Adaptive Backstepping Control for Spacecraft Rendezvous on Elliptical Orbits Based on Transformed Variables Model

This paper presents a new robust adaptive current control strategy for induction motor drive systems. This control scheme is based on an adaptive pole placement control strategy (APPC) integrated to a sliding mode control scheme (SMC). The proposed APPC employs the internal model principle for achieving a zero steady state tracking error of machine stator currents. Moreover, this current controller can be implemented on the stator reference frame transformations. With this control scheme, fast transient response and robustness to parametric uncertainties and unmodeled disturbances are obtained. These disturbance can be owing to saturation, temperature changes or loading conditions. Simulation and experimental results are presented for showing the effectiveness of the proposed close-loop current control strategy.

In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme.

In this paper, robust adaptive control strategies are designed for Underwater Remotely Operated Vehicles (ROVs) with velocity constraints. First, robust control strategies are investigated for under-water ROVs, and then adaptive robust control strategies are further developed with online parameter estimation. To prevent the velocity constraint violation, the Barrier Lyapunov Function (BLF) is employed in Lyapunov synthesis. By ensuring the boundedness of the BLF, we also guarantee that the velocity constraints are not transgressed. The stability analysis of the closed-loop system is provided and all closed-loop signals are ensured to be bounded. Simulation results for 5 degree-of-freedom (DOF) underwater ROV demonstrate the effectiveness of the proposed approach.

In this paper, we investigate the trajectory tracking control problem of surface ships subject to unknown parameters, unknown time-varying disturbances, and input saturation. The input saturation is approximated using a Gaussian error function. The compounded uncertain term caused by the unknown parameters and disturbances is transformed into a linear parametric form with a single parameter called virtual parameter. Then, a novel robust adaptive ship trajectory tracking control law is designed using the adaptive vector-backstepping design method, where the adaptive law online provides the estimation of the virtual parameter. Further, a finite-time control scheme is developed injecting a power function vector of the position and velocity errors into the proposed control scheme such that the control performance is improved. Both our control schemes are simple to compute and easy to implement in engineering applications. Theoretical analysis indicates that the designed control laws can force the surface ship to track the desired trajectory while guaranteeing the global uniform ultimate boundedness of all the signals in the closed-loop trajectory tracking control system of surface ships. Simulation results and comparison verify the effectiveness of our novel robust adaptive trajectory tracking control schemes.

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

This paper proposes a robust adaptive fuzzy control scheme for the uncertain nonlinear time-delay systems preceded by an unknown dead-zone. It is well known that time-delay and dead-zone characteristics are frequently encountered in various engineering systems. The dead-zone phenomena may occur in various components of control systems including sensors, amplifiers and actuators, especially in pneumatic valves, in electric servo motors and in hydraulic components. On the other hand, the time-delay phenomena are quite commonly found in chemical process, hydraulic systems and pneumatic systems. In most practice motion systems, the dead-zone characteristics are usually poorly known and may vary with time. Therefore, by using a description of a dead-zone and exploring the properties of this dead-zone model intuitively and mathematically, a robust adaptive fuzzy control method is presented without constructing the dead-zone inverse. According to some adaptive laws, the unknown nonlinear functions of the plant are approximated by the fuzzy logic system. Based on Lyapunov stability theorem, the proposed robust adaptive fuzzy control scheme can guarantee the robust stability of the whole closed-loop nonlinear time-delay system with an unknown dead-zone in the actuator and obtain good tracking performance as well. Finally, an example and simulation results are provided to show the efficiency of the proposed control method.

In this article, a robust adaptive self-structuring fuzzy control (RASFC) scheme for the uncertain or ill-defined nonlinear, nonaffine systems is proposed. The RASFC scheme is composed of a robust adaptive controller and a self-structuring fuzzy controller. In the self-structuring fuzzy controller design, a novel self-structuring fuzzy system (SFS) is used to approximate the unknown plant nonlinearity, and the SFS can automatically grow and prune fuzzy rules to realise a compact fuzzy rule base. The robust adaptive controller is designed to achieve an L 2 tracking performance to stabilise the closed-loop system. This L 2 tracking performance can provide a clear expression of tracking error in terms of the sum of lumped uncertainty and external disturbance, which has not been shown in previous works. Finally, five examples are presented to show that the proposed RASFC scheme can achieve favourable tracking performance, yet heavy computational burden is relieved.

