain systems

We propose a new method for robust adaptive backstepping control of nonlinear systems with parametric uncertainties and disturbances in the strict feedback form. The method is called dynamic surface control. Traditional backstepping algorithms require repeated differentiations of the modelled nonlinearities. The addition of n first order low pass filters allows the algorithm to be implemented without differentiating any model nonlinearities, thus ending the complexity arising due to the 'explosion of terms' that makes other methods difficult to implement in practice. The combined robust adaptive backstepping/first order filter system is proved to be semiglobally asymptotically stable for sufficiently fast filters by a singular perturbation approach. The simulation results demonstrate the feasibility and effectiveness of the controller designed by the method.

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.

Adaptive robust output force tracking control of pneumatic cylinder while maximizing/minimizing its stiffness

The system considered in this work consists of a cylinder which is controlled by a pair of three-way servo valves rather than a four-way one.Therefore,the cylinder output stiffness is independently controllable of the output force.A discontinuous projection based adaptive robust controller (ARC) was constructed to achieve high-accuracy output force trajectory tracking for the system.In ARC,on-line parameter adaptation method was adopted to reduce the extent of parametric uncertainties due to the variation of friction parameters,and sliding mode control method was utilized to attenuate the effects of parameter estimation errors,unmodelled dynamics and disturbance.Furthermore,output stiffness maximization/minimization was introduced to fulfill the requirement of many robotic applications.Extensive experimental results were presented to illustrate the effectiveness and the achievable performance of the proposed scheme.For tracking a 0.5 Hz sinusoidal trajectory,maximum tracking error is 4.1 N and average tracking error is 2.2 N.Meanwhile,the output stiffness can be made and maintained near its maximum/minimum.

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.

Robust control with compensation of adaptive model for dual-stage inertially stabilized platform

To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a secondary servo mechanism to control lens motion in the imaging optical path.Firstly,the dual-stage dynamics is mathematically modeled as a coupling multi-input multi-output(MIMO)control system.Then,by incorporating compensation of adaptive model to deal with parameter variations and nonlinearity,a systematic robust H∞control scheme is designed,which can achieve good tracking performance,as well as improve system robustness against model uncertainties.Lyapunov stability analysis confirmed the stability of the overall control system.Finally,simulation and experiment results are provided to demonstrate the feasibility and effectiveness of the proposed control design method.

Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation

A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.

Adaptive robust motion trajectory tracking control of pneumatic cylinders

High-accuracy motion trajectory tracking control of a pneumatic cylinder driven by a proportional directional control valve was considered. A mathematical model of the system was developed firstly. Due to the time-varying friction force in the cylinder, unmodeled dynamics, and unknown disturbances, there exist large extent of parametric uncertainties and rather severe uncertain nonlinearities in the pneumatic system. To deal with these uncertainties effectively, an adaptive robust controller was constructed in this work. The proposed controller employs on-line recursive least squares estimation(RLSE) to reduce the extent of parametric uncertainties, and utilizes the sliding mode control method to attenuate the effects of parameter estimation errors, unmodeled dynamics and disturbances. Therefore, a prescribed motion tracking transient performance and final tracking accuracy can be guaranteed. Since the system model uncertainties are unmatched, the recursive backstepping design technology was applied. In order to solve the conflicts between the sliding mode control design and the adaptive control design, the projection mapping was used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Extensive experimental results were presented to illustrate the excellent achievable performance of the proposed controller and performance robustness to the load variation and sudden disturbance.

