Design of a Networked Tracking Control System With a Data-based Approach

Tracking control is a very challenging problem in the networked control system(NCS), especially for the process with blurred mechanism and where only input-output data are available. This paper has proposed a data-based design approach for the networked tracking control system(NTCS). The method utilizes the input-output data of the controlled process to establish a predictive model with the help of fuzzy cluster modelling(FCM)technology. Then, the deduced error and error change in the future are treated as inputs of a fuzzy sliding mode controller(FSMC) to obtain a string of future control actions. These candidate control actions in the controller side are delivered to the plant side. Thus, the network induced time delays are compensated by selecting appropriate control action. Simulation outputs prove the validity of the proposed method.

Robust Fuzzy Tracking Control for Nonlinear Networked Control Systems with Integral Quadratic Constraints

This paper investigates the robust tracking control problcm for a class of nonlinear networked control systems （NCSs） using the Takagi-Sugeno （T-S） fuzzy model approach. Based on a time-varying delay system transformed from the NCSs, an augmented Lyapunov function containing more useful information is constructed. A less conservative sufficient condition is established such that the closed-loop systems stability and time-domain integral quadratic constraints （IQCs） are satisfied while both time-varying network- induced delays and packet losses are taken into account. The fuzzy tracking controllers design scheme is derived in terms of linear matrix inequalities （LMIs） and parallel distributed compensation （PDC）. Furthermore, robust stabilization criterion for nonlinear NCSs is given as an extension of the tracking control result. Finally, numerical simulations are provided to illustrate the effectiveness and merits of the proposed method.

Tracking Control of Multi-Agent Systems Using a Networked Predictive PID Tracking Scheme

With the rapid development of network technology and control technology,a networked multi-agent control system is a key direction of modern industrial control systems,such as industrial Internet systems.This paper studies the tracking control problem of networked multi-agent systems with communication constraints,where each agent has no information on the dynamics of other agents except their outputs.A networked predictive proportional integral derivative(PPID)tracking scheme is proposed to achieve the desired tracking performance,compensate actively for communication delays,and simplify implementation in a distributed manner.This scheme combines the past,present and predictive information of neighbour agents to form a tracking error signal for each agent,and applies the proportional,integral,and derivative of the agent tracking error signal to control each individual agent.The criteria of the stability and output tracking consensus of multi-agent systems with the networked PPID tracking scheme are derived through detailed analysis on the closed-loop systems.The effectiveness of the networked PPID tracking scheme is illustrated via an example.

Robust H∞ output tracking control of uncertain networked control systems

This paper investigates a problem of robust output tracking control of networked control systems(NCSs) with network-induced delays, packet dropouts, parameter uncertainties and external disturbances. Firstly, an augmented model of time-delay system is proposed for networked tracking control systems. Then, considering the piecewise differentiable characteristic of time-delay, the criterion to output tracking performance analysis and controller design are derived by using an approach of free weighting matrix, reciprocally convex and cone complementarity linearization(CCL). Finally, simulation results of numerical examples show the effectiveness of the proposed approach, and illustrate the advantages of the proposed criteria which outperform previous criteria in the literature.

Distributed Adaptive Tracking Control for Unknown Nonlinear Networked Systems

在这份报纸，我们为易于一个积极领导人，其仅仅说罐头的非线性的不明确的联网的系统的一个类调查合作追踪问题部分被测量，输入隧道也被扰乱。由神经网络(NN ) 的优点技术，追随者的动力学适当地在某些基础功能上被建模，他们的输入隧道被假定也被扰乱。在这个工作，基于观察员的适应控制为可以有非相同的动力学的非线性的联网的系统被建议。它被适当地在一些图状况下面选择参数经由 Lyapunov 理论(UUB ) 显示出全面系统最终一致地合作地被围住。最后，几数字模拟为建议适应控制器的确认被详细描述。

Networked guaranteed cost PID attitude tracking control of a laboratory flight system

This paper investigates the networked flight control system for a laboratory 3 degrees of freedom (3-DOF) helicopter,and presents a novel networked guaranteed cost proportion-integration-differentiation (PID) attitude tracking control method with consideration of time-varying delay and packet dropouts.As the 3-DOF helicopter characteristics of multi-input multi-output (MIMO),channel coupling,and nonlinearity,a genera linear time delay system is modeled by analyzing the motions on elevation,pitch,and travel axis.By using the reciprocal convex approach,the free weight matrix,and the cone complementarity linearization (CCL) method,the PID tracking controller parameters can be designed if the related linear matrix inequalities (LMIs) are feasible.Finally,a practical experiment of laboratory 3-DOF helicopter is given,and the experimental results show that the proposed method is effective.

