Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems

In this paper, we address the problem of neural networks (NNs) stabilization and disturbance rejection for a class of nonlinear switched impulsive systems. An adaptive NN feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy based on NN. The NN is used to compensate for the nonlinear uncertainties of switched impulsive systems, and the approximation error of NN is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Impulsive controller is designed to attenuate effect of switching impulse. Under all admissible switching law, impulsive controller and adaptive NN feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched impulsive system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control and stabilization methods.     

Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form

An adaptive neural network (NN)-based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which are represented in non-strict feedback form. The NN backstepping approach is utilized to design the adaptive output feedback controller consisting of: (1) an NN observer to estimate the system states and (2) two NNs to generate the virtual and actual control inputs, respectively. The non-causal problem encountered during the control design is overcome by using a dynamic NN which is constructed through a feedforward NN with a novel weight tuning law. The separation principle is relaxed, persistency of excitation condition (PE) is not needed and certainty equivalence principle is not used. The uniformly ultimate boundedness (UUB) of the closed-loop tracking error, the state estimation errors and the NN weight estimates is demonstrated. Though the proposed work is applicable for second order nonlinear discrete-time systems expressed in non-strict feedback form, the proposed controller design can be easily extendable to an nth order nonlinear discrete-time system.     

Nonlinear asymptotic attitude tracking control of an underactuated 3-degree-of-freedom helicopter using neural network feedforward term

This paper proposes a new asymptotic attitude tracking controller for an underactuated 3-degree-of-freedom (DOF) laboratory helicopter system by using a nonlinear robust feedback and a neural network (NN) feedforward term. The nonlinear robust control law is developed through a modified inner-outer loop approach. The application of the NN-based feedforward is to compensate for the system uncertainties. The proposed control design strategy requires very limited knowledge of the system dynamic model, and achieves good robustness with respect to system parametric uncertainties. A Lyapunov-based stability analysis shows that the proposed algorithms can ensure asymptotic tracking of the helicopter’s elevation and travel motion, while keeping the stability of the closed-loop system. Real-time experiment results demonstrate that the controller has achieved good tracking performance.     

Adaptive neural control with fast approximation for uncertain nonlinear systems: A novel composite learning approach

In existing adaptive neural control approaches, only when the regressor satisfies the persistent excitation (PE) or interval excitation (IE) conditions, the constant optimal weights of neural network (NN) can be identified, which can be used to establish uncertainties in nonlinear systems. This paper proposes a novel composite learning approach based on adaptive neural control. The focus of this approach is to make the NN approximate uncertainties in nonlinear systems quickly and accurately without identifying the constant optimal weights of the NN. Hence, the regressor does not need to satisfy the PE or IE conditions. In this paper, regressor filtering scheme is adopted to generate prediction error, and then the prediction error and tracking error simultaneously drive the update of NN weights. Under the framework of Lyapulov theory, the proposed composite learning approach can ensure that approximation error of the uncertainty and tracking error of the system states converge to an arbitrarily small neighborhood of zero exponentially. The simulation results verify the effectiveness and advantages of the proposed approach in terms of fast approximation.     

弧焊机器人焊缝跟踪神经网络控制器

介绍了一种能提高高弧焊机器人焊缝跟踪精度的神经网络控制器,通过神经网络的补偿作用,弥补了由于无法知道机器人精确模型所造成的控制上的误差,不同于机器人控制中传统的网络控制器,本文提出并应用了基于笛卡尔空间轨迹控制的机器人焊缝跟踪神经网络,大大简化了控制算法,计算机模拟及实验表明,该控制器非常适用于只的实际焊接,对于现有机器人,无须改变其控制器内部结构,即可应用该技术,与常用的机器人关节力矩控制法相比     

An interval type-2 fuzzy logic system-based method for prediction interval construction

This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise.     

Neural-Network-Based State Feedback Control of a Nonlinear Discrete-Time System in Nonstrict Feedback Form

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.      

Neural network based iterative learning predictive control design for mechatronic systems with isolated nonlinearity

The paper presents a new nonlinear predictive control design for a kind of nonlinear mechatronic drive systems, which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. The nonlinear system is first treated into an equal linear time-variant system plus a nonlinear part using a neural network, then an iterative learning linear predictive controller is developed with a similar structure of PI optimal regulator and with setpoint feed forward control. Because the overall control law is a linear one, this design gives a direct and also effective multi-step prediction method and avoids the complicated nonlinear optimization. The control law is also an accurate one compared with traditional linearized method. Besides, changes of the system state variables are considered in the objective function with control performance superior to conventional state space predictive control designs which only consider the predicted output errors. The proposed method is compared with conventional state space predictive control method and classical PI optimal control method. Tracking performance, robustness and disturbance rejection are enlightened.     

Nonlinear Adaptive Model Predictive Control of Constrained Systems with Offset‐Free Tracking Behavior

In this paper, a nonlinear model‐based predictive control strategy for constrained systems based on an adaptive neural network (NN) predictor is proposed. The proposed controller is robust against the model uncertainties and external bounded disturbances. Moreover, it provides offset‐free tracking behavior using the adaptive structure in the model. Based on the uncertainties bounds, the restriction of the system constraints causes robust feasibility and stability of the closed‐loop system. It is shown that the output of the NN predictor converges to the system output. Moreover, offset‐free behavior of the closed‐loop system is investigated using the Lyapunov theorem. Simulation results show the effectiveness of the proposed method as compared to the recently proposed model predictive control methods in the literature.     

