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51
Adaptive neural network control of nonlinear systems by stable output feedback
 IEEE Trans. Syst., Man, Cybern. B
, 1999
"... Abstract—This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN’s). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the o ..."
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Cited by 28 (6 self)
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Abstract—This paper presents a novel control method for a general class of nonlinear systems using neural networks (NN’s). Firstly, under the conditions of the system output and its time derivatives being available for feedback, an adaptive state feedback NN controller is developed. When only the output is measurable, by using a highgain observer to estimate the derivatives of the system output, an adaptive output feedback NN controller is proposed. The closedloop system is proven to be semiglobally uniformly ultimately bounded (SGUUB). In addition, if the approximation accuracy of the neural networks is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussions. Index Terms — Adaptive control, highgain observer, neural networks, nonlinear system, output feedback control.
Deadzone Compensation in Motion Control Systems Using Neural Networks
, 2000
"... A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NN's), one to estimate the unknown deadzone and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented fo ..."
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Cited by 22 (3 self)
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A compensation scheme is presented for general nonlinear actuator deadzones of unknown width. The compensator uses two neural networks (NN's), one to estimate the unknown deadzone and another to provide adaptive compensation in the feedforward path. The compensator NN has a special augmented form containing extra neurons whose activation functions provide a "jump function basis set" for approximating piecewise continuous functions. Rigorous proofs of closedloop stability for the deadzone compensator are provided and yield tuning algorithms for the weights of the two NN's. The technique provides a general procedure for using NN's to determine the preinverse of an unknown rightinvertible function. I. INTRODUCTION A GENERAL class of industrial motion control systems has the structure of a dynamical system, usually of the Lagrangian form, preceded by some nonlinearities in the actuator, either deadzone, backlash, saturation, etc. [7]. This includespositioning tables [17], robot manip...
Dynamics and Adaptive Control for Stability Recovery
 of Damaged Asymmetric Aircraft”, AIAA Guidance, Navigation, and Control Conference
, 2006
"... This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in offnominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects ..."
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Cited by 21 (6 self)
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This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in offnominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid directindirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented: 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive leastsquare method for parameter identification. A hardwareintheloop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and highfidelity simulations of the proposed hybrid adaptive flight control approach. I.
Variable Neural Networks for Adaptive Control Of Nonlinear Systems
 IEEE Transactions on Systems Man & Cybernetics  Part C
, 1999
"... Abstract — This paper is concerned with the adaptive control of continuoustime nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamica ..."
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Cited by 15 (0 self)
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Abstract — This paper is concerned with the adaptive control of continuoustime nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time according to specified design strategies so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBF’s in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error. The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples. Index Terms — Adaptive control, neural networks, nonlinear systems, radial basis functions.
Adaptive Synchronization Design for Chaotic Systems via a Scalar Driving Signal
"... Abstract—Using a scalar driving signal, synchronization for a class of chaotic systems has been developed in this study. For chaotic systems characterized by nonlinearity, which depends only on the available output, a unified approach is developed by carefully extending the conventional adaptive obs ..."
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Cited by 10 (0 self)
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Abstract—Using a scalar driving signal, synchronization for a class of chaotic systems has been developed in this study. For chaotic systems characterized by nonlinearity, which depends only on the available output, a unified approach is developed by carefully extending the conventional adaptive observer design. For exactly known chaotic systems, an exponential convergence of synchronization is achieved in the large. When mismatched parameters are presented, this method performs the asymptotic synchronization of output state in the large. The convergence of the estimated parameter error is related to an implicit condition of persistent excitation (PE) on internal signals. From the broad spectrum characteristics of the chaotic driving signal, we reformulate the implicit PE condition as an condition on injection inputs. If this condition is satisfied, the estimated parameters converge to true values and exponential synchronization of all internal states is guaranteed. Two typical examples, including Duffing–Holmes system and Chua’s circuit, are considered as illustrations to demonstrate the effectiveness of the adaptive synchronizer. Furthermore, the robustness of adaptive synchronization in presence of measurement noise is considered where the update law is modified. Finally, numerical simulations and DSPbased experiments show the validity of theoretical derivations. Index Terms—Adaptive observer, chaotic synchronization, persistent excitation. I.
Neural Network Approximation Of Piecewise Continuous Functions: Application To Friction Compensation
, 2000
"... One of the most important properties of neural nets (NN) for control purposes is the universal approximation property. Unfortunately, this property is generally proven for continuous functions. In most real industrial control systems there are nonsmooth functions (e.g. piecewise continuous) for whic ..."
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Cited by 9 (2 self)
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One of the most important properties of neural nets (NN) for control purposes is the universal approximation property. Unfortunately, this property is generally proven for continuous functions. In most real industrial control systems there are nonsmooth functions (e.g. piecewise continuous) for which approximation results in the literature are sparse. Examples include friction, deadzone, backlash, and so on. It is found that attempts to approximate piecewise continuous functions using smooth activation functions require many NN nodes and many training iterations, and still do not yield very good results. Therefore, a novel neural network structure is given for approximation of piecewise continuous functions of the sort that appear in friction, deadzone, backlash and other motion control actuator nonlinearities. The novel NN consists of neurons having standard sigmoid activation functions, plus some additional neurons having a special class of nonsmooth activation functions termed 'jum...
