Adaptive neural output feedback finite-time command filtered backstepping control for nonlinear systems with full-state constraints

In this paper, an adaptive neural finite-time control method via barrier Lyapunov function, command filtered backstepping, and output feedback is proposed to solve the tracking problem of uncertain high-order nonlinear systems with full-state constraints and input saturation. By utilizing the neural network (NN) to approximate unknown nonlinear functions, the finite-time command filters are used to filtering the virtual control signals and get the intermediate control signals in a finite time in the backstepping process. Because there are errors between the output of finite-time command filters and the virtual control signals, the error compensation signals are added to eliminate the influence of filtering errors. Based on the proposed control scheme, the states of the system can be constrained in the predetermined region, all signals in the system are bounded in finite time, and the tracking error can converge to the desired region in finite time. At last, a simulation example is given to show the effectiveness of the proposed control method.     

带有饱和执行器的T-S离散模糊系统的LQ模糊控制

研究了带有饱和执行器的Takagi-SugenoT-S离散模糊系统的LQ模糊控制问题,利用Lyapunov稳定理论、PDC(平行分配补偿)技术以及线性矩阵不等式方法,得到了闭环模糊系统的渐近稳定的充分条件,给出了闭环系统的LQ模糊控制律的设计方法和吸引域的一个估计,并建立了闭环系统的LQ性能函数上界的计算公式.进一步,针对两类优化问题,即:LQ性能最小化问题和吸引域最大化问题,给出了相应的带有线性矩阵不等式约束的计算方法.最后,一个仿真例子说明了所给方法的有效性.     

执行器饱和T-S模糊系统的鲁棒耗散容错控制

研究了一类执行器饱和状态变时滞T-S模糊系统的鲁棒容错控制问题. 通过时滞相关Lyapunov函数和对状态的椭球域约束, 基于线性矩阵不等式技术, 提出了非线性系统稳定的不变集条件和模糊鲁棒耗散容错控制器存在的充分条件. 控制方案的设计结果不仅为执行器饱和状态变时滞T-S模糊系统的无源控制和H1鲁棒控制建立了统一框架, 而且保证了闭环控制系统对执行器故障的稳定性和容错性. 最后以时滞倒车系统的控制仿真验证了方法 的有效性.     

Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

模糊B样条基神经网络及其在机器人轨迹跟踪中的应用

提出一种模糊神经网络控制器并用于机器人轨迹跟踪控制．这种模糊神经网络利用B样条基函数作为隶属函数,可在线根据误差调整隶属函数的形状,使模糊神经网络具有更强的学习和适应能力．仿真与实验结果表明这种网络能很好的用于机器人的轨迹跟踪控制,具有很好的性能．     

Adaptive neural network dynamic event-triggered control for strong interconnected stochastic nonlinear systems with output constraint

This paper proposes a dynamic event-triggered mechanism based command filtered adaptive neural network (NN) tracking control scheme for strong interconnected stochastic nonlinear systems with time-varying output constraints. By designing a state observer, the unmeasured states of the systems can be estimated. The NNs are utilized to handle the unknown intermediate functions. In the controller design process, the asymmetric time-varying barrier Lyapunov functions are used to guarantee that the systems outputs do not violate the constraint regions. By integrating the command filter with variable separation technique, the controller design process is more simple, and the problem of algebraic-loop can be solved which caused by interconnected functions. According to the Lyapunov stability theory, it can be ensured that all signals of the systems are bounded in probability. Finally, the availability of the developed control scheme can be showed by the simulation example.     

带执行器饱和的柔性关节机器人位置反馈动态面控制

针对带有执行器饱和的柔性关节机器人系统,提出一种位置反馈动态面控制,以实现机器人连杆的角位置跟踪.在一般动态面控制的设计框架下,设计观测器重构系统未知速度状态,利用径向基函数神经网络学习饱和非线性特性,结合“最小参数学习”算法减轻计算负担.通过Lyapunov方法证明得出闭环系统所有信号半全局一致有界,跟踪误差可以通过调节控制器参数达到任意小.仿真结果表明,控制系统能够克服外界干扰,有效补偿系统存在的执行器饱和,实现柔性关节机器人的准确跟踪控制.该方法避免了传统反演设计存在的“微分爆炸”现象,简化了设计过程.     

