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In this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the help of neural network (NN) and tan-type barrier Lyapunov function (BLF). The NN is introduced to identify the unknown part in the dynamic model of the space manipulator. Benefiting from the neural identification, the proposed controller is model-free and insensitive to external perturbations. Moreover, the BLF is adopted to guarantee the position tracking errors never exceed the predefined output constraints. Different from log-type BLF, the tan-type BLF is employed for the control design, which makes the proposed controller universal for the cases with and without considering the output constraints. The semiglobal uniform ultimate boundedness of the resulting closed-loop system is strictly obtained through stability argument. All error variables in the closed-loop system can eventually stabilize to the small residual sets about zero under the proposed controller. Lastly, simulations and comparisons are given to demonstrate the effectiveness and excellent tracking performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are subject to complex aerodynamic or hydrodynamic forces and various uncertainties, making their dynamic modeling difficult. Hence, in these systems, it is preferred to apply control techniques whose closed-loop systems can predict the system's uncertain dynamics and provide acceptable performance. To this end, this paper exploits a new neural network-based optimal mixed H 2 / H ∞ control for a modified unmanned aerial vehicle to accomplish trajectory tracking missions. Firstly, the dynamic model of the modified unmanned aerial vehicles is presented. Then, the design procedure of the new controller is delineated. In this approach, H ∞ attenuates the effect of uncertainties and through H 2 the consumed control energy is minimized. Similar to real-world applications, it is supposed that there are control input constraints and external disturbances. The neural networks are also employed to estimate all uncertainties. The numerical simulations for two different desired paths are demonstrated. Simulation results illustrate the efficient performance of the proposed control technique in the presence of external disturbances, dynamic uncertainties, and control input constraints. [ABSTRACT FROM AUTHOR]

This article addresses trajectory tracking control of unmanned surface vessels (USVs) subject to position and velocity measurement biases. Unlike model uncertainties and external disturbances, measurement biases can lead to mismatched disturbances in system kinematics, rendering great difficulty to the USV control system design. To overcome this problem, a disturbance attenuation controller was recursively synthesized by incorporating two disturbance observers into the backstepping control design. The stability argument shows that all error signals in the closed-loop system can regulate to the small neighborhoods about the origin. The proposed controller has two remarkable features: (1) By adopting two disturbance observers to estimate the mismatched and matched lumped disturbances, the proposed controller is robust against model uncertainties and external disturbances and insensitive to measurement biases. (2) Meanwhile, the proposed controller is structurally simple and user friendly. Lastly, comparative simulations were conducted to validate the obtained results. [ABSTRACT FROM AUTHOR]

This article addresses the challenging problem of fixed-time output-constrained synchronization for master–slave chaotic financial systems with unknown parameters and perturbations. A fixed-time neural adaptive control approach is originally proposed with the aid of the barrier Lyapunov function (BLF) and neural network (NN) identification. The BLF is introduced to preserve the synchronization errors always within the predefined output constraints. The NN is adopted to identify the compound unknown item in the synchronization error system. Unlike the conventional NN identification, the concept of indirect NN identification is employed, and only a single adaptive learning parameter is required to be adjusted online. According to the stability argument, the proposed controller can ensure that all error variables in the closed-loop system regulate to the minor residual sets around zero in fixed time. Finally, simulations and comparisons are conducted to verify the efficiency and benefits of the proposed control strategy. It can be concluded from the simulation results that the proposed fixed-time neural adaptive controller is capable of achieving better synchronization performance than the compared linear feedback controller. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stabilization and tracking control of a hyperchaotic memristor oscillator using a novel disturbance-observer-based integral terminal sliding mode control. Firstly, the dynamic behaviours of the hyperchaotic memristor oscillator are investigated. Afterward, the fast disturbance-observer is combined with integral terminal sliding mode control for multi-input-multi-output (MIMO) nonlinear relative degree one systems in presences of external disturbance, unknown input saturation, and control singularity. The Lyapunov stability theorem is implemented to prove the finite time convergence of the closed-loop system. Furthermore, to specify the parameters of the proposed controller, the genetic algorithm optimization is applied to decrease chattering of the system's response. Simulation results are presented to show the appropriate performance of the proposed control scheme in terms of stabilization and tracking control in the presence of external disturbances and input saturation. [ABSTRACT FROM AUTHOR]

