Adaptive Robust Servo Control for Vertical Electric Stabilization System of Tank and Experimental Validation

A tracking stability control problem for the vertical electric stabilization system of moving tank based on adaptive robust servo control is addressed.This paper mainly focuses on two types of possibly fast timevarying but bounded uncertainty within the vertical electric stabilization system:model parameter uncertainty and uncertain nonlinearity.First,the vertical electric stabilization system is constructed as an uncertain nonlinear dynamic system that can reflect the practical mechanics transfer process of the system.Second,the dynamical equation in the form of state space is established by designing the angular tracking error.Third,the comprehensive parameter of system uncertainty is designed to estimate the most conservative effects of uncertainty.Finally,an adaptive robust servo control which can effectively handle the combined effects of complex nonlinearity and uncertainty is proposed.The feasibility of the proposed control strategy under the practical physical condition is validated through the tests on the experimental platform.This paper pioneers the introduction of the internal nonlinearity and uncertainty of the vertical electric stabilization system into the settlement of the tracking stability control problem,and validates the advanced servo control strategy through experiment for the first time.

Adaptive Robust Control for a Class of Uncertain MIMO Non-affine Nonlinear Systems

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multioutput(MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO nonaffine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem,and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible"controller singularity" problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control(DSC) method is applied to solve the problem of"explosion of complexity" in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Robust adaptive fuzzy tracking control for a class of strict-feedback nonlinear systems based on backstepping technique

In this paper, the robust adaptive fuzzy tracking control problem is discussed for a class of perturbed strict-feedback nonlinear systems. The fuzzy logic systems in Mamdani type are used to approximate unknown nonlinear functions. A design scheme of the robust adaptive fuzzy controller is proposed by use of the backstepping technique. The proposed controller guarantees semi-global uniform ultimate boundedness of all the signals in the derived closed-loop system and achieves the good tracking performance. The possible controller singularity problem which may occur in some existing adaptive control schemes with feedback linearization techniques can be avoided. In addition, the number of the on-line adaptive parameters is not more than the order of the designed system. Finally, two simulation examples are used to demonstrate the effectiveness of the proposed control scheme.

Robust Adaptive Control of Nonholonomic Systems with Nonlinear Parameterization

全球适应的州的反馈控制策略与强壮的非线性的飘移和未知非线性的参数在锁住的形式为 nonholonomic 系统的一个类被介绍。一种参数分离技术被介绍把非线性的 parameterization nonholonomic 系统转变成一个象线性一样 parameterized nonholonomic 系统。然后，反馈支配设计被使用设计一个全球适应稳定控制器，切换的策略被开发消除 uncontrollability 的现象。建议控制器能保证那得到状态全球性集成到起源的所有系统，当另外的信号仍然保持围住时。模拟例子表明有效性和建议控制器的柔韧的特征。

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.

Switching Logic-based Adaptive Robust Control of Nonlinearly Parameterized Uncertain Systems

在这份报纸，切换基于逻辑的适应柔韧的控制为一个类被建议非线性地 parameterized 系统(NFS ) 。明确地，控制器主要由一项柔韧的类型条款组成探讨系统无常，和切换逻辑调节机制更新深奥控制获得。构造控制器完成全球一致地最终的固定(GUUB ) 系统错误的结果，和模拟结果被包括表明控制法律的有效性。

Neural network based adaptive sliding mode control of uncertain nonlinear systems

The purpose of this paper is the design of neural network-based adaptive sliding mode controller for uncertain unknown nonlinear systems. A special architecture adaptive neural network, with hyperbolic tangent activation functions, is used to emulate the equivalent and switching control terms of the classic sliding mode control （SMC）. Lyapunov stability theory is used to guarantee a uniform ultimate boundedness property for the tracking error, as well as of all other signals in the closed loop. In addition to keeping the stability and robustness properties of the SMC, the neural network-based adaptive sliding mode controller exhibits perfect rejection of faults arising during the system operating. Simulation studies are used to illustrate and clarify the theoretical results.

Adaptive robust dissipative designs on straight path control for underactuated ships

An adaptive robust control algorithm for ship straight path control system in the presence of both modeling uncertainties and the bounded disturbances is proposed. Motivated by the backstepping approach, the algorithm is developed by using the dissipation theory, such that the resulting dosed-loop system is both strictly dissipative and asymptotically adaptively stable for all admissible uncertainties. Also, it is able to steer an underactuated ship along a prescribed straight path with ultimate bounds under external disturbances induced by wave, wind and ocean current. When there are no disturbances, the straight path control can be implemented in a locally asymptotically stable manner. Simulation results on an ocean-going training ship ‘YULONG＇ are presented to validate the effectiveness of the algorithm.

