Automated Maximum Sensitivity And Phase Margin Specification Attainment In Pi Control

The prevailing industrial and academic autotune paradigm uses the relay experiment to find the ultimate data of an unknown system, followed by the use of this data in PI (D) tuning rules. This paper firstly reviews a novel method of obtaining desired frequency response data‐points of an unknown system which is more accurate and flexible than the relay experiment. The paper then demonstrates how this new identification module can be used in closed loop to automate PI controller tuning to achieve classical robustness specifications. The new algorithm is given along with details of MATLAB/SIMULINK simulation results. The non‐parametric identification method developed by the authors and used in this work is termed the Phase Locked Loop method (PLL). The classical robustness specification to be achieved by the PI controller is a pair of desired maximum sensitivity and phase margin specifications. Conclusions and future research directions close the paper.     

Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.     

Decentralized adaptive fuzzy sliding mode control for reconfigurable modular manipulators

A stable decentralized adaptive fuzzy sliding mode control scheme is proposed for reconfigurable modular manipulators to satisfy the concept of modular software. For the development of the decentralized control, the dynamics of reconfigurable modular manipulators is represented as a set of interconnected subsystems. A first‐order Takagi–Sugeno fuzzy logic system is introduced to approximate the unknown dynamics of subsystem by using adaptive algorithm. The effect of interconnection term and fuzzy approximation error is removed by employing an adaptive sliding mode controller. All adaptive algorithms in the subsystem controller are derived from the sense of Lyapunov stability analysis, so that resulting closed‐loop system is stable and the trajectory tracking performance is guaranteed. The simulation results are presented to show the effectiveness of the proposed decentralized control scheme. Copyright © 2009 John Wiley & Sons, Ltd.     

A new adaptive backstepping method for nonlinear control of turbine main steam valve

A new approach for nonlinear adaptive control of turbine main steam valve is developed. In comparison with the existing controller based on ＂classical＂ adaptive backstepping, this method does not follow the classical certaintyequivalence principle in the design of adaptive control law. We introduce this approach, for the first time, to power systems and present a novel parameter estimator and dynamic feedback controller for a single machine infinite bus （SMIB） system with steam valve control. This system contains unknown parameters such as reactance of transmission lines. Besides preserving useful nonlinearities and the real-time estimation of uncertain parameters, the proposed approach possesses better performances with respect to the response of the system and the speed of adaptation. The simulation results demonstrate that the proposed approach is better than the design based on ＂classical＂ adaptive backstepping in terms of properties of stability and parameter estimation, and recovers the performance of the ＂full-information＂ controller. Hence, the proposed method provides an alternative for engineers in applications.     

Adaptive neural control for a class of uncertain nonlinear systems with unknown time delay

A neural network (NN)‐based robust adaptive control design scheme is developed for a class of nonlinear systems represented by input–output models with an unknown nonlinear function and unknown time delay. By approximating on‐line the unknown nonlinear functions with a three‐layer feedforward NN, the proposed approach does not require the unknown parameters to satisfy the linear dependence condition. The control law is delay independent and possible controller singularity problem is avoided. It is proved that with the proposed neural control law, all the signals in the closed‐loop system are semiglobally bounded in the presence of unknown time delay and unknown nonlinearity. A simulation example is presented to demonstrate the method. Copyright © 2008 John Wiley & Sons, Ltd.     

Adaptive finite‐time stabilization of nonlinearly parameterized systems subject to mismatching disturbances

This paper gives a first try to the finite‐time control for nonlinear systems with unknown parametric uncertainty and external disturbances. The serious uncertainties generated by unknown parameters are compensated by skillfully using an adaptive control technique. Exact knowledge of the upper bounds of the disturbances is removed by employing a disturbance observer–based control method. Then, based on the disturbance observer–based control, backstepping technique, finite‐time adaptive control, and Lyapunov stability theory, a composite adaptive state‐feedback controller is strictly designed and analyzed, which guarantees the closed‐loop system to be practically finite‐time stable. Finally, both the practical and numerical examples are presented and compared to demonstrate the effectiveness of the proposed scheme.     

