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The design of output constrained control system for unmanned aerial vehicles deployed in confined areas is an important issue in practice and not taken into account in many autopilot systems. In this study, the authors address a neural networks-based adaptive trajectory tracking control algorithm for multi-rotors systems in the presence of various uncertainties in their dynamics. Given any sufficient smooth and bounded reference trajectory input, the proposed algorithm achieves that (i) the system output (Euclidean position) tracking error converges to a neighbourhood of zero and furthermore (ii) the system output remains uniformly in a prescribed set. Instead of element-wise estimation, a norm estimation approach of unknown weight vectors is incorporated into the control system design to relieve the onboard computation burden. The convergence property of the closed-loop system subject to output constraint is analysed via a symmetric barrier Lyapunov function augmented with several quadratic terms. Simulation results are demonstrated on a quadrotor model to validate the effectiveness of the proposed algorithm.

This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes.

In recent years, the agricultural applications of unmanned vehicles have garnered significant attention thanks to the rapid development of global positioning systems, inertial navigation technology, and control theory. In this study, a novel sliding mode controller for farm vehicles lateral path tracking control in the presence of unknown disturbances is created. Based on the standard kinematic model and the study of agricultural circumstances, the kinematic error model with unknown external disturbances and severe nonlinearity is initially constructed. To deal with the disturbances that exist in the lateral path tracking system, this work offers a finite-time disturbance observer-based composite terminal sliding mode control (FDOB-CTSMC). Meanwhile, the finite-time disturbance observer-based composite adaptive terminal sliding mode control (FDOB-CATSMC) is developed on the basis of the sliding mode filter and the adaptive control technology, which will significantly reduce the controller chattering issue. Using the Lyapunov theory, the finite-time convergence of the lateral deviation and the sliding variable can be verified. The numerical simulations demonstrate that the proposed controller is far better than the traditional path tracking controllers.

Purpose This paper aims to investigate the problem of adaptive bipartite tracking control in nonlinear networked multi-agent systems (MASs) under the influence of periodic disturbances. It considers both cooperative and competitive relationships among agents within the MASs. Design/methodology/approach In response to the inherent limitations of practical systems regarding transmission resources, this paper introduces a novel approach. It addresses both control signal transmission and triggering conditions, presenting a two-bit-triggered control method aimed at conserving system transmission resources. Additionally, a command filter is incorporated to address the problem of complexity explosion. Furthermore, to model the uncertain nonlinear dynamics affected by time-varying periodic disturbances, this paper combines Fourier series expansion and radial basis function neural networks. Finally, the effectiveness of the proposed methodology is demonstrated through simulation results. Findings Based on neural networks and command filter control method, an adaptive two-bit-triggered bipartite control strategy for nonlinear networked MASs is proposed. Originality/value The proposed control strategy effectively addresses the challenges of limited transmission resources, nonlinear dynamics and periodic disturbances in networked MASs. It guarantees the boundedness of all signals within the closed-loop system while also ensuring effective bipartite tracking performance.

Human-in-the-loop (HiTL) control is promising for the cooperative control problem of multi-agent systems (MASs) under the complicated environment. By considering the effect of human intelligence and decision making, the system robustness and security are notably enhanced. Hence, a distributed fixed-time tracking control problem is investigated in this paper for heterogeneous MASs based on the HiTL idea. First, a lemma of practically fixed-time stable is given where an explicit relationship of settling time and convergence domain is clearly shown. Then, under the framework of the adaptive backstepping approach, a series of modified intermediate control signals is designed to avoid the singularity problem by taking advantage of power transformation, fuzzy logic systems, and inequality schemes. Finally, the numerical example and comparison results are utilized to testify the effectiveness of the proposed method.