Parallel Control for Optimal Tracking via Adaptive Dynamic Programming

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems.Unlike existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback system.However,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied directly.To address this problem,an augmented system and an augmented performance index function are proposed firstly.Thus,the general nonlinear system is transformed into an affine nonlinear system.The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically.It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function.Moreover,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function online.The stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals.Finally,the effectiveness of the developed optimal parallel control method is verified in two cases.

Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller （NNC） is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A twolayer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded （UUB） stabilities of tracking errors and NN weights are guaran- teed through the proposed controller. An on-line NN weights tuning algorithm is also propesed. Good performances of the tracking control system are illustrated bv the results of numerical simulations.

Dynamics Modeling and Robust Trajectory Tracking Control for a Class of Hybrid Humanoid Arm Based on Neural Network

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm（HHA）, which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network（NN）-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function（RBF） NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.

Predictive Tracking Control of Network-Based Agents With Communication Delays

This paper investigates the remote tracking control problem of Network-based Agents with communication delays existing in both forward and feedback communication channels.A networked predictive tracking controller is proposed to compensate the negative effects caused by bilateral time-delays in a wireless network. Furthermore, the problem of consecutive data loss in the feedback channel is solved using aforementioned controller, where lateral movement perturbations are introduced.Simulations and experiments are provided for several cases,which verify the realizability and effectiveness of the proposed controller.

Toward Coordination Control of Multiple Fish-Like Robots:Real-Time Vision-Based Pose Estimation and Tracking via Deep Neural Networks

Controlling multiple multi-joint fish-like robots has long captivated the attention of engineers and biologists,for which a fundamental but challenging topic is to robustly track the postures of the individuals in real time.This requires detecting multiple robots,estimating multi-joint postures,and tracking identities,as well as processing fast in real time.To the best of our knowledge,this challenge has not been tackled in the previous studies.In this paper,to precisely track the planar postures of multiple swimming multi-joint fish-like robots in real time,we propose a novel deep neural network-based method,named TAB-IOL.Its TAB part fuses the top-down and bottom-up approaches for vision-based pose estimation,while the IOL part with long short-term memory considers the motion constraints among joints for precise pose tracking.The satisfying performance of our TAB-IOL is verified by testing on a group of freely swimming fish-like robots in various scenarios with strong disturbances and by a deed comparison of accuracy,speed,and robustness with most state-of-the-art algorithms.Further,based on the precise pose estimation and tracking realized by our TAB-IOL,several formation control experiments are conducted for the group of fish-like robots.The results clearly demonstrate that our TAB-IOL lays a solid foundation for the coordination control of multiple fish-like robots in a real working environment.We believe our proposed method will facilitate the growth and development of related fields.

A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely manner.This study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed conditions.Finally,the effectiveness of this method is verified via Simulink simulation.

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are discussed.At first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler method.Based on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law.The closed chain system is formed in the post-impact phase.Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics.With the closed chain constraint equations,the composite system dynamic equations are derived.Secondly,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter.In order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking characteristic.According to the Lyapunov theory,the global stability is demonstrated.Meanwhile,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators.At last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

A Bio-Inspired Global Finite Time Tracking Control of Four-Rotor Test Bench System

A bio-inspired global finite time control using global fast-terminal sliding mode controller and radial basis function network is presented in this article,to address the attitude tracking control problem of the three degree-of-freedom four-rotor hover system.The proposed controller provides convergence of system states in a predetermined finite time and estimates the unmodeled dynamics of the four-rotor system.Dynamic model of the four-rotor system is derived with Newton’s force equations.The unknown dynamics of four-rotor systems are estimated using Radial basis function.The bio-inspired global fast terminal sliding mode controller is proposed to provide chattering free finite time error convergence and to provide optimal tracking of the attitude angles while being subjected to unknown dynamics.The global stability proof of the designed controller is provided on the basis of Lyapunov stability theorem.The proposed controller is validated by(i)conducting an experiment through implementing it on the laboratory-based hover system,and(ii)through simulations.Performance of the proposed control scheme is also compared with classical and intelligent controllers.The performance comparison exhibits that the designed controller has quick transient response and improved chattering free steady state performance.The proposed bioinspired global fast terminal sliding mode controller offers improved estimation and better tracking performance than the traditional controllers.In addition,the proposed controller is computationally cost effective and can be implanted on multirotor unmanned air vehicles with limited computational processing capabilities.

