Xiangmin Tan

Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach

This paper addresses the robust trajectory tracking problem for a redundantly actuated omnidirectional mobile manipulator in the presence of uncertainties and disturbances. The development of control algorithms is based on sliding mode control (SMC) technique. First, a dynamic model is derived based on the practical omnidirectional mobile manipulator system. Then, a SMC scheme, based on the fixed large upper boundedness of the system dynamics (FLUBSMC), is designed to ensure trajectory tracking of the closed-loop system. However, the FLUBSMC scheme has inherent deficiency, which needs computing the upper boundedness of the system dynamics, and may cause high noise amplification and high control cost, particularly for the complex dynamics of the omnidirectional mobile manipulator system. Therefore, a robust neural network (NN)-based sliding mode controller (NNSMC), which uses an NN to identify the unstructured system dynamics directly, is further proposed to overcome the disadvantages of FLUBSMC and reduce the online computing burden of conventional NN adaptive controllers. Using learning ability of NN, NNSMC can coordinately control the omnidirectional mobile platform and the mounted manipulator with different dynamics effectively. The stability of the closed-loop system, the convergence of the NN weight-updating process, and the boundedness of the NN weight estimation errors are all strictly guaranteed. Then, in order to accelerate the NN learning efficiency, a partitioned NN structure is applied. Finally, simulation examples are given to demonstrate the proposed NNSMC approach can guarantee the whole systems convergence to the desired manifold with prescribed performance.

A Class of Adaptive Extended State Observers for Nonlinear Disturbed Systems

This paper proposes a novel class of adaptive extended state observers (AESOs) that significantly expand the applications of extended state observers (ESOs) to nonlinear disturbed systems. An AESO is designed as a linear time-varying form that, as a result, combines both the advantages of theoretical completeness in a conventional linear ESO (LESO) and good practical performance in a conventional nonlinear ESO (NESO). To tune the time-varying observer gains, AESO error dynamics is first transformed into a canonical (phase-variable) form. Then, time-varying PD-eigenvalues are assigned for the canonical system based on differential algebraic spectral theory. Theorems for stability and estimate error bounds of the AESO are given in the presence of unknown disturbances. These theorems also offer some important guidelines for assigning the PD-eigenvalues. To demonstrate the effectiveness of this new observer, two representative applications, including a numerical single-input-single-output example and a practical multiple-input-multiple-output hypersonic vehicle application, are exemplified, and comparison simulations are conducted among AESO, LESO, and NESO. Future work is pointed out in the end.

Robust adaptive neural network control for a class of uncertain nonlinear systems with actuator amplitude and rate saturations

An adaptive controller which is designed with a priori consideration of actuator saturation effects and guarantees H^~ tracking performance for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems with extern disturbances and actuator saturations is presented in this paper. Adaptive radial basis function (RBF) neural networks are used in this controller to approximate the unknown nonlinearities. An auxiliary system is constructed to compensate the effects of actuator saturations. Furthermore, in order to deal with approximation errors for unknown nonlinearities and extern disturbances, a supervisory control is designed, which guarantees that the closed loop system achieves a prescribed disturbance attenuation level so that H^~ tracking performance is achieved. Steady and transient tracking performance are analyzed and the tracking error is adjustable by explicit choice of design parameters. Computer simulations are presented to illustrate the efficiency of the proposed controller.

Adaptive trajectory tracking control system design for hypersonic vehicles with parametric uncertainty

A new nonlinear adaptive control scheme based on the immersion and invariance theory is presented to achieve robust velocity and altitude tracking for hypersonic vehicles with parametric uncertainty. The longitudinal dynamics of the hypersonic vehicle are first decomposed into velocity, altitude/flight-path angle, and angle of attack/pitch rate subsystems. Then a non-certainty-equivalent controller based on immersion and invariance, consisting of a control module and a parameter estimator, is designed for each subsystem with all the aerodynamic parameters unknown. The main feature of this method lies in the construction of the estimator, which is a sum of a partial estimate generated from the update law and an additional nonlinear term. The new term is capable of assigning appointed stable dynamics to the parameter estimate error. Stability analysis is presented using Lyapunov theory and shows asymptotical convergence of the tracking error to zero. Representative simulations are performed. Rapid and accurate command tracking is demonstrated in these numerical simulations, which illustrate the effectiveness and robustness of the proposed approach.