基于自适应模糊滑模控制的船舶航向控制器设计

针对船舶运动系统中固有的非线性、模型不确定性和风、浪、流等的干扰.提出了自适应模糊滑模控制(AFSMC)策略解决船舶的航向控制问题.通过采用模糊逻辑系统逼近系统未知函数,将滑模控制技术与自适应模糊控制技术相结合,设计了船舶航向AFSMC控制器.在滑模边界层内应用PI (proportional-integral)控制代替滑模控制中的切换项,削弱了滑模控制带来的抖振现象.借助李亚普诺夫函数证明了船舶运动系统中的信号都一致有界并利用Barbalat引理证明了跟踪误差渐近收敛到零.在参数摄动和外界干扰情况下进行了航向保持与改变仿真试验,采用AFSMC控制器得到了与无摄动和无干扰情况下相似的输出响应.实验结果表明,所提控制器能有效地处理系统不确定性和外界干扰,控制性能良好,具有很强的鲁棒性.     

SECOND ORDER SLIDING MODE CONTROL OF A DIESEL ENGINE

A 2nd order sliding mode algorithm is applied to control the speed of a diesel power generator set. Tuning guidelines are given. The robustness of the controller is investigated and the performance of the 2nd order sliding mode controller is compared with that obtained by a commercial diesel engine controller and a classical proportional‐integral (PI) controller. Robustness to unmodelled dynamics is discussed and implementation results presented.     

Adaptive Dual Control of Discrete‐Time Lqg Problems with Unknown‐But‐Bounded Parameter

The dual control for a class of discrete‐time linear‐quadratic Gaussian (LQG) problems is studied in this paper, where there exist unknown‐but‐bounded parameter uncertainties both in the state equation and the observation equation. It is assumed that the unknown parameter belongs to a known bounded interval. To achieve the adaptive dual control, a subdivision for the continuous bounded interval is used, thus, the computational tractability is ensured. Based on such subdivision, the adaptive dual controller developed can learn a more accurate interval that contains the true value of the unknown parameter with a learning error given in advance. The performance of the controller is illustrated with two examples.     

Nonlinear adaptive control for power integrator triangular systems by switching linear controllers

In this paper, a nonlinear adaptive stabilizer is designed for a class of power integrator triangular systems with the following four features: (i) the chained integrators have the powers of positive odd numbers, which makes the linearization of the studied system uncontrollable; (ii) the nonlinear function contains the virtual control variables; (iii) the bound of the nonlinear parameters entering the function nonlinearity is not required to be known a priori; and (iv) there exists an unknown control coefficient with the unknown bound in the control channel. Our proposed adaptive controller is a switching type controller, in which the designed adaptive stabilizer takes a two‐step procedure: a linear stabilizing controller containing the tuning gains is first designed by the adding a power integrator technique. Switching logic is then proposed to tune online the gains in a switching manner. The proposed adaptive controller globally asymptotically stabilizes the considered system in the sense that, for any initial conditions, the state converges to the origin while all the signals of the closed‐loop system are bounded. Simulation studies clarify and verify the approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive Neural Dynamic Surface Control For Nonlinear Pure‐Feedback Systems With Multiple Time‐Varying Delays: A Lyapunov‐Razumikhin Method

This paper focuses on the problem of adaptive neural control for a class of uncertain nonlinear pure‐feedback systems with multiple unknown time‐varying delays. The considered problem is challenging due to the non‐affine pure‐feedback form and the unknown system functions with multiple unknown time‐varying delays. Based on a novel combination of mean value theorem, Razumikhin functional method, dynamic surface control (DSC) technique and neural network (NN) parameterization, a new adaptive neural controller which contains only one parameter is developed for such systems. Moreover, The DSC technique can overcome the problem of ‘explosion of complexity’ in the traditional backstepping design procedure. All closed‐loop signals are shown to be semi‐globally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. Two simulation examples are given to verify the effectiveness of the proposed design.     

