Adaptive quantized tracking control for switched nonlinear systems: A improved predefined-time filter backstepping method

This article deals with the optimal tracking control problem for spacecraft formation flying via a sliding mode approach in the presence of external disturbances and signal quantization, where both state quantization and input quantization are considered. First, the Gauss pseudospectral method is adopted to solve the multi-objective optimization problem, where performance optimization, thruster amplitude constraints, and collision avoidance are simultaneously taken into consideration. Second, a novel quantized sliding mode control strategy is developed by employing a dynamic logarithmic quantizer to track the obtained optimal trajectories of relative position and velocity. In this design, the quantizer parameters are input into the designed controller to compensate for the signal quantization effects. Under the proposed robust quantized sliding mode control strategy, the resulting closed-loop control system is asymptotically stable with satisfying performance multi-objective constraints. Finally, a simulation example is presented to show the effectiveness of the proposed control design scheme.

Observer-based adaptive fuzzy quantized control of uncertain nonlinear systems with unknown control directions

6-DOF fixed-time adaptive tracking control for spacecraft formation flying with input quantization

Consensus tracking and vibration control for multiple single-link flexible manipulators with input signal quantization

This paper investigates the cooperative formation trajectory tracking problem for heterogeneous unmanned aerial vehicle (UAV) and multiple unmanned surface vessel (USV) systems with input quantization performance. Firstly, at the kinematic level, a distributed guidance law based on an extended state observer (ESO) is designed to compensate for the unknown speed of neighbor agents for expected trajectory tracking, and subsequently at the dynamic level, an ESO is utilized to estimate model uncertainties and environmental disturbances. Following that, a linear analytic model is employed to depict the input quantization process, and the corresponding adaptive quantization controller is designed without necessitating prior information on quantization parameters. Based on the input-to-state stability, the stability of the proposed control structure is proved, and all the signals in the closed-loop system are ultimately bounded. Finally, a simulation study is provided to show the efficacy of the proposed strategy.

This thesis suggests a control strategy based on rotation matrix for autonomous underwater vehicles (AUVs) with full actuation and input quantization regarding model uncertainties and outer interferences. First, without unwinding, the dynamics of the attitude regarding AUV will be solely and globally depicted via rotation matrix. To simplify the calculation and execution, an alternative error vector is presented. Next, a terminal sliding mode surface which includes two components is designed to guarantee the convergence in a finite time way and singularity-free character. The input quantization is considered in the controller design of AUVs, thus it reduces the relevant loads of the derived system. Then, a new adaptive law is set to ensure the information which is estimated and could appear in the form of limited values for all time. Lastly, the effectiveness of control plan is proved through abstract analysis and simulations.KeywordsAUVsRotation matrixInput quantizationTrajectory tracking control

Event-triggered adaptive fuzzy trajectory tracking control of unmanned surface vehicle with time-varying output constraints and input quantization.

In this paper, a composite finite-time tracking controller is designed for second-order systems with power integrators and input quantization, where the tracking reference signal is generated by a power-integrator exogenous system. A fixed-time disturbance observer is presented to estimate the unknown disturbances. And then a novel non-singular terminal sliding mode (NTSM) surface is constructed. Based on sliding mode surface (SMS) and disturbance estimation, two composite finite-time tracking controllers are developed for two different cases. The controllers make the system trajectories track the desired signal in finite-time. Simulation results are employed to demonstrate the effectiveness of the proposed schemes.

This paper addresses the finite-time tracking control problem for a class of quadrotor unmanned aerial vehicle (QUAV) subject to unknown mixed faults and external disturbances. The considered mixed faults include both input quantization and actuator faults. First, radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear dynamics of the QUAV system, with adaptive control laws designed for online weights updates. Second, since the neural network approximation errors and external disturbances can be treated as unknown but bounded constants, adaptive control laws are developed to estimate these parameters. Third, to address the design complexity caused by unknown control coefficients arising from mixed faults, a Nussbaum gain function is introduced. Subsequently, based on the designed global fast terminal sliding mode (GFTSM) functions, adaptive GFTSM neural network control strategies are proposed for position and attitude tracking control. Theoretical analysis confirms that these control strategies guarantee the QUAV system’s position and attitude outputs converge to reference trajectories, with tracking errors reaching a very small neighborhood of zero within a finite time. Finally, the effectiveness of proposed control strategies is validated through an actual system.

