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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.

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.

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.

This paper addresses the finite‐time trajectory tracking control problem for quadrotor UAVs under model uncertainties, external disturbances, and input saturation. A robust finite‐time trajectory tracking control scheme is proposed by following steps. First, a nominal controller is established based on integral terminal sliding mode control. Second, an auxiliary system is used to address the input saturation constraint problem. It effectively restricts inputs from exceeding the bounds. Third, a reinforcement learning component is designed to estimate and compensate for model uncertainties and external disturbances. Then, a robust finite‐time scheme is constructed by integrating the nominal controller, the reinforcement learning compensating component, and the auxiliary system. Theoretical analysis verifies that the finite‐time stability of controlled systems can be guaranteed by the proposed tracking control scheme, and the tracking error can be driven to a compact set in finite time. Furthermore, simulation results confirm the effectiveness of the proposed control scheme. This paper addresses the finite‐time trajectory tracking control problem for quadrotor UAVs under model uncertainties, external disturbances, and input saturation. A robust finite‐time scheme is constructed by integrating the nominal controller, the RL compensating component, and the auxiliary system.

This paper proposes a robust model predictive control (MPC) strategy for the trajectory tracking control of a four-mecanum-wheeled omni-directional mobile robot (FM-OMR) under various constraints. The method proposed in this paper can solve various constraints while implementing trajectory tracking of the FM-OMR. Firstly, a kinematics model with constraint relationship of the FM-OMR is established. On the basis of the kinematics model, the kinematics trajectory tracking error model of the FM-OMR is further formulated. Then, it is transformed into a constrained quadratic programming(QP) problem by the method of MPC. In addition, aiming at the speed deficiencies of conventional neural networks in QP solving, a delayed neural network (DNN) is applied to solve the optimal solution of the QP problem, and compared with the Lagrange programming neural network (LPNN) to show the rapidity of the DNN. Finally, two simulation cases considering bounded random disturbance are provided to verify the robustness and effectiveness of the proposed method. Theoretical analysis and simulation results show that the control strategy is effective and feasible.

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.

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.

This paper proposes a neural approximation based model predictive control approach for tracking control of a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertainty and unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertain environment, effective approaches for reliable tracking control should be investigated with the consideration of the disturbances, including internal-robot friction and external physical interactions in the robot’s dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller (NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybrid control strategy. The results indicate that the proposed methodology can achieve promising tracking performance in terms of accuracy and stability.

This paper addresses a pattern-moving-based modified model free adaptive control (PMFAC) scheme and illustrates the convergence of its tracking error for a class of nonlinear systems with unknown model and multi-threshold quantized observations. The basic idea is to consider the system’s quantization error as an external disturbance and an improved performance index function of control law is proposed from the perspective of two-player zero-sum game (TP-ZSG) based on the existed MFAC algorithms. The PMFAC scheme is established which mainly includes an improved tracking control law and a pseudo-partial derivative (PPD) estimation algorithm. Under certain conditions, the bounded convergence of system tracking error and the stability of PMFAC system with quantization errors can be guaranteed. The theoretical results are demonstrated by two numerical examples.

This paper investigates the trajectory tracking control problem of a four-wheel independently driven skid-steering mobile robot (FWID-SSMR) while considering friction resistance, parameter variation and external disturbances. Unlike previous studies that only achieved stable tracking control of FWID-SSMR, this paper accomplishes prescribed steady-state and transient performance. Based on the dynamic model of FWID-SSMR, an integer-order prescribed-time controller (IOPTC) is developed first, which can make the tracking errors converge to a predetermined residual set with a preset convergence rate in a prescribed time. Motivated by it, a fractional-order prescribed-time controller (FOPTC) is developed by exploiting the genetic attenuation properties of fractional calculus (FC) for improving the control performance. The feasibility and effectiveness of the developed controller are verified by Lyapunov theoretical analysis and numerical simulation studies. The simulation results show that both the IOPTC and FOPTC outperform the feedback controller (FBC). Moreover, the influence of the performance function on control performance is also tested, which can serve as a reference for selecting the appropriate performance function to use in future applications.