Fuzzy predictive PI control for processes with large time delays

This paper presents the design, tuning and performance analysis of a new predictive fuzzy controller structure for higher order plants with large time delays. The designed controller consists of a fuzzy proportional-integral (PI) part and a fuzzy predictor. The fuzzy predictive PI controller combines the advantages of fuzzy control while maintaining the simplicity and robustness of a conventional PI controller. The dynamics of the prediction term are adaptive to the system's time delay. The prediction term has two parts: a fuzzy predictor that uses the system time delay as an input for calculating the prediction horizon and an exponential term that uses the prediction horizon as its positive power. The prediction term also introduces phase lead into the system which compensates for the phase lag due to the time delay in the plant, thereby stabilizing the closed-loop configuration. The performance of the proposed controller is compared with the responses of the conventional predictive PI controller, showing many advantages of the new design over its conventional counterpart.     

A powerful output‐feedback controller for uncertain nonlinear systems

System nonlinearities, immeasurableness, and uncertainties (especially their coupling/coexistence) have constantly challenged the control design, which requires the controller to be powerful enough to counteract the resulting negative effects. In this paper, a global adaptive output‐feedback controller is designed for uncertain nonlinear systems. The proposed controller is of switching type, in which the design parameters are online adjusted on the basis of a switching logic in a recursive manner. This makes the controller powerful enough to compensate the system unknowns and dominate the system nonlinearities and therefore be applicable to a rather wide range of systems. Remarkably, our strategy allows the coexistence of an unknown control direction and unmeasured state–dependent growth but which are substantially excluded in the related literature. An example of the single‐link robot arm system is provided to illustrate the effectiveness of the proposed controller.     

Design of Adaptive PI Rate Controller for Best-Effort Traffic in the Internet Based on Phase Margin

In this paper, we propose an adaptive PI (proportional-integral) rate controller for the AQM (active queue management) router that would support best-effort traffic in the Internet. Unlike most window-based controllers, our rate-based controller design is derived from the classical control theory and it would allow the users to achieve good stability robustness of the AQM control system by specifying a proper phase margin. We also make our controller adaptive by selecting a simple heuristic parameter to monitor the network environment real-time so that the controller would self-tune only when a dramatic change of the network traffic has drifted the monitoring parameter outside its specified interval. Located in the router, the adaptive PI rate controller calculates desirable source window sizes (i.e., source sending rates) based on the instantaneous queue length of the buffer and advertises it to the sources. Our simulations demonstrate that our AQM control system can adapt very well to sudden changes in network environment, thus providing the network with good transient behavior. By making the source sending rate relatively smooth, our adaptive PI rate controller becomes quite suitable for streaming media traffic control in the Internet     

DIRECT ADAPTIVE FEEDBACK DESIGN FOR LINEAR DISCRETE‐TIME UNCERTAIN SYSTEMS

In this paper, we develope a direct adaptive control framework for linear discrete‐time uncertain MIMO systems, based on assumptions that the system is controllable and the difference of the unknown system matrix from the stable solution is bounded by a given value. The proposed framework is Lyapunov‐based, and the controller guarantees adaptive stabilization. In addition, the adaptive laws are characterized by means of Kronecker calculus. Furthermore, the results can easily be extended to time‐varying cases, where the deviation from the stable solution is bounded, and the controllability assumption holds. The controller not only tolerates plant deviation, but also guarantees asymptotical stability of the closed‐loop system. Two numerical examples are provided to demonstrate the performance of the controller.     