Research of robust adaptive trajectory linearization control based on T-S fuzzy system

A robust adaptive trajectory linearization control （RATLC） algorithm for a class of nonlinear systems with uncertainty and disturbance based on the T-S fuzzy system is presented. The unknown disturbance and uncertainty are estimated by the T-S fuzzy system, and a robust adaptive control law is designed by the Lyapunov theory. Irrespective of whether the dimensions of the system and the rules of the fuzzy system are large or small, there is only one parameter adjusting on line. Uniformly ultimately boundedness of all signals of the composite closed-loop system are proved by theory analysis. Finally, a numerical example is studied based on the proposed method. The simulation results demonstrate the effectiveness and robustness of the control scheme.

Robust Optimization-Based Iterative Learning Control for Nonlinear Systems With Nonrepetitive Uncertainties

This paper aims to solve the robust iterative learning control(ILC)problems for nonlinear time-varying systems in the presence of nonrepetitive uncertainties.A new optimization-based method is proposed to design and analyze adaptive ILC,for which robust convergence analysis via a contraction mapping approach is realized by leveraging properties of substochastic matrices.It is shown that robust tracking tasks can be realized for optimization-based adaptive ILC,where the boundedness of system trajectories and estimated parameters can be ensured,regardless of unknown time-varying nonlinearities and nonrepetitive uncertainties.Two simulation tests,especially implemented for an injection molding process,demonstrate the effectiveness of our robust optimization-based ILC results.

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.

An Adaptive Fuzzy Control Model for Multi-Joint Manipulators

Multi-joint manipulator systems are subject to nonlinear influences such as frictional characteristics,random disturbances and load variations.To account for uncertain disturbances in the operation of manipulators,we propose an adaptive manipulator control method based on a multi-joint fuzzy system,in which the upper bound information of the fuzzy system is constant and the state variables of the manipulator control system are measurable.The control algorithm of the system is a MIMO(multi-input-multi-output)fuzzy system that can approximate system error by using a robust adaptive control law to eliminate the shadow caused by approximation error.It can ensure the stability of complex manipulator control systems and reduce the number of fuzzy rules required.Comparison of experimental and simulation data shows that the controller designed using this algorithm has highly-precise trajectory-tracking control and can control robotic systems with complex characteristics of non-linearity,coupling and uncertainty.Therefore,the proposed algorithm has good practical application prospects and promotes the development of complex control systems.

Neural Network Based Adaptive Tracking of Nonlinear Multi-Agent System

In this paper,the problems of robust consensus tracking control for the second-order multi-agent system with uncertain model parameters and nonlinear disturbances are considered.An adaptive control strategy is proposed to smooth the agent’s trajectory,and the neural network is constructed to estimate the system’s unknown components.The consensus conditions are demonstrated for tracking a leader with nonlinear dynamics under an adaptive control algorithm in the absence of model uncertainties.Then,the results are extended to the system with unknown time-varying disturbances by applying the neural network estimation to compensating for the uncertain parts of the agents’models.Update laws are designed based on the Lyapunov function terms to ensure the effectiveness of robust control.Finally,the theoretical results are verified by numerical simulations,and a comparative experiment is conducted,showing that the trajectories generated by the proposed method exhibit less oscillation and converge faster.

Adaptive robust control of soft bending actuators:an empirical nonlinear model-based approach

Soft robotics,compared with their rigid counterparts,are able to adapt to uncharted environments,are superior in safe human-robot interactions,and have low cost,owing to the native compliance of the soft materials.However,customized complex structures,as well as the nonlinear and viscoelastic soft materials,pose a great challenge to accurate modeling and control of soft robotics,and impose restrictions on further applications.In this study,a unified modeling strategy is proposed to establish a complete dynamic model of the most widely used pneumatic soft bending actuator.First,a novel empirical nonlinear model with parametric and nonlinear uncertainties is identified to describe the nonlinear behaviors of pneumatic soft bending actuators.Second,an inner pressure dynamic model of a pneumatic soft bending actuator is established by introducing a modified valve flow rate model of the unbalanced pneumatic proportional valves.Third,an adaptive robust controller is designed using a backstepping method to handle and update the nonlinear and uncertain system.Finally,the experimental results of comparative trajectory tracking control indicate the validity of the proposed modeling and control method.

