Yongping Pan

Global Neural Dynamic Surface Tracking Control of Strict-Feedback Systems With Application to Hypersonic Flight Vehicle

This paper studies both indirect and direct global neural control of strict-feedback systems in the presence of unknown dynamics, using the dynamic surface control (DSC) technique in a novel manner. A new switching mechanism is designed to combine an adaptive neural controller in the neural approximation domain, together with the robust controller that pulls the transient states back into the neural approximation domain from the outside. In comparison with the conventional control techniques, which could only achieve semiglobally uniformly ultimately bounded stability, the proposed control scheme guarantees all the signals in the closed-loop system are globally uniformly ultimately bounded, such that the conventional constraints on initial conditions of the neural control system can be relaxed. The simulation studies of hypersonic flight vehicle (HFV) are performed to demonstrate the effectiveness of the proposed global neural DSC design.

Adaptive Fuzzy Control With Guaranteed Convergence of Optimal Approximation Error

With no a priori knowledge of plant boundary functions, a novel direct adaptive fuzzy controller (AFC) for a class of single-input single-output (SISO) uncertain affine nonlinear systems is developed in this paper. Based on the theory of fuzzy logic systems (FLSs) with variable universes of discourse (UDs), sufficient conditions that guarantee that the optimal fuzzy approximation error (FAE) is locally convergent are given. By the use of the output tracking error and its derivatives as input variables and by the selection of suitable adjusting parameters, a variable UD FLS with an optimal FAE local convergence is constructed, and its parameter adaptive law is derived by virtue of the Lyapunov stability theorem. Under the assumption that the optimal FAE is bounded, it is proved that the closed-loop system is asymptotically stable in the sense that all variables are uniformly ultimately bounded and that the tracking errors converge to zero. The proposed approach eliminates the influence of the FAE on the tracking errors by means of the inherent mechanism of the variable UD FLS. Thus, it has the potential to achieve high control performance without additional compensation under only a few fuzzy rules. Simulation studies demonstrate the superiority of the proposed AFC in terms of the settling time, tracking accuracy, smoothness of the control input, and robustness against external disturbances and parameter variations.

Composite adaptive fuzzy H∞ tracking control of uncertain nonlinear systems

In the H^~ tracking-based adaptive fuzzy controllers (HAFCs) of perturbed uncertain nonlinear systems, additional H^~ control terms would greatly degrade fuzzy approximation abilities, which violates the original intention of using fuzzy logic systems. To solve this problem, a composite HAFC (CHAFC), which combines the HAFC with composite adaptation technique, is proposed in this paper. Outside of the approximation region, a robust stabilization controller is developed to achieve semi-global stability of the closed-loop system. Within the approximation region, a series-parallel identification model is introduced into an indirect HAFC to construct a CHAFC that can simultaneously achieve fuzzy identification and H^~ tracking control. It is proved that the closed-loop system obtains H^~ tracking performance in the sense that both tracking and modeling errors converge to small neighborhoods of zero. Simulated applications of aircraft wing rock suppression and inverted pendulum tracking demonstrate that the proposed approach not only effectively solves the aforementioned approximation problem, but also obviously outperforms previous approaches.

Cooperative quantum cutting in one-dimensional (YbxGd1−x)Al3(BO3)4:Tb3+ nanorods

Near-infrared (NIR) quantum cutting (QC) involving the emission of two NIR photons per absorbed photon via a cooperative downconversion mechanism in one-dimensional (1D) (YbxGd1−x)Al3(BO3)4:Tb3+ nanorods has been demonstrated. The authors have analyzed the measured luminescence spectra and decay lifetimes and proposed a mechanism to rationalize the QC effect. Upon excitation of Tb3+ with a blue-visible photon at 485nm, two NIR photons could be emitted by Yb3+ through an efficient cooperative energy transfer from Tb3+ to two Yb3+ with optimal quantum efficiency as great as 196%. The development of 1D Tb3+–Yb3+ QC nanomaterials could open up a possibility to realize high efficiency silicon-based solar cells by means of downconversion of the green-to-ultraviolet part of the solar spectrum to ∼1000nm photons with a twofold increase in the photon number.