Cross-coupling integral adaptive robust posture control of a pneumatic parallel platform

A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-based controller designed. The pneumatic muscles were controlled by three proportional position valves, and the air cylinder was controlled by a proportional pressure valve. As the forward kinematics of this structure had no analytical solution, the control strategy should be designed in joint space. A cross-coupling integral adaptive robust controller(CCIARC) which combined cross-coupling control strategy and traditional adaptive robust control(ARC) theory was developed by back-stepping method to accomplish trajectory tracking control of the parallel platform. The cross-coupling part of the controller stabilized the length error in joint space as well as the synchronization error, and the adaptive robust control part attenuated the adverse effects of modelling error and disturbance. The force character of the pneumatic muscles was difficult to model precisely, so the on-line recursive least square estimation(RLSE) method was employed to modify the model compensation. The projector mapping method was used to condition the RLSE algorithm to bound the parameters estimated. An integral feedback part was added to the traditional robust function to reduce the negative influence of the slow time-varying characteristic of pneumatic muscles and enhance the ability of trajectory tracking. The stability of the controller designed was proved through Laypunov's theory. Various contrast controllers were designed to testify the newly designed components of the CCIARC. Extensive experiments were conducted to illustrate the performance of the controller.

A new robust fuzzy method for unmanned flying vehicle control

A new general robust fuzzy approach was presented to control the position and the attitude of unmanned flying vehicles(UFVs). Control of these vehicles was challenging due to their nonlinear underactuated behaviors. The proposed control system combined great advantages of generalized indirect adaptive sliding mode control(IASMC) and fuzzy control for the UFVs. An on-line adaptive tuning algorithm based on Lyapunov function and Barbalat lemma was designed, thus the stability of the system can be guaranteed. The chattering phenomenon in the sliding mode control was reduced and the steady error was also alleviated. The numerical results, for an underactuated quadcopter and a high speed underwater vehicle as case studies, indicate that the presented adaptive design of fuzzy sliding mode controller performs robustly in the presence of sensor noise and external disturbances. In addition, online unknown parameter estimation of the UFVs, such as ground effect and planing force especially in the cases with the Gaussian sensor noise with zero mean and standard deviation of 0.5 m and 0.1 rad and external disturbances with amplitude of 0.1 m/s2 and frequency of 0.2 Hz, is one of the advantages of this method. These estimated parameters are then used in the controller to improve the trajectory tracking performance.

Global Robust and Adaptive Output Feedback Dynamic Positioning of Surface Ships

A constructive method was presented to design a global robust and adaptive output feedback controller for dynamic positioning of surface ships under environmental disturbances induced by waves, wind, and ocean currents. The ship's parameters were not required to be known. An adaptive observer was first designed to estimate the ship's velocities and parameters. The ship position measurements were also passed through the adaptive observer to reduce high frequency measurement noise from entering the control system. Using these estimate signals, the control was then designed based on Lyapunov's direct method to force the ship's position and orientation to globally asymptotically converge to desired values. Simulation results illustrate the effectiveness of the proposed control system. In conclusion, the paper presented a new method to design an effective control system for dynamic positioning of surface ships.

A novel robust adaptive controller for EAF electrode regulator system based on approximate model method

The electrode regulator system is a complex system with many variables, strong coupling and strong nonlinearity, while conventional control methods such as proportional integral derivative （PID） can not meet the requirements. A robust adaptive neural network controller （RANNC） for electrode regulator system was proposed. Artificial neural networks were established to learn the system dynamics. The nonlinear control law was derived directly based on an input-output approximating method via the Taylor expansion, which avoids complex control development and intensive computation. The stability of the closed-loop system was established by the Lyapunov method. The current fluctuation relative percentage is less than ±8% and heating rate is up to 6.32 ℃/min when the proposed controller is used. The experiment results show that the proposed control scheme is better than inverse neural network controller （INNC） and PID controller （PIDC）.

Robust Adaptive Control of PVTOL System via Minimum Projection Method

＂Dynamic extension＂ is commonly used for stabilization of the planar vertical take off and landing （PVTOL） system. Most controllers designed by the method are based on ＂dynamic＂ control Lyapunov functions （CLFs）. We design a C^∞ differentiable ＂static＂ CLF for the PVTOL system by dynamic extension and minimum projection method. Then we propose an inverse optimal controller based on the static CLF that attains a gain margin. We design an adaptive control input and show the robustness of the controller by computer simulation.