Robust/Optimal Temperature Profile Control of a HighSpeed Aerospace Vehicle Using Neural Networks
, 2005
"... Abstract—An approximate dynamic programming (ADP)based suboptimal neurocontroller to obtain desired temperature for a highspeed aerospace vehicle is synthesized in this paper. A 1D distributed parameter model of a fin is developed from basic thermal physics principles. “Snapshot ” solutions of th ..."
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Cited by 8 (0 self)
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Abstract—An approximate dynamic programming (ADP)based suboptimal neurocontroller to obtain desired temperature for a highspeed aerospace vehicle is synthesized in this paper. A 1D distributed parameter model of a fin is developed from basic thermal physics principles. “Snapshot ” solutions of the dynamics are generated with a simple dynamic inversionbased feedback controller. Empirical basis functions are designed using the “proper orthogonal decomposition ” (POD) technique and the snapshot solutions. A loworder nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADPbased neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a singlenetworkadaptivecritic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NNbased controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems. Index Terms—Control of distributed parameter systems, neural networks (NNs), proper orthogonal decomposition (POD), temperature control. I.
Robust and adaptive backstepping control for nonlinear systems using RBF neural networks
 IEEE Trans. on Neural Networks
, 2004
"... Abstract—In this paper, two different backstepping neural network (NN) control approaches are presented for a class of affine nonlinear systems in the strictfeedback form with unknown nonlinearities. By a special design scheme, the controller singularity problem is avoided perfectly in both approac ..."
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Cited by 8 (0 self)
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Abstract—In this paper, two different backstepping neural network (NN) control approaches are presented for a class of affine nonlinear systems in the strictfeedback form with unknown nonlinearities. By a special design scheme, the controller singularity problem is avoided perfectly in both approaches. Furthermore, the closed loop signals are guaranteed to be semiglobally uniformly ultimately bounded and the outputs of the system are proved to converge to a small neighborhood of the desired trajectory. The control performances of the closedloop systems can be shaped as desired by suitably choosing the design parameters. Simulation results obtained demonstrate the effectiveness of the approaches proposed. The differences observed between the inputs of the two controllers are analyzed briefly. Index Terms—Adaptive control, backstepping, neural network (NN), robust adaptive control, uncertain strictfeedback system.
Robust Adaptive Control Of Underwater Vehicles: A Comparative Study
 Proc. of the 3rd IFAC Workshop on Control Applications in Marine Systems (CAMS'95
, 1995
"... . Robust adaptive control of underwater vehicles in 6 DOF is analyzed in the context of measurement noise. The performance of the adaptive control laws of Sadegh and Horowitz (1990) and Slotine and Benedetto (1990) are compared. Both these schemes require that all states are measured, that is the ve ..."
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Cited by 7 (2 self)
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. Robust adaptive control of underwater vehicles in 6 DOF is analyzed in the context of measurement noise. The performance of the adaptive control laws of Sadegh and Horowitz (1990) and Slotine and Benedetto (1990) are compared. Both these schemes require that all states are measured, that is the velocities and positions in surge, sway, heave, roll, pitch and yaw. However, for underwater vehicles it is difficult to measure the linear velocities whereas angular velocity measurements can be obtained by using a 3axes angular rate sensor. This problem is adressed by designing a nonlinear observer for linear velocity state estimation. The proposed observer requires that the position and the attitude are measured, e.g. by using a hydroacoustic positioning system for linear positions, two gyros for roll and pitch and a compass for yaw. In addition angular rate measurements will be assumed available from a 3axes rate sensor or a state estimator. It is also assumed that the measurement rate i...
Backlash Compensation In Nonlinear Systems Using Dynamic Inversion By Neural Networks
, 1999
"... A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of ..."
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Cited by 7 (4 self)
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A dynamic inversion compensation scheme is presented for backlash. The compensator uses the backstepping technique with neural networks (NN) for inverting the backlash nonlinearity in the feedforward path. The technique provides a general procedure for using NN to determine the dynamic preinverse of an invertible dynamical system. A tuning algorithm is presented for the NN backlash compensator which yields a stable closedloop system. 1 INTRODUCTION A general class of industrial motion control systems has the structure of a nonlinear dynamical system preceded by some nonlinearities in the actuator, either deadzone, backlash, saturation, etc. This includes xypositioning tables [19], robot manipulators [14], overhead crane mechanisms, and more. The problems are particularly exacerbated when the required accuracy is high, as in micropositioning devices. Due to the nonanalytic nature of the actuator nonlinearities and the fact that their exact nonlinear functions are unknown, such systems...