In this paper, a robust adaptive control scheme is proposed for the trajectory tracking control of underactuated surface vessels (USVs) subject to unknown dynamics, external disturbances and input saturation. First, a coordinate transformation is introduced to deal with the underactuation problem of the USV. A Gaussian error function and an adaptive neural network (NN) are adopted to approximate the saturation function and the unknown dynamics, respectively. Then, an adaptive robust integral of the sign of the error (RISE) feedback term is introduced in feedback control design to compensate the NN and saturation approximation residual errors and unknown external disturbances. On the basis of the above, a robust adaptive trajectory tracking control law is proposed incorporating a coordinate transformation, Gaussian error function and NN into RISE method. In addition, the adjustable-online adaptive feedback gain reduces the conservativeness of the control design. The theoretical analysis indicates that the designed robust adaptive control law can force USVs to track the desired trajectory while guaranteeing the asymptotic tracking performance. Simulation results verify the effectiveness of the novel robust adaptive trajectory tracking control scheme.

This paper deals with the problem of adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems. The considered issues include arbitrary switchings, unmodeled dynamics, input saturation, unknown dead-zone output, dynamic disturbances, and unmeasurable states, which makes the results more applicable. A Nussbaum-type function is exploited in the paper to overcome the difficulty existing in tracking the dead-zone output with unknown control direction. Furthermore, fuzzy logic systems are utilized to approximate the uncertain nonlinear system functions. Also, the state observer is constructed to approximate the unmeasurable states. Then, the adaptive fuzzy tracking controller with only three adaptive laws is presented on the basis of backstepping technique, common Lyapunov function, and small-gain approach. Under the designed controller, all the signals of the switched closed-loop systems are semi-globally, uniformly and ultimately bounded, and the tracking error is driven to a small area of the origin. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control scheme.

This work investigates the anti-saturation attitude tracking control for the tailless aircraft with guaranteed output constraints, in the presence of uncertain inertia parameters, bounded external disturbance, and actuator faults/failures. A predefined-time adaptive backstepping attitude control scheme has been proposed, the main features of this scheme lie in (a) designing a predefined-time filter to deal with the ‘explosion of complexity’ and singularity problem; (b) introducing a nonlinear state-dependent function to handle the asymmetric time-varying output constraints; (c) compensating for the impact of the actuator faults/failures and input saturation by a nonlinear function and bounded estimation simultaneously. Moreover, the proposed control scheme can ensure all signals in the closed-loop system converge to a residual set around the origin within a predefined time, and this time constant can be set freely by the designer, independently of initial conditions. Finally, numerical simulations have been conducted to verify the performance of the proposed predefined-time fault-tolerant control scheme.

This paper presents an adaptive learning method to achieve the output-feedback robust tracking control of systems with uncertain dynamics, which uses the techniques developed for optimal control. An augmented system is first constructed using the system state and desired output trajectory. Then, the robust tracking control problem is equivalent to the optimal tracking control problem with an appropriate cost function. To design the output-feedback optimal tracking control, an output tracking algebraic Riccati equation (OTARE) is then constructed, which can be used in the online learning process. To obtain the solution of the derived OTARE, an online adaptive learning method is proposed, where the input gain matrix is removed. In this learning algorithm, only the system output information is required and the observers widely used in the output-feedback optimal control design are removed. Simulations based on the power system are given to test the proposed method.

This paper studies a novel adaptive fixed-time sliding mode attitude tracking control for a submarine-launched missile, which is affected by sea winds, sea waves, ocean currents and other disturbances during the water-exit process. Firstly, the nonlinear water-exit dynamic model of the submarine-launched missile is established, and then it is transformed into a simple second-order attitude tracking system. Subsequently, a novel non-singular fixed-time fast terminal sliding mode surface (NFFTSMS) with fixed-time convergence is presented, and the pre-established settling time is also developed. Moreover, a novel adaptive non-singular fixed-time fast terminal sliding mode control (ANFFTSMC) is presented by employing a fixed-time disturbance observer, a fixed-time differentiator and the proposed NFFTSMS. Closed-loop stability of the proposed controller is proved by utilizing the Lyapunov methodology. Finally, numerical simulations including two typical launch trajectories of the missile are carried out to demonstrate the strong robustness of the proposed control scheme.