SummaryThis paper studies the problem of adaptive fuzzy asymptotic tracking control for multiple input multiple output nonlinear systems in nonstrict‐feedback form. Full state constraints, input quantization, and unknown control direction are simultaneously considered in the systems. By using the fuzzy logic systems, the unknown nonlinear functions are identified. A modified partition of variables is introduced to handle the difficulty caused by nonstrict‐feedback structure. In each step of the backstepping design, the symmetric barrier Lyapunov functions are designed to avoid the breach of the state constraints, and the issues of overparametrization and unknown control direction are settled via introducing two compensation functions and the property of Nussbaum function, respectively. Furthermore, an adaptive fuzzy asymptotic tracking control strategy is raised. Based on Lyapunov stability analysis, the developed control strategy can effectually ensure that all the system variables are bounded, and the tracking errors asymptotically converge to zero. Eventually, simulation results are supplied to verify the feasibility of the proposed scheme.

In this article, a practical fixed-time adaptive neural network (NN) trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to uncertain dynamics, unknown time-varying disturbances, an unknown dead zone, actuator faults and input quantization is developed for the first time. Here, a hysteresis quantizer is introduced to decrease the oscillation in the signal quantization process. Then, the radial basis function NN is employed to compensate the uncertainty term in the AUVs trajectory tracking control system. By incorporating the bounded estimate, smoothing functions and parameter adaptive technique, the problem of unknown dead zone, actuator fault and input quantization are addressed. The restrictive conditions of boundedness for the disturbance-like item in conventional sector bounded quantizer is resolved. Subsequently, a practical fixed-time adaptive NN trajectory tracking control law is designed does not require any parameter information of the quantizer under the backstepping design framework. The theoretical analysis further confirms that all signals in the AUV trajectory tracking closed-loop control system remain bounded, and the developed control scheme is shown to be effective through simulation results.

This study investigates the neural adaptive integral sliding mode control for attitude tracking of flexible spacecraft, where unknown actuator nonlinearity, input quantisation and external disturbances are considered simultaneously. In this design, the hysteresis encoder–decoder scheme is employed between the controller and actuator side for signal quantisation. A quantised adaptive integral sliding mode control strategy is developed, where the neural network scheme is applied to approximate the unmeasurable rigid-flexible coupled nonlinear dynamics. The proposed control strategy can compensate for quantisation error, actuator faults, actuator dead-zone as well as external disturbances effectively, and guarantee the trajectory of the attitude tracking error converge to the equilibrium point along the designed sliding surface. Finally, a simulation example is conducted to demonstrate the validness of the developed quantised control strategy for flexible spacecraft attitude tracking problem.

This paper researches the adaptive fixed-time global prescribed performance tracking control (GPPTC) problem for a robotic manipulator (RM) under unknown backlash-like hysteresis and disturbance. In the control design of the RM, a novel global fixed-time prescribed performance function (FTPPF) is designed to achieve output tracking with arbitrarily prescribed transient performance. Moreover, fuzzy logic systems (FLSs) are used to handle the unknown robotic dynamics, and a hyperbolic tangent activation function is applied to deal with the hysteresis nonlinearity. The proposed prescribed performance control (PPC) method based on the FTPPF ensures that the tracking error converges to a predetermined range in a given time and remains within that range thereafter. Unlike the existing PPC methods, our control algorithm removes the limitation requiring the initial tracking error to satisfy the initial value of the PPF. Based on the fixed-time command-filtered (FTCF) technique, an adaptive fixed-time controller is designed, which enables the joint position to track the desired signal in a fixed time. Finally, the effectiveness of the proposed controller is demonstrated via the simulation.

ABSTRACTIn addressing the adaptive neural backstepping control for multiple-input and multiple-output nonlinear systems in pure-feedback form with time-delay and input quantisation, we construct a high-gain state observer and an output-feedback adaptive control scheme using backstepping method, with neural networks to estimate the uncertain nonlinear functions. Then, we propose an output feedback neural controller that ensures all the state trajectories in the time-delay quantised nonlinear systems are ultimately bounded, with the control signal being quantised by either a hysteretic quantiser or a logarithmic quantiser. An illustrative example is presented to show the applicability of the new control method developed.

The paper researches smooth switching controller for multi linear time-varying (LTV) systems with input quantization and input saturation, which is a common problem for modern flight vehicles. First, this paper presents the problem statement, in which a tracking problem is converted to a regulation problem by constructing deviation system, the tracking error is treated as the system performance and the conditions for smooth switching tracking control are discussed based on the theory of robust control technique. Secondly, controller of the deviation system with input saturation and input quantization is designed by solving Linear Matrix Inequalities (LMIs) based convex optimization problem with the conditions for smooth switching tracking control being considered. Finally, simulations are carried out for the longitudinal dynamics model of hypersonic reentry vehicle (HRV) driven by Reaction Control System (RCS) to illustrate the effectiveness and rationality of the method.