Fault-tolerant Control and Disturbance Attenuation of a Class of Nonlinear Systems with Actuator and Component Failures

这份报纸象外部骚乱一样与致动器和部件失败调查非线性的动态系统的一个班的稳定问题。没有对在致动器失败变量上固定的估计的经分解的需要，新差错容忍的控制算法被导出，并且因此，结果的控制计划有更简单的结构并且作为与大多数存在方法相比要求更少的联机计算。与建议控制，致动器和分系统 / 部件失败能被提供，这被显示出，并且州的依赖生长骚乱能有效地被稀释。算法面对外部骚乱，参量的无常，以及严重致动器 / 分系统失败基于一条 Lyapunov 途径和数字模拟经由构词的数学分析被验证。

Nonlinear adaptive flight control system:Performance enhancement and validation

The problem of decreasing stability margins in L1 adaptive control systems is discussed and an out-of-loop L1 adaptive control scheme based on Lyapunov’s stability theorem is proposed.This scheme enhances the effectiveness of the adaptation,which ensures that the system has suffi-cient stability margins to achieve the desired performance under parametric uncertainty,additional delays,and actuator faults.The stability of the developed control system is demonstrated through a series of simulations.Compared with an existing control scheme,the constant adjustment of the sta-bility margins by the proposed adaptive scheme allows their range to be extended by a factor of 4–5,bringing the stability margin close to that of variable gain PD control with adaptively scheduled gains.The engineered practicability of adaptive technology is verified.A series of flight tests verify the practicability of the designed adaptive technology.The results of these tests demonstrate the enhanced performance of the proposed control scheme with nonlinear parameter estimations under insufficient stability margins and validate its robustness in the event of actuator failures.

Nonlinear Adaptive Robust Force Control of Hydraulic Load Simulator

This paper deals with the high performance force control of hydraulic load simulator. Many previous works for hydraulic force control are based on their linearization equations, but hydraulic inherent nonlinear properties and uncertainties make the conven- tional feedback proportional-integral-derivative control not yield to high-performance requirements. In this paper, a nonlinear system model is derived and linear parameterization is made for adaptive control. Then a discontinuous projection-based nonlin- ear adaptive robust force controller is developed for hydraulic load simulator. The proposed controller constructs an asymptoti- cally stable adaptive controller and adaptation laws, which can compensate for the system nonlinearities and uncertain parame- ters. Meanwhile a well-designed robust controller is also developed to cope with the hydraulic system uncertain nonlinearities. The controller achieves a guaranteed transient performance and final tracking accuracy in the presence of both parametric uncer- tainties and uncertain nonlinearities; in the absence of uncertain nonlinearities, the scheme also achieves asymptotic tracking performance. Simulation and experiment comparative results are obtained to verify the high-performance nature of the proposed control strategy and the tracking accuracy is greatly improved.

Application of a Robust Model Reference Adaptive Control Algorithm to a Nonlinear Automotive Actuator

Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.

一种新型永磁同步电机高阶滑模控制算法

针对永磁同步电机控制系统易因外部扰动等不确定性影响而导致控制性能下降的问题,提出了一种基于改进自适应非奇异终端滑模控制器和滑模扰动观测器补偿技术的复合控制算法。选择新型自适应指数趋近律设计非奇异终端滑模转速环控制器,新型趋近律可以在保证减少抖振的同时保证控制系统能够快速收敛,可动态适应被控系统的变化;利用扩展滑模扰动观测器实时估计系统中不确定性参数,并对控制器进行前馈补偿,且通过Lyapunov函数证明了其稳定性。仿真和试验结果表明,与PI控制、传统滑模控制方法相比,所提方法有效改善了抖振问题,提高了控制系统对电机的控制精度,提升了系统的鲁棒性和抗干扰性能。

Adaptive Control for a Class of Nonlinear Time-Delay System Based on the Fully Actuated System Approaches

This paper focuses on the problem of adaptive control for a class of time-delay systems.First,the strict feedback nonlinear time-delay system is transformed into a fully actuated system by utilizing the fully actuated system theory.Then,the uncertain time-delay terms of the system are bounded by the product of the absolute value of the system state and the non-linear function with the unknown parameters.By following the high order fully actuated system approaches,a continuous adaptive controller is designed for the system.It is proved that the controller can render the system achieve asymptotically stability.Finally,two numerical examples are provided to illustrate the effectiveness of the theoretical results.