Composite Learning From Adaptive Dynamic Surface Control

In the conventional adaptive control, a stringent condition named persistent excitation (PE) must be satisfied to guarantee parameter convergence. This technical note focuses on adaptive dynamic surface control for a class of strict-feedback nonlinear systems with parametric uncertainties, where a novel technique coined composite learning is developed to guarantee parameter convergence without the PE condition. In the composite learning, online recorded data together with instantaneous data are applied to generate prediction errors, and both tracking errors and prediction errors are utilized to update parametric estimates. The proposed approach is also extended to an output-feedback case by using a nonlinear separation principle. The distinctive feature of the composite learning is that parameter convergence can be guaranteed by an interval-excitation condition which is much weaker than the PE condition such that the control performance can be improved from practical asymptotic stability to practical exponential stability. An illustrative example is used for verifying effectiveness of the proposed approach.

Enhanced Adaptive Fuzzy Control With Optimal Approximation Error Convergence

In this paper, an enhanced adaptive fuzzy control (AFC) strategy with guaranteed convergence of an optimal fuzzy approximation error (FAE) is presented for a class of uncertain nonlinear systems in the general Brunovsky form. Based on the fuzzy logic system (FLS) with variable universes of discourse, relaxed sufficient conditions that guarantee the optimal FAE being convergent are given, and the upper bound of the optimal FAE is obtained. The control singularity problem resulting from the unknown affine term is resolved by a novel fuzzy approximation equation, and the parameter adaptive law of the FLS is derived by the Lyapunov synthesis. By means of the optimal FAE bound result, it is proved that the closed-loop system achieves partially asymptotic stability under a certain selection of control parameters. The proposed approach retains all advantages of a previous similar approach under relaxed constraint conditions. Thus, it provides a more flexible solution to the AFC with optimal FAE convergence. Simulation studies have demonstrated high-precision tracking performance with smooth control input of the proposed approach.

Neural network based dynamic surface control of hypersonic flight dynamics using small-gain theorem

This paper analyzed the neural control for longitudinal dynamics of a generic hypersonic aircraft in presence of unknown dynamics and actuator fault. For the attitude subsystem, direct adaptive design is presented with the dynamic surface approach and the singularity problem is removed. For actuator fault, the unknown dynamics caused by fault is approximated by neural networks. The highlight is that the minimal-learning-parameter technique is applied on the dynamics and the simple adaptive algorithm is easy to implement since the online updating computation burden is greatly reduced. The uniformly ultimate boundedness stability is guaranteed via small-gain theorem. Simulation result shows that the controller could achieve good tracking performance with minimal learning parameter in case of actuator fault.

Human–Robot Interaction Control of Rehabilitation Robots With Series Elastic Actuators

Rehabilitation robots, by necessity, have direct physical interaction with humans. Physical interaction affects the controlled variables and may even cause system instability. Thus, human-robot interaction control design is critical in rehabilitation robotics research. This paper presents an interaction control strategy for a gait rehabilitation robot. The robot is driven by a novel compact series elastic actuator, which provides intrinsic compliance and backdrivablility for safe human-robot interaction. The control design is based on the actuator model with consideration of interaction dynamics. It consists mainly of human interaction compensation, friction compensation, and is enhanced with a disturbance observer. Such a control scheme enables the robot to achieve low output impedance when operating in human-in-charge mode and achieve accurate force tracking when operating in force control mode. Due to the direct physical interaction with humans, the controller design must also meet the stability requirement. A theoretical proof is provided to show the guaranteed stability of the closed-loop system under the proposed controller. The proposed design is verified with an ankle robot in walking experiments. The results can be readily extended to other rehabilitation and assistive robots driven with compliant actuators without much difficulty.

Hybrid feedback feedforward

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost.

Dynamic surface control via singular perturbation analysis

This note contributes to the technical nature of dynamic surface control (DSC) based on a class of strict-feedback nonlinear systems. The DSC technique prevents the complexity problem in integrator backstepping control (IBC). Yet, the stability results obtained by existing DSCs are conservative, and consequently, cannot fully reveal the technical nature of DSC. By the exploitation of the singular perturbation theory, several novel features of DSC are revealed as follows: (1) semiglobal practical exponential stability can be guaranteed only by a suitably small filter parameter; (2) steady-state tracking accuracy does not rely on high control gains; (3) the performance of IBC can be recovered via the decrease of the filter parameter. These features are not only significant for understanding the technical nature of DSC, but also useful for establishing an efficient strategy of parameter selection and system debugging. Simulation results have verified correctness of the theoretical result.