Search

Based on a line-of-sight (LOS) guidance law for a curve parametrized path, a finite-time backstepping control is proposed for the kinematic path-following of an underactuated autonomous surface vehicle (ASV). Finite-time observer is utilized to estimate the unknown external disturbances accurately. The first-order Levant differentiator is introduced into the finite-time filter technique, such that the output of filter can not only approximate the derivative of the virtual control, but also avoid the singularity problem of real heading control. The integral terminal sliding mode is employed to improve the tracking performance and converging rate in the surging velocity control. By virtue of Lyapunov function, all the signals in the closed-loop system can be guaranteed uniformly ultimate boundedness, and accurate path-following task can be fulfilled in finite time. The simulation results and comparative analysis validate the effectiveness and robustness of the proposed control approach. [ABSTRACT FROM AUTHOR]

This paper aims to improve the tracking control performance of the three-stage valve (TSV) controlled electro-hydraulic servo system (EHSS) with parameter uncertainties and other lumped unknown nonlinearities, including unknown dynamics and disturbances. A more accurate nonlinear model of the TSV-controlled EHSS is established and a neural network-based finite-time command-filtered adaptive backstepping control (NNFCABC) method is proposed for the EHSS. Adaptive control is used to deal with the system parameter uncertainties, and the radial basis function neural network (RBFNN) algorithm is introduced to approximate the lumped unknown nonlinearities. The prediction errors of serial-parallel estimation models (SPEMs) and the tracking errors are utilized together to design adaptive laws to estimate the system parameters and the weights of the RBFNNs. The entire control framework utilizes command-filtered control and backstepping techniques. By applying Levant differentiators as command filters and introducing fractional power terms into the virtual control laws and the SPEMs, the proposed NNFCABC theoretically guarantees the tracking performance of the closed-loop control system with finite-time convergence. Comparative simulations and experiments verify the feasibility and superiority of the proposed control scheme. • A more accurate model of the three-stage valve is built. • Neural network and adaptive control are combined under command-filtered technology. • The finite-time convergence of all signals in the system is achieved. • Simulations and experiments have verified the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper examines a finite-time command-filtered backstepping control proposed for a class of strict-feedback nonlinear systems with input delay and time-varying asymmetric full-state constraints. The existing control method either ignores the effect of input delay or converges asymptotically in infinite time. A command-filtered backstepping design is used to decrease computational burden. A first-order Levant differentiator is employed to replace the command filter for estimating the virtual control signals in finite time. The time-varying asymmetric barrier Lyapunov function (TVABLF) and command-filtered backstepping design are applied to alleviate the significant challenges caused by the backstepping approach and full-state constraints. A novel finite-time delay compensation mechanism is proposed to remove the impact of input delay. The closed-loop signals are proved to be practical bounded. Simulation results are provided to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Summary: This paper investigates the command filter‐based adaptive neural network tracking control problem for uncertain nonsmooth nonlinear systems. First, an integral barrier Lyapunov function is introduced to deal with the symmetric output constraint and make the output comply with prescribed restrictions. Second, by the Filippov's differential inclusion theory and approximation theorem, the considered nonsmooth nonlinear system is converted to an equivalent smooth nonlinear system. Third, the Levant's differentiator is used to deal with the "explosion of complexity" problem. An error compensation mechanism is established to attenuate the effect of the filtering error on control performance. Then, an adaptive neural network controller is set up by resorting to the backstepping technique. It is strictly mathematically proved that the tracking error can converge to an arbitrarily small neighborhood of the origin and all the signals in the closed‐loop system are semi‐globally uniformly ultimately bounded. Finally, a numerical example and an application example of the robotic manipulator system are provided to demonstrate the availability of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates a class of output-mask-based adaptive neural network (NN) tracking control for nonlinear stochastic time-delayed multi-agent systems (STMASs) based on a unified event-triggered approach. The output signal relies on an output mapping acted as a mask, defined as a privacy-protection-like method, so that the internal state of one agent cannot be identified by other distrustful eavesdroppers or attackers. Moreover, the construction of a unified event-triggered control scheme retains the advantages of the saturation threshold triggering strategy, incorporates distributed errors, and increases the flexibility of thresholds. Furthermore, for stochastic time-delay multi-agent systems, the initial value limitation of the conventional first-order filter is removed by a first-order Levant differentiator, and a new estimation term in the fuzzy observer is established to solve the nonlinear fault. The unknown function in pure-feedback form is addressed via combining Butterworth low-pass filter and radial basis function neural networks (RBF NNs). Finally, the boundedness of all signals in the closed-loop systems is demonstrated, and the effectiveness of the proposed algorithm is verified by some simulation results. • This paper proposes a privacy-protection-like scheme for continuous systems to make those signals transmitted in ciphertext. • A unified approach for event-triggered control, namely dynamic saturation threshold event trigger, was established. • The proposed method relaxes two conditions and reduces the conservatism. [ABSTRACT FROM AUTHOR]

In the fully mechanized mining face, proper chain tension of chain transmission system is of great significance in reducing the failure rate and improving the service life of scraper conveyor. However, the disturbance caused by random load and variable stiffness makes it very difficult to control chain tension. An adaptive tension control scheme combining a neural command filtering backstepping algorithm and online identification is proposed in this article based on a novel tension model. First of all, the proposed tension model is represented by a linear regression part and a nonlinear mapping part, which effectively reflects the chain transmission characteristics and avoids the full measurement of each chain ring. Then, Levant's differentiator and state observer are used to expand input variables for identification algorithm and neural networks estimation. To avoid the adverse