Relay Switching Controller with Finite Time Tracking for Rigid Robotic Manipulators

A global relay switching control scheme with finite time convergence (FTC) is proposed for multi-joint rigid robotic manipulator systems with uncertain dynamics.For a general finite time controller,the control signals may tend to infinity in finite time when the initial states of the system are in some specified area,causing the singularity problem of the closed-loop system and finite escape. The design scheme for finite time tracking control uses a relay switching control method so that the boundedness of the control signal is guaranteed and the singularity phenomenon is avoided.The validity of our scheme is demonstrated by simulation results.     

Fault Accommodation for a Class of Nonlinear Uncertain Systems With Event-Triggered Input

The event-triggered fault accommodation problem for a class of nonlinear uncertain systems is considered in this paper.The control signal transmission from the controller to the system is determined by an event-triggering scheme with relative and constant triggering thresholds.Considering the event-induced control input error and system fault threat,a novel eventtriggered active fault accommodation scheme is designed,which consists of an event-triggered nominal controller for the time period before detecting the occurrence of faults and an adaptive approximation based event-triggered fault accommodation scheme for handling the unknown faults after detecting the occurrence of faults.The closed-loop stability and inter-event time of the proposed fault accommodation scheme are rigorously analyzed.Special cases for the fault accommodation design under constant triggering threshold are also derived.An example is employed to illustrate the effectiveness of the proposed fault accommodation scheme.     

Robust adaptive neuro-fuzzy control of uncertain nonholonomic systems

In this paper, we present an adaptive neuro-fuzzy controller design for a class of uncertain nonholonomic systems in the perturbed chained form with unknown virtual control coefficients and strong drift nonlinearities. The robust adaptive neuro-fuzzy control laws are developed using state scaling and backstepping. Semiglobal uniform ultimate bound-edness of all the signals in the closed-loop are guaranteed, and the system states are proven to converge to a small neigh-borhood of zero. The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters. By using fuzzy logic approximation, the proposed control is free of control singularity problem. An adaptive control-based switching strategy is proposed to overcome the uncontrollability problem associated with x 0 (t 0 ) = 0.     

Robustness assessment and adaptive FDI for car engine

A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.     

Robust adaptive control for uncertain nonlinear systems with unknown control directions and actuator nonlinearity

A robust adaptive control scheme is proposed for a class of uncertain nonlinear systems in strict feedback form with both unknown control directions and non-symmetric dead-zone nonlinearity based on backstepping design.The conditions that the dead-zone slopes and the boundaries are equal and symmetric are removed by simplifying nonlinear dead-zone input model,the assumption that the priori knowledge of the control directions to be known is eliminated by utilizing Nussbaum-type gain technique and neural networks(NN) approximation capability.The possible controller singularity problem and the effect of dead-zone input nonlinearity are avoided perfectly by combining integral Lyapunov design with sliding mode control strategy.All the signals in the closed-loop system are guaranteed to be semi-globally uniformly ultimately bounded and the tracking error of the system is proven to be converged to a small neighborhood of the origin.Simulation results demonstrate the effectiveness of the proposed control scheme.     

Critical dwell time of switched linear systems

In this paper, we consider the relation between the switching dwell time and the stabilization of switched linear control systems. First of all, a concept of critical dwell time is given for switched linear systems without control inputs, and the critical dwell time is taken as an arbitrary given positive constant for a switched linear control systems with controllable switching models. Secondly, when a switched linear system has many stabilizable switching models, the problem of stabilization of the overall system is considered. An on-line feedback control is designed such that the overall system is asymptotically stabilizable under switching laws which depend only on those of uncontrollable subsystems of the switching models. Finally, when a switched system is partially controllable （While some switching models are probably unstabilizable）, an on-line feedback control and a cyclic switching strategy are designed such that the overall system is asymptotically stabilizable if all switching models of this uncontrollable subsystems are asymptotically stable. In addition, algorithms for designing switching laws and controls are presented.     