基于神经网络的小型无人直升机非线性鲁棒控制设计

针对小型无人直升机的姿态控制问题,为补偿系统参数不确定性和外界扰动的影响,设计一种连续的非线性鲁棒控制器.首先,利用神经网络在线估计系统不确定性,采用基于误差符号函数积分的鲁棒控制算法抑制外界扰动,同时补偿神经网络估计误差; 然后,利用基于Lyapunov函数的分析方法,证明所设计控制器的闭环稳定性,确保无人直升机姿态误差的半全局渐近收敛;最后,在无人直升机飞行实验平台上,进行无人机抗风扰控制实验.实验结果表明,所提出的控制方法具有良好的控制效果,对系统不确定性和外界扰动具有良好的鲁棒性.     

An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics

This paper proposes an efficient neural network (NN) approach to tracking control of an autonomous surface vehicle (ASV) with completely unknown vehicle dynamics and subject to significant uncertainties. The proposed NN has a single-layer structure by utilising the vehicle regressor dynamics that expresses the highly nonlinear dynamics in terms of the known and unknown dynamic parameters. The learning algorithm of the NN is simple yet computationally efficient. It is derived from Lyapunov stability analysis, which guarantees that all the error signals in the control system are uniformly ultimately bounded (UUB). The proposed NN approach can force the ASV to track the desired trajectory with good control performance through the on-line learning of the NN without any off-line learning procedures. In addition, the proposed controller is capable of compensating bounded unknown disturbances. The effectiveness and efficiency are demonstrated by simulation and comparison studies.     

基于神经网络的单相有源滤波器

有源滤渡器的控制是一个典型的非线性控制过程．非常适合用神经网络来实现．本文提出了一种应用于有源滤波器系统的神经网络控制器,神经网络控制器的输入是负载电流和补偿电流。输出是开关控制信号甩于控制有源滤波器产生补偿电流来抵消非线性负载的畸变电流。基于MATLAB/SIMULINK平台．建立了单相有源滤波器仿真模型．仿真结果表明所提出的神经网络控制器的有效性。     

电驱动刚性机器人的鲁棒神经网络复合控制

采用逐步逆向的设计思想，提出一种新的电驱动刚性机器人轨迹跟踪的鲁棒神经网络复合控制策略，该策略不仅能有效地消除模型不确定性的影响，而且可避免复杂的求导运算以及对关节角加速度可测的要求。给出了控制器的具体组成和神经网络连接权的在线学习算法，理论表明该算法能保证跟踪误差及神经网络连接权估计最终一致有界，仿真结果也验证了算法的有效性。     

Fuzzy adaptive single neuron NN control of brushless DC motor

Inherently, the brushless DC motor (BLDCM) is a nonlinear plant. So, it is hard to get a good performance by using the conventional PI controller for the speed control of BLDCM. In this paper, a fuzzy adaptive single neuron neural networks (NN) controller for BLDCM is developed. The fuzzy logic system (FLS) is adopted to adjust the parameter K of single neuron NN controller online. By this way, performance of the system can be improved. Performances of the proposed fuzzy adaptive single neuron NN controller are compared with the performances of conventional PI controller and normal single neuron NN controller. The experimental results demonstrate that a good control performance is achieved. The using of fuzzy adaptive single neuron NN makes the drive system robust, accurate, and insensitive to parameter variations.     

Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

This paper presents a novel decentralized filtering adaptive constrained tracking control framework for uncertain interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, a piecewise constant adaptive law will generate total uncertainty estimates by solving the error dynamics between the host system and decentralized state predictor with the neglection of unknowns, whereas a decentralized filtering control law is designed to compensate both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. In the control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low‐pass filters, while the trade‐off between tracking and constraints violation avoidance is formulated as a numerical constrained optimization problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. The uniform performance bounds are derived for the system states and control inputs as compared to the corresponding signals of a bounded closed‐loop reference system, which assumes partial cancelation of uncertainties within the bandwidth of the control signal. Compared with model predictive control (MPC) and unconstrained controller, the proposed control architecture is capable of solving the tracking control problems for interconnected nonlinear systems subject to constraints and uncertainties.     

基于RBFN的伺服系统逆控制器设计及仿真研究

飞行仿真转台伺服系统中有一些诸如负载变化、机械摩擦等不确定和非线性因素，而神经网络逆控制对模型的不依赖性能较好的解决这些问题。该文给出了转台伺服系统速度跟踪的神经网络逆控制方案，它可以克服转台中负载变化及一些参数变化的影响，且能显著的提高动态精度。通过使用RBF神经网络实现了对象逆动态模型的在线辨识。并直接将该RBFN与PI环节构成一种神经网络逆控制制器，仿真结果表明这种方法具有较好的鲁棒性及较高的跟踪精度，有实际应用价值。     

Output Feedback Control of a Quadrotor UAV Using Neural Networks

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.      

A DIRECT APPROACH TO THE DESIGN OF ROBUST TRACKING CONTROLLERS FOR UNCERTAIN DELAY SYSTEMS

The robust tracking and model following problem of linear uncertain time‐delay systems is investigated in this paper. By using the solution of the algebraic Riccati equation, this paper presents a direct approach to the design of robust tracking controllers. The system is controlled to track dynamic inputs generated from a reference model. In the case of matched uncertainties, the proposed controller ensures uniform ultimate boundedness of tracking errors and, furthermore, the bounds can be made arbitrarily small. In the case of mismatched uncertainties, a sufficient condition is presented such that the controller guarantees uniform ultimate boundedness of tracking errors. Compared with existing results, the main feature of the approach proposed in this paper is that it does not require any precompensator even for the non‐Hurwitz nominal system and, obviously, it is a direct method. It also employs linear controllers rather than nonlinear ones. Therefore, the designing method is simple for use and the resulting controller is easy to implement. Numerical examples show that this scheme can accommodate larger uncertainties and is likely to produce less conservative results.     

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.