Adaptive fuzzy decentralised fault-tolerant control for nonlinear large-scale systems with actuator failures and unmodelled dynamics

This paper discusses the adaptive fuzzy decentralised fault-tolerant control (FTC) problem for a class of nonlinear large-scale systems in strict-feedback form. The systems under study contain the unknown nonlinearities, unmodelled dynamics, actuator faults and without the direct measurements of state variables. With the help of fuzzy logic systems identifying the unknown functions and a fuzzy adaptive observer is designed to estimate the unmeasured states. By using the backstepping design technique and the dynamic surface control approach and combining with the changing supply function technique, a fuzzy adaptive FTC scheme is developed. The main features of the proposed control approach are that it can guarantee the closed-loop system to be input–to-state practically stable, and also has the robustness to the unmodelled dynamics. Moreover, it can overcome the so-called problem of ‘explosion of complexity’ existing in the previous literature. Finally, simulation studies are provided to illustrate the effectiveness of the proposed approach.     

Adaptive neural practical fixed-time command filtered control for multi-input and multi-output nonlinear systems with dead zones input and unknown control direction

In this article, under the circumstance of dead zones input and unknown control direction, the adaptive practical fixed-time control strategy is presented for a general class of multi-input and multi-output (MIMO) nonlinear systems. The inherent explosion of computational complexity difficulty is eliminated by adopting a command filter technique and the universal approximation properties of radial basis function neural networks (RBFNNs) are applied to model the unknown nonlinear functions. The difficulties of the dynamic surface method and unknown directions can be handled by invoking error compensation mechanism and Nussbaum-type functions, respectively. The uniqueness of the presented control scheme is that the tracking system can achieve the fixed-time stability without relying on the boundedness of dead-zone parameters. The fixed-time convergence of the output tracking error and the semiglobally fixed-time stable of closed-loop system are assured via the developed adaptive fixed-time command filtered controller. Finally, a practical example is supplied to further validate the availability of the presented theoretic result.     

Adaptive finite-time control of uncertain nonlinear systems with the powers of odd rational numbers

This paper studies adaptive finite-time control problem of a class of nonlinear systems with dynamic and parametric uncertainties. The power of systems in this paper is any positive odd rational number but not necessary equal to or greater than one. To solve this problem, finite-time input-to-state stability (FTISS) is used to characterise the unmeasured dynamic uncertainty. By skillfully combining Lyapunov function, sign function, adding a power integrator, adaptive control and FTISS methods, an adaptive state feedback controller is designed to drive the states to the origin in finite time while maintaining all the closed-loop signals bounded.     

Switching event-triggered control of switched systems with actuator saturation

This paper focuses on switching event-triggered controller design for switched continuous-time systems with actuator saturation. First, we revisit the switching event-triggered sampled-data mechanism (ETSDM) to adapt it to the state feedback control of switched systems with actuator saturation. Then, by thinking of the ETSDM as a switching between periodic sampling and continuous ETSDM, constructing a switching multiple Lyapunov functions to give the analysis and design, and adopting the sector conditions to deal with the saturation, the sufficient conditions and the initial region ensuring the exponential stability of the switched system are proposed. Furthermore, the corresponding solvable conditions for the switching event-triggered controller and the triggering parameter matrices are established. Finally, a circuit example is given to illustrate the validity of the proposed results.     

不确定非线性离散系统指令滤波事件触发控制

本文提出了考虑输入饱和的一类不确定非线性离散系统的事件触发指令滤波控制方法. 采用指令滤波控制技术解决了传统反步法存在的“因果矛盾”问题, 引入补偿机制提高了系统的控制精度; 利用事件触发机制能够避免自适应律和控制律的频繁更新, 降低了计算负担, 提高了资源利用率; 运用模糊逻辑系统逼近系统中未知的非线性函数; 结合李雅普诺夫稳定性理论, 验证了提出的控制方案能够保证跟踪误差收敛到原点小的邻域内以及闭环系统的所有信号有界. 仿真结果表明, 本文提出的控制方法具有较强的鲁棒性及较好的跟踪性能.     

Command-filter-based distributed containment control of nonlinear multi-agent systems with actuator failures

In this paper, the problem of distributed containment fault-tolerant control for a class of nonlinear multi-agent systems in strict-feedback form is studied. The considered nonlinear multi-agent systems are subject to unknown nonlinear functions and actuator faults with loss of effectiveness and lock-in-place. By resorting to the universal approximation capability of fuzzy logical systems, the command filtered backstepping technique and nonlinear fault-tolerant control theory, distributed controllers are designed recursively. From the Lyapunov stability theory, it is proved that all signals of the resulting closed-loop systems are cooperatively semi-globally uniformly ultimately bounded and the containment errors converge to a small neighbourhood of origin by properly tuning the design parameters. Finally, a numerical example is provided to show the effectiveness of the proposed control method.     