Robust Adaptive FTC against Sensor Fault

This paper addresses the problem of robust Fault-Tolerant （FT） design for large-scale systems. This particular class constitutes complex system which can be decomposed into N-interconnected subsystems. Special attention is paid to two different design architectures of an Active Fault-Tolerant Control （AFTC）. An AFTCS is characterized by an online Fault Detection and Isolation （FDI） process and a control reconfiguration mechanism. As the AFTC system offers the possibility to choose different controllers, the controller may be the most appropriate choice for the faulty situation and obtaining better performance. The goal of each adaptive controller is to accommodate sensor anomalies. Continuous, Linear and Time Invariant （LTI） complex system with faulty sensors and external disturbances is proposed. This study focuses on two different internal structures of the system. In this paper the direct adaptive method based on feedback controller design is applied both centralized and decentralized architectures. The controller gain is updated online using an adaptive law which takes into account the estimation of the faults and the disturbances. Then from the both classes of systems structures the adaptation controller performances, in terms of stability and fault effect rejection capability, are studied and compared. The proposed techniques are finally evaluated in the light of a simulation for a centralized interconnected system that can be decomposed into N-subsystems with some strong interconnections.

Adaptive Robust Dead-Zone Compensation Control of Electro-Hydraulic Servo Systems with Load Disturbance Rejection

A backstepping method based adaptive robust dead-zone compensation controller is pro- posed for the electro-hydraulic servo systems （EHSSs） with unknown dead-zone and uncertain system parameters. Variable load is seen as a sum of a constant and a variable part. The constant part is regarded as a parameter of the system to be estimated real time. The variable part together with the friction are seen as disturbance so that a robust term in the controller can be adopted to reject them. Compared with the traditional dead-zone compensation method, a dead-zone compensator is incor- porated in the EH$S without constructing a dead-zone inverse. Combining backstepping method, an adaptive robust controller （ARC） with dead-zone compensation is formed. An easy-to-use ARC tuning method is also proposed after a further analysis of the ARC structure. Simulations show that the proposed method has a splendid tracking performance, all the uncertain parameters can be estimated, and the disturbance has been rejected while the dead-zone term is well estimated and compensated.

A Robust Adaptive Dynamic Programming Principle for Sensorimotor Control with Signal-Dependent Noise

As human beings,people coordinate movements and interact with the environment through sensory information and motor adaptation in the daily lives.Many characteristics of these interactions can be studied using optimization-based models,which assume that the precise knowledge of both the sensorimotor system and its interactive environment is available for the central nervous system(CNS).However,both static and dynamic uncertainties occur inevitably in the daily movements.When these uncertainties are taken into consideration,the previously developed models based on optimization theory may fail to explain how the CNS can still coordinate human movements which are also robust with respect to the uncertainties.In order to address this problem,this paper presents a novel computational mechanism for sensorimotor control from a perspective of robust adaptive dynamic programming(RADP).Sharing some essential features of reinforcement learning,which was originally observed from mammals,the RADP model for sensorimotor control suggests that,instead of identifying the system dynamics of both the motor system and the environment,the CNS computes iteratively a robust optimal control policy using the real-time sensory data.An online learning algorithm is provided in this paper,with rigorous convergence and stability analysis.Then,it is applied to simulate several experiments reported from the past literature.By comparing the proposed numerical results with these experimentally observed data,the authors show that the proposed model can reproduce movement trajectories which are consistent with experimental observations.In addition,the RADP theory provides a unified framework that connects optimality and robustness properties in the sensorimotor system.

Robust adaptive cross-coupling position control of biaxial motion system

The stability and synchronous performance are usually hard to be improved simultaneously in the biaxial cross-coupling position motion control system.Based on analyzing the characteristics of the cross-coupling control system,a robust adaptive cross-coupling control strategy is proposed.To restrict influences of destabilizing factors and improve both of stability and synchronous performance,the strategy forces dual axes to track the same reference model using Narendra adaptive control theory.And then,a robust parameters adaptive law is proposed.The stability analysis of the proposed strategy is conducted by applying Lyapunov stability theory.Related simulations and experiments indicate that the proposed strategy can improve synchronous performance and stability simultaneously.