Output feedback adaptive super twisting sliding mode control for quadrotor UAVs

In this paper, a sliding mode control with adaptive gain combined with a high-order sliding mode observer to solve the tracking problem for a quadrotor UAV is addressed, in presence of bounded external disturbances and parametric uncertainties. The high order sliding mode observer is designed for estimating the linear and angular speed in order to implement the proposed scheme. Furthermore, a Lyapunov function is introduced to design the controller with the adaptation law, whereas an analysis of finite time convergence towards to zero is provided, where sufficient conditions are obtained. Regarding previous works from literature, one important advantage of proposed strategy is that the gains of control are parameterized in terms of only one adaptive parameter, which reduces the control effort by avoiding gain overestimation. Numerical simulations for tracking control of the quadrotor are given to show the performance of proposed adaptive control–observer scheme.     

Feedback stabilization of $N$-dimensional stochastic quantum systems based on bang-bang control

For an $N$-dimensional quantum system under the influence of continuous measurement, this paper presents a switching control scheme where the control law is of bang-bang type and achieves asymptotic preparation of an arbitrarily given eigenstate of a non-degenerate and degenerate measurement operator, respectively. In the switching control strategy, we divide the state space into two parts: a set containing a target state, and its complementary set. By analyzing the stability of the stochastic system model under consideration, we design a constant control law and give some conditions that the control Hamiltonian satisfies so that the system trajectories in the complementary set converge to the set which contains the target state. Further, for the case of a non-degenerate measurement operator, we show that the system trajectories in the set containing the target state will automatically converge to the target state via quantum continuous measurement theory; while for the case of a degenerate measurement operator, the corresponding system trajectories will also converge to the target state via the construction of the control Hamiltonians. The convergence of the whole closed-loop systems under the cases of a non-degenerate and a degenerate measurement operator is strictly proved. The effectiveness of the proposed switching control scheme is verified by the simulation experiments on a finite-dimensional angular momentum system and a two-qubit system.     

Joint resource allocation for lifetime-aware cooperative multi-carrier DF system

In this paper,we study the problem of optimal resource allocation for lifetime maximization in an orthogonal-frequencydivision multiplexing(OFDM)system with decode-and-forward relay.The goal is to minimize total energy cost of the system by jointly optimizing power allocation,subcarrier pairing and relay selection.We present a heuristic solution that is composed of two parts.The first part is an optimal power allocation approach to allocate power to a subcarrier pair of the source and the relay.The second part is a modified Hungarian algorithm to make subcarrier pairing and relay selection.Evaluations show that the presented scheme outperforms other schemes in the total transmitted data and the network lifetime.     

Automatic Train Operation Based on Adaptive Terminal Sliding Mode Control

This paper presents an adaptive terminal sliding mode control(ATSMC) method for automatic train operation. The criterion for the design is keeping high-precision tracking with relatively less adjustment of the control input. The ATSMC structure is designed by considering the nonlinear characteristics of the dynamic model and the parametric uncertainties of the train operation in real time. A nonsingular terminal sliding mode control is employed to make the system quickly reach a stable state within a finite time, which makes the control input less adjust to guarantee the riding comfort. An adaptive mechanism is used to estimate controller parameters to get rid of the need of the prior knowledge about the bounds of system uncertainty. Simulations are presented to demonstrate the effectiveness of the proposed controller, which has robust performance to deal with the external disturbance and system parametric uncertainties. Thereby, the system guarantees the train operation to be accurate and comfortable.     

Terminal sliding mode control design for uncertain dynamic systems

A terminal sliding mode control design scheme for uncertain dynamic systems in the pure-feedback form is presented in this paper. This design employs a recursive procedure which utilizes a set of switching manifolds to realize finite time convergence. To avoid a singularity problem, the scheme uses two-phase control: one phase is a preterminal sliding mode control that transfers the trajectory to a specified open region in which the terminal sliding mode control is not singular. Inside the region, the other phase – the terminal sliding mode control takes place bringing the state to the origin in finite time.     