Neural adaptive attitude tracking controller for flexible spacecraft

In this paper,a neural network adaptive controller is proposed for attitude tracking of flexible spacecraft in presence of unknown inertial matrix and external disturbance.In this approach,neural network technique is employed to approximate the unknown system dynamics with finite combinations of some basis functions,and a robust controller is also designed to attenuate the effect of approximation error,more specially,the knowledge of angular velocity is not required.In the closed-loop system,Lyapunov stability analysis shows that the attitude trajectories asymptotically follow the reference output trajectories.Finally,simulation results are presented for the attitude tracking of a flexible spacecraft to show the excellent performance of the proposed controller and illustrate its robustness in face of external disturbances and unknown dynamics.

An optimal adaptive H-infinity tracking control design via wavelet network

In this paper, an optimal adaptive H-infinity tracking control design method via wavelet network for a class of uncertain nonlinear systems with external disturbances is proposed to achieve H-infinity tracking performance. First, an alternate tracking error and a performance index with respect to the tracking error and the control effort are introduced in order to obtain better performance, especially, in reducing the cost of the control effort in the case of small attenuation levels. Next, H-infinity tracking performance, which attenuates the influence of both wavelet network approximation error and external disturbances on the modified tracking error, is formulated. Our results indicate that a small attenuation level does not lead to a large control signal. The proposed method insures an optimal trade-off between the amplitude of control signals and the performance of tracking errors. An example is given to illustrate the design efficiency.

Trajectory tracking guidance of interceptor via prescribed performance integral sliding mode with neural network disturbance observer

This paper investigates interception missiles’trajectory tracking guidance problem under wind field and external disturbances in the boost phase.Indeed,the velocity control in such trajectory tracking guidance systems of missiles is challenging.As our contribution,the velocity control channel is designed to deal with the intractable velocity problem and improve tracking accuracy.The global prescribed performance function,which guarantees the tracking error within the set range and the global convergence of the tracking guidance system,is first proposed based on the traditional PPF.Then,a tracking guidance strategy is derived using the integral sliding mode control techniques to make the sliding manifold and tracking errors converge to zero and avoid singularities.Meanwhile,an improved switching control law is introduced into the designed tracking guidance algorithm to deal with the chattering problem.A back propagation neural network(BPNN)extended state observer(BPNNESO)is employed in the inner loop to identify disturbances.The obtained results indicate that the proposed tracking guidance approach achieves the trajectory tracking guidance objective without and with disturbances and outperforms the existing tracking guidance schemes with the lowest tracking errors,convergence times,and overshoots.

Adaptive Output Tracking for Nonlinear Network Control Systems with Time-Delay

The problem of adaptive output tracking is researched for a class of nonlinear network control systems with parameter uncertainties and time-delay. In this paper, a new program is proposed to design a state-feedback controller for this system. For time-delay and parameter uncertainties problems in network control systems, applying the backstepping recursive method, and using Young inequality to process the time-delay term of the systems, a robust adaptive output tracking controller is designed to achieve robust control over a class of nonlinear time-delay network control systems. According to Lyapunov stability theory, Barbalat lemma and Gronwall inequality, it is proved that the designed state feedback controller not only guarantees the state of systems is uniformly bounded, but also ensures the tracking error of the systems converges to a small neighborhood of the origin. Finally, a simulation example for nonlinear network control systems with parameter uncertainties and time-delay is given to illustrate the robust effectiveness of the designed state-feedback controller.

Distributionally robust model predictive control for constrained robotic manipulators based on neural network modeling

A distributionally robust model predictive control(DRMPC)scheme is proposed based on neural network(NN)modeling to achieve the trajectory tracking control of robot manipulators with state and control torque constraints.First,an NN is used to fit the motion data of robot manipulators for data-driven dynamic modeling,converting it into a linear prediction model through gradients.Then,by statistically analyzing the stochastic characteristics of the NN modeling errors,a distributionally robust model predictive controller is designed based on the chance constraints,and the optimization problem is transformed into a tractable quadratic programming(QP)problem under the distributionally robust optimization(DRO)framework.The recursive feasibility and convergence of the proposed algorithm are proven.Finally,the effectiveness of the proposed algorithm is verified through numerical simulation.