Immersion and Invariance-Based Output Feedback Control of Air-Breathing Hypersonic Vehicles

A new output feedback control design for robust velocity and altitude tracking of an air-breathing hypersonic vehicle (AHSV) is presented in this paper. The control scheme is performed on the assumption that only partial states of AHSV are measurable. The key idea is to employ the immersion and invariance approach to design globally asymptotically stable observers for the unmeasurable states. For controller design, the whole control architecture is constructed using dynamic surface control, based on the decomposition of the longitudinal dynamics of AHSV into velocity and altitude subsystems. Stability analysis is presented using the Lyapunov theory. Representative simulations are carried out on the high-fidelity model, which illustrate the effectiveness and robustness of the proposed scheme.

Direct adaptive type-2 fuzzy neural network control for a generic hypersonic flight vehicle

A direct adaptive interval type-2 fuzzy neural network (IT2-FNN) controller is designed for the first time in hypersonic flight control. The generic hypersonic flight vehicle is a multi-input multi-output system whose longitudinal model is high-order, highly nonlinear, tight coupling and most of all includes big uncertainties. Interval type-2 fuzzy sets with Gaussian membership functions are used in antecedent and consequent parts of fuzzy rules. The IT2-FNN directly outputs elevator deflection and throttle setting which make the GHFV track the altitude command signal and meanwhile maintain its velocity. The parameter adaptive law of IT2-FNN is derived using backpropagation method. The deviation of the control signal from the nominal dynamic inversion control signal is used as the reference output signal of IT2-FNN. The tracking errors of velocity and altitude are used as inputs of IT2-FNN. Tracking differentiator is designed to form an arranged transition process (ATP) of the command signal as well as ATP’s high-order derivatives. Nonlinear state observer is designed to get the approximations of velocity, altitude as well as their high-order derivatives. Simulation results validate the effectiveness and robustness of the proposed controller especially under big uncertainties.

Unified Model and Robust Neural-Network Control of Omnidirectional Mobile Manipulators

As a typical holonomic mechanical system, the omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. While the omnidirectional mobile manipulator provides many advantages, modeling and control of such a system are very challenging because of its complicated mechanism. In this paper, a unified dynamic model is developed by Lagrangian formalism. In terms of the proposed model, a tracking controller, based on computed torque control (CTC) method and radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.

Trajectory tracking control of omnidirecitonal wheeled mobile manipulators: Robust neural network based sliding mode approach

This paper focuses on developing a robust neural network (NN) based sliding mode controller (NNSMC) to solve the trajectory tracking problem of a redundantly-actuated omnidirectional mobile manipulator. The SMC is designed to be robust to disturbances assuring the stability of the system. The NN is used to identify the unstructured uncertainty of system dynamics. The stability of the closed-loop system, the convergence of the NN weight-updating process, and the boundedness of the NN weight estimation errors are all strictly guaranteed. Through theories analysis, we know the controller is also capable of disturbance-rejection in the presence of time varying disturbances. Finally, simulation results demonstrate the proposed NNSMC approach can guarantee the whole systems convergence to the desired manifold with prescribed performance.

Design of entry trajectory tracking law for a hypersonic vehicle via inversion control

A nominal altitude-velocity longitudinal entry trajectory is planned and tracked for a Generic Hypersonic Vehicle (GHV) in this paper. The entry corridor is presented which is defined by the dynamic pressure, normal acceleration, heating constraints, and the so-called Quasi-Equilibrium Glide Condition (QEGC). The flyability of the vehicle along the nominal trajectory is carefully analyzed for further validation of the selected nominal trajectory. The control scheme mainly consists of two loops: a guidance loop and an attitude loop, of which the latter is separated into the slow and fast loops with the time-scale separation theory. Inversion control is employed in these three loops, and an integration feedback approach is especially added into the inversion controller to eliminate the tracking error. Simulations demonstrate that the nominal trajectory is designed appropriately and tracked well.

Backstepping Based Type-2 Adaptive Fuzzy Control for a Generic Hypersonic Flight Vehicle

A backstepping controller is designed for the altitude subsystem of a generic hypersonic flight vehicle. The derivatives of the virtual signals in backstepping control design are obtained by command filters with magnitude, bandwidth and rate limit constraints. Dynamic inversion control is used in velocity subsystem design. General uncertainties are estimated online using interval type-2 adaptive fuzzy logic system. Simulation results demonstrate the effectiveness and robustness of the proposed controller and also validate type-2 fuzzy logic is more capable of handling uncertainties than type-1 fuzzy logic.