Control-affine fuzzy neural network approach for nonlinear process control

An internal model control strategy employing a fuzzy neural network is proposed for SISO nonlinear process. The control-affine model is identified from both steady state and transient data using back-propagation. The inverse of the process is obtained through algebraic inversion of the process model. The resulting model is easier to interpret than models obtained from the standard neural network approaches. The proposed approach is applied to the tasks of modelling and control of a continuous stirred tank reactor and a pH neutralization process which are not inherently control-affine. The results show a significant performance improvement over a conventional PID controller. In addition, an additional neural network which models the discrepancy between a control-affine model and real process dynamics is added, and is shown to lead to further improvement in the closed-loop performance.     

Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints

This paper focuses on the adaptive finite-time neural network control problem for nonlinear stochastic systems with full state constraints. Adaptive controller and adaptive law are designed by backstepping design with log-type barrier Lyapunov function. Radial basis function neural networks are employed to approximate unknown system parameters. It is proved that the tracking error can achieve finite-time convergence to a small region of the origin in probability and the state constraints are confirmed in probability. Different from deterministic nonlinear systems, here the stochastic system is affected by two random terms including continuous Brownian motion and discontinuous Poisson jump process. Therefore, it will bring difficulties to the controller design and the estimations of unknown parameters. A simulation example is given to illustrate the effectiveness of the designed control method.     

Quantized stabilization of networked control systems with actuator saturation

In this paper, we investigate the problems of stabilization of networked control systems with quantization and actuator saturation via delta operator approach. The definition of the domain of attraction for delta operator systems is introduced to analyze the stochastic stability of the closed‐loop networked control systems. The quantizer is a uniform one with arbitrary quantization regions, and the packet dropout process is modeled as a Bernoulli process. On the basis of the zoom strategy and Lyapunov theory in delta domain, sufficient conditions are given for the closed‐loop delta operator systems to be mean square stable, and the feedback controllers are designed to ensure the stability of networked control systems. A single link direct joint driven manipulator model is presented to show the effectiveness of the main results. Copyright © 2016 John Wiley & Sons, Ltd.     

Neural-based online finite-time optimal tracking control for wheeled mobile robotic system with inequality constraints

In this study, a finite-time online optimal controller was designed for a nonlinear wheeled mobile robotic system (WMRS) with inequality constraints, based on reinforcement learning (RL) neural networks. In addition, an extended cost function, obtained by introducing a penalty function to the original long-time cost function, was proposed to deal with the optimal control problem of the system with inequality constraints. A novel Hamilton-Jacobi-Bellman (HJB) equation containing the constraint conditions was defined to determine the optimal control input. Furthermore, two neural networks (NNs), a critic and an actor NN, were established to approximate the extended cost function and the optimal control input, respectively. The adaptation laws of the critic and actor NN were obtained with the gradient descent method. The semi-global practical finite-time stability (SGPFS) was proved using Lyapunov's stability theory. The tracking error converges to a small region near zero within the constraints in a finite period. Finally, the effectiveness of the proposed optimal controller was verified by a simulation based on a practical wheeled mobile robot model.     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

Adaptive neural network tracking control for uncertain nonlinear systems with input delay and saturation

In this article, the adaptive tracking control problem is considered for a class of uncertain nonlinear systems with input delay and saturation. To compensate for the effect of the input delay and saturation, a compensation system is designed. Radial basis function neural networks are directly utilized to approximate the unknown nonlinear functions. With the aid of the backstepping method, novel adaptive neural network tracking controllers are developed, which can guarantee all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the system output can track the desired signal with a small tracking error. In the end, a simulation example is given to illustrate the effectiveness of the proposed methods.     

Decentralized dynamic event-triggered control for networked control systems under unreliable communication network and actuator saturation

In this paper, the stochastic stability of networked control systems (NCSs) with nonlinear perturbation and parameter uncertainties is studied. A decentralized dynamic event triggering scheme (DDETS) is introduced to reduce energy consumption and network transmission burden. Each channel can decide whether to transmit signals sampled by corresponding sensors through this mechanism. Due to the unreliability of communication network, a new mathematical model of NCSs based on multi-channel fading and hybrid cyber attacks is established, and the actuator is constrained by saturation. A sufficient condition for stochastic stability based on static controller is derived through Lyapunov stability theory, and two numerical examples are given to demonstrate the effectiveness of the method.