Finite‐time geometric control for underactuated aerial manipulators with unknown disturbances

In this article, the finite‐time geometric control for underactuated aerial manipulators is investigated. The dynamics of the aerial manipulator with unknown disturbances is analyzed first. The dynamics of the system is decomposed into the locked subsystem and shape subsystem. The finite‐time controller for the aerial manipulator is then designed based on the analyzed dynamics. In the controller, the attitude tracking error of the aircraft base is expressed from the rotation matrix, which makes the controller continuous and almost globally stable on SO(3). A continuous adaptive term is added in the controller to compensate for the unknown disturbances. Finite‐time filters are designed to ensure the smoothness of the commands on each loop. The convergence of the entire controlled system is strictly proved using Lyapunov theory and the definition of finite‐time stability. The results show that the tracking error and the disturbance bound estimation error of the entire system are finite‐time bounded near origin. Finally, comparative simulation results are presented to show the performance of the proposed controller.     

智能控制与复合控制在二阶时变纯滞后对象的应用研究

针对二阶时变纯滞后对象难以控制的问题,提出了采用改进Smith预估器提高系统的稳定性和鲁棒性;采用CMAC和PID并行控制的算法来提高动态性能;以CMAC神经网络作为一个前馈控制器,通过对PID控制器输出的学习,实现二阶变时滞系统的自适应稳定控制。仿真实验表明,这种复合控制方法保留了Smith算法与CMAC神经网络和PID算法的各自特长,具有学习速度快,适应能力强、动态性能好的优点,具有良好的稳定性控制效果。     

Development of a Novel Self‐Tuned Adaptive Supervisory Cerebellar Model Articulation Controller for Induction Motor Drive

This work presents a novel speed control scheme for an induction motor (IM) using an adaptive supervisory differential cerebellar model articulation controller (ASDCMAC). The ASDCMAC has a supervisory controller and an adaptive differential cerebellar model articulation controller (ADCMAC), and the ASDCMAC is utilized as the speed controller. The supervisory controller monitors the control process to keep speed tracking error within a predefined range, and the ADCMAC learns and approximates system dynamics. The connective weights of ADCMAC are adjusted online, according to adaptive rules derived in Lyapunov stability theory, to ensure system stability. The robustness of the proposed ASDCMAC against parameter variations and external load torque disturbances is verified via simulations and experiments, respectively. Three control schemes, the ASDCMAC, fuzzy control, and PI control, are investigated experimentally, and a performance index, root mean square error (RMSE), is utilized for each scheme. The experimental results demonstrate that the ASDCMAC outperforms the two other control schemes with external load torque variations.     

Integrated vehicle dynamics management for distributed‐drive electric vehicles with active front steering using adaptive neural approaches against unknown nonlinearity

This paper proposes a new integrated vehicle dynamics management for enhancing the yaw stability and wheel slip regulation of the distributed‐drive electric vehicle with active front steering. To cope with the unknown nonlinear tire dynamics with uncertain disturbances in integrated control problem of vehicle dynamics, a neuro‐adaptive predictive control is therefore proposed for multiobjective coordination of constrained systems with unknown nonlinearity. Unknown nonlinearity with unmodeled dynamics is modeled using a random projection neural network via adaptive machine learning, where a new adaptation law is designed in premise of Lyapunov stability. Given the computational efficiency, a neurodynamic method is extended to solve the constrained programming problem with unknown nonlinearity. To test the performance of the proposed control method, simulations were conducted using a validated vehicle model. Simulation results show that the proposed neuro‐adaptive predictive controller outperforms the classical model predictive controller in tracking nominal wheel slip ratio, desired vehicle yaw rate and sideslip angle, showing its significance in vehicle yaw stability enhancement and wheels slip regulation.     

Robust Saturated PI Joint Velocity Control for Robot Manipulators

It is well known that many industrial manipulators use an embedded linear proportional‐integral (PI) joint velocity controller to guarantee motion control through proper velocity commands. However, although this control scheme has been very successful in practice, not much attention has been paid to designing new PI velocity control structures. The problem of analyzing a saturated PI velocity joint velocity controller is addressed in this paper. By using the theory of singularly perturbed systems, the closed‐loop system is studied. The robot dynamics assumed in this paper take into account bounded time–varying disturbances which may include the friction at the joints. An experimental study in a planar two degrees‐of‐freedom direct‐drive robot is also presented, confirming the advantage of the new saturated PI joint velocity controller.