不确定非线性系统的自适应最终滑模控制──Backstepping方法(英)

对具有参数不确定与未知非线性的一类非线性系统，本文通过引入快慢两种切换线给出了一种自适应有限时间滑模控制机制．Backstepping方法被应用到设计中．此种控制机制保证了闭环系统的稳定性并使状态在有限时间内收敛到原点．仿真结果表明该控制机制的有效性．     

无奇异间接迭代学习控制及其在机器人运动模仿中的应用

针对相当广泛的一类非线性系统有限时间轨迹跟踪问题,提出了间接迭代学习方案. 采用最小二乘算法,根据重复跟踪历史辨识非线性系统的线性化模型.利用一个分段学习方案 可保证学习控制总在有效线性近似区域内进行.探讨了如何在学习过程中避免控制奇异问题, 提出了一种高效的参数修正方法,保证输入耦合矩阵的估计行列式不为零.本文将这一控制方 案应用于未知机器人及摄像机模型下的机器人运动模仿中,而不面临任何奇异问题.这是一个 采用摄像机替代传统程序编写的新的机器人编程方法.     

Time-Optimal Control of T--S Fuzzy Models via Lie Algebra

This paper investigates a geometric property of time-optimal problem in the Takagi–Sugeno (T--S) fuzzy model via Lie algebra. We will focus on the existence of a time-optimal solution, singularity of switching function, and number of switching. These inherent problems are considered because of their rich geometric properties. The sufficient condition for the existence of a time-optimal solution reveals the controllability of T--S fuzzy model that can be found by the generalized rank condition. The time-optimal controller can be found as the bang–bang type with a finite number of switching by applying the maximum principle. In the study of the singularity problem, we will focus on the switching function whenever it vanishes over a finite time interval. Finally, we show that the bounded number of switching can be found if the T--S model (also a nonlinear system) is solvable.      

On maximal deviation of linear system

An analytical solution of the B.V. Bulgakov problem of maximum of the norm of finite state of the stationary linear system with one control (perturbation) function taking values over an interval was proposed. The switching function of the worst relay perturbation was shown to depend on time and coordinates and be the product of the phase system vector, its transition matrix transposed to it, and the transposed column of the coefficients at perturbation.     

Linear–quadratic switching control with switching cost

We study in this paper the linear–quadratic (LQ) optimal control problem of discrete-time switched systems with a constant switching cost for both finite and infinite time horizons. We reduce these problems into an auxiliary problem, which is an LQ optimal switching control problem with a cardinality constraint on the total number of switchings. Based on the solution structure derived from the dynamic programming (DP) procedure, we develop a lower bounding scheme by exploiting the monotonicity of the Riccati difference equation. Integrating such a lower bounding scheme into a branch and bound (BnB) framework, we offer an efficient numerical solution scheme for the LQ switching control problem with switching cost.     

Wavelet based control for a class of delayed nonlinear systems with input constraints

In this paper, a delay independent adaptive control strategy is presented for a class of uncertain, delayed nonlinear system subjected to actuator saturation. In proposed control scheme wavelet networks are used for approximation of unknown system dynamics as well as a wavelet based compensator is designed to deal with actuator saturation. Delayed wavelet networks are used for identification of unknown system dynamics having state delayed terms, thereby the approximation capabilities of delayed wavelet network are utilized. Adaptation laws are developed for the online tuning of wavelet parameters. Adaptation singularity problem is solved by employing a switching scheme. The stability of closed loop system and ultimate upper boundedness all closed loop signals is proved by constructing a Lyapunov–Krasovskii functional.     

Multiple model adaptive control for switched linear systems: A two‐layer switching strategy

In this paper, the multiple model adaptive control scheme is first introduced into a class of switched systems. A switched multiple model adaptive control scheme is proposed to improve the transient behavior by resetting the controller parameters. Firstly, a finite‐time parameter identification model is presented, which greatly reduces the number of identification models. Secondly, a two‐layer switching strategy is constructed. The outer layer switching mechanism is to ensure the stability of the switched systems. The inner layer switching mechanism is to improve the transient behavior. Then, by using the constructed jumping multiple Lyapunov functions, the proposed adaptive control scheme guarantees that all the closed‐loop system signals remain bounded and the state tracking error converges to a small ball whose radius can be made arbitrarily small by appropriately choosing the design parameter. Finally, a practical example about model reference adaptive control of an electrohydraulic system using multiple models is given to demonstrate the validity of the main results.