This brief presents a finite‐time output feedback control scheme for the velocity‐free attitude stabilization of rigid spacecraft under inertia uncertainties and disturbances. Note that either the finite‐time velocity observer or the finite‐time extended state observer contains the control torques, which makes the transient performance of the observer‐based output feedback control difficult to be regulated. Alternatively, the filter‐based output feedback control can overcome such problem naturally since the finite‐time velocity filter does not involve the control torques in its structure. Different from the existing finite‐time velocity filters, a finite‐time extended state filter is introduced in this work to estimate the pseudo angular velocity and total uncertain item simultaneously. Then, the developed controller is synthesized based on the recovered information, which does not require the angular velocity for feedback and has the excellent uncertainty and disturbance compensation capability. The global finite‐time stability of the resultant closed‐loop system is evaluated through the Lyapunov direct methodology and homogeneous system theory. Lastly, comparative simulations are carried out to examine the efficiency and superiority of the presented control scheme. [ABSTRACT FROM AUTHOR]

In this paper, we present an adaptive control strategy for the output-constrained attitude tracking of rigid spacecraft under unknown inertia and disturbances. The designed controller is recursively constructed under the framework of the backstepping method, which involves an auxiliary stabilizing law and a practical control law. In the auxiliary stabilizing law design, the log-type barrier Lyapunov function (BLF) is embedded to tackle the time-varying output constraints. In addition, in the practical control law design, the element-wise parametric adaptive laws are adopted to identify the unknown inertia and disturbances by introducing a linear mapping operator. The stability evaluation indicates that the overall closed-loop system is semi-globally uniformly ultimately bounded (SGUUB) and all error variables can eventually regulate to the minor fields about zero. Meanwhile, the spacecraft attitude tracking errors can be preserved in the preassigned output constraints. Lastly, the efficiency and strong robustness of the presented control strategy are demonstrated through competitive simulations. [ABSTRACT FROM AUTHOR]

This article aims to propose a novel neural adaptive control approach for the fixed-time attitude tracking of space vehicle subject to constrained outputs. The proposed controller is recursively synthesized by integrating with the barrier Lyapunov function (BLF) and neural approximation under the fixed-time backstepping control framework. The whole control design procedure involves two steps. In the first step, the virtual control signal is designed by adopting the BLF to guarantee the attitude tracking errors always within the predefined output constraints. In the second step, the actual control signal is designed by augmenting the neural network (NN) to approximate the uncertain term caused by unknown dynamics and disturbances. Theoretical analysis shows that the proposed controller can ensure all error signals in the closed-loop system stabilize to the small regions about the origin in fixed time even under unknown dynamics and disturbances. The effectiveness and excellent performance of the proposed control approach are validated by simulations and comparisons. [ABSTRACT FROM AUTHOR]

Although the literature presents promising techniques for the control of integer-order systems, control and synchronizing fractional systems still need further improvement to ensure their robustness and convergence time. This study aims to address this issue by proposing a model-free and finite-time super-twisting control technique for a variable-order fractional Hopfield-like neural network. The proposed controller is enhanced with an intelligent observer to account for disturbances and uncertainties in the chaotic model of the Hopfield-like neural network. The controller is able to regulate the system even when its complex variable-order fractional dynamic is completely unknown. Moreover, the proposed technique guarantees finite-time convergence of the closed-loop system. First, the dynamics of the variable-order fractional Hopfield-like neural network are examined. Then, the control design is described and its finite-time stability is proven. The controller is then applied to the variable-order fractional system and tested under two different scenarios to evaluate its performance. The results of the simulations demonstrate the excellent performance of the proposed method in both scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law are designed by adopting the neural networks (NNs) to approximate the upper bound of the lumped unknowns. Benefiting from the indirect neural approximation, the proposed controller does not need any model information for feedback. In addition, only two adaptive parameters are required for the indirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Lyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, the effectiveness and superior performance of the proposed control scheme are confirmed by a simulated example. [ABSTRACT FROM AUTHOR]

In this paper, by considering the Caputo-like delta difference definition, a fractional difference order map with chaotic dynamics and with no equilibria is proposed. The complex dynamical behaviors associated with fractional difference order maps are analyzed employing the phase portraits, bifurcations diagrams, and Lyapunov exponents. The complexity of the sequence generated by the chaotic difference map is studied using the permutation entropy approach. Afterwards, projective synchronization of the systems is investigated. Fuzzy logic engines as intelligent schemes are strong tools for control of various systems. However, studies that apply fuzzy logic engines for control of fractional-order discrete-time systems are rare. Hence, in the current study, by taking advantages of fuzzy systems, a new controller is proposed for the fractional-order discrete-time map. The fuzzy logic engine is implemented in order to enhance the performance and agility of the proposed control technique. The stability of the closed-loop systems and asymptotic convergence of the projective synchronization error based on the proposed control scheme are proven. Finally, numerical simulations which clearly confirm that the offered control technique is able to push the states of the fractional-order discrete-time system to the desired value in a short period of time are presented. [ABSTRACT FROM AUTHOR]