Revisiting the general periodic relative motion in elliptic reference orbits

This paper investigates the spacecraft rendezvous problem with the target spacecraft on an arbitrary elliptical orbit. The relative motion of two spacecrafts is established based on the Lawden equations. In order to take the model parameter uncertainty into consideration, an adaptive backstepping controller is designed by a Lyapunov approach. The proposed control laws can guarantee the asymptotic stability of the closed-loop system and can estimate the uncertain model parameters at the same time. Finally, simulation results are presented to illustrate the effectiveness of the proposed approach.

In this brief, an adaptive robust control (ARC) scheme with compensation for nonlinearly parameterized dynamic friction is proposed. Both parametric uncertainties and external disturbances are considered in this method. Our method takes advantage of a Lipschitzian property with respect to the parameters of nonlinearly parameterized model in the ARC design. The outcome is that the number of parameters to be updated in the ARC is equal to the number of unknown parameters in the plant, and thus the resulting control algorithm is convenient to be implemented. We have proved theoretically that the proposed method can not only guarantee desired transient performance for the system, but also make the magnitude of steady-state tracking error to be arbitrarily small in the presence of parametric uncertainties only. Experimental results are given to demonstrate the effectiveness of the proposed ARC scheme.

Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

In this paper, a novel adaptive robust control (ARC) scheme is proposed for electro-hydraulic servo systems (EHSSs) with uncertainties and disturbances. All dynamic functions in system dynamics are effectively approximated by multi-layer radial basis function neural network (RBF NN)-based approximators with online adaptive mechanisms. Moreover, neural network-based disturbance observers (NN-DOBs) are established to actively estimate and efficiently compensate for the effects of not only the matched/mismatched but also the imperfections of RBF NN-based approximators on the control system. Based on that, the nonlinear robust control law which integrates RBF NNs and NN-DOBs is synthesized via the sliding mode control (SMC) approach to guarantee the high-accuracy position tracking performance of the overall control system. Furthermore, the problem of the combination between DOBs and RBF NNs is first introduced in this paper to treat both disturbances and uncertainties in the EHSS. The stability of the recommended control mechanism is proven by using Lyapunov theory. Finally, numerical simulations with several distinct frequency levels of reference trajectory are conducted to convincingly demonstrate the effectiveness of the proposed approach.

It is shown that, for a variety of reasons, conventional adaptive control schemes cannot be generally applied to the control of space robots. Therefore, a robust adaptive control scheme that can be successfully applied to the control of space robots in various applications is presented. By using a special Lagrangian formalism, a globally valid dynamic model of space robots is derived and its dynamic properties which are useful for adaptive controller design are investigated. A unified framework is then presented to formulate the various applications, based on which the robust adaptive control scheme is derived. Since the framework is general, the proposed scheme can be actually applied to wider applications. To demonstrate the proposed scheme, the design process is applied to typical applications such as joint space control and Cartesian space control of space robots. >

A robust adaptive neural dynamic surface control (DSC) approach is presented for the longitudinal dynamics of a morphing aircraft in the presence of unknown dynamics and input constraints. For the altitude subsystem, neural systems are utilized to approximate the unknown nonlinear functions with smooth robust compensations to counteract the lumped approximation errors. By combining dynamic surface control and minimal learning parameter techniques, a robust adaptive neural control scheme is proposed and a simple adaptive algorithm is constructed. Meanwhile, an auxiliary system is incorporated into the control scheme to overcome the problem of input saturation. The highlight is that the proposed neural controller not only owns less updated neural parameters, but also has the ability of handling input constraints. It is proved that all the signals in the closed-loop system are bounded. Simulation results demonstrate the effectiveness of the proposed control scheme.

Hydraulic system is widely used in industry where large actuation forces are needed such as electro-hydraulic positioning system, active suspension control, and so on. One of the main problem in hydraulic system is the nontriviality, for instance, the deadbands in valve operation, frictions of links, saturations, pressure dynamics, etc. both system unknown nonlinearities and external disturbances should be considered in controller design. For underwater hydraulic manipulator, because of the environment of sea and the large nonlinearities of the hydraulic manipulator system, both system unknown nonlinearities and external disturbances should be considered in controller design. A lot of control methods have been proposed to solve the problem properly, such as sliding mode control, adaptive control, fuzzy adaptive control, adaptive robust control, ect. However, due to limited knowledge about hydraulic manipulator system, it is hard to precisely estimate the nonlinearities for a possible higher performance. In this paper, a neural network adaptive robust control method is proposed to help control an underwater hydraulic manipulator. In order to achieve better performance using adaptive robust control strategy, unknown nonlinearities in the system are approximated by the outputs of radial basis function neural network (RBFNNs). A projection law has been used to bound the NN weights when they are tuning on-line to avoid possible adaptive divergence, and the tracking transient performance is guaranteed by robust control law. Simulation results illustrate that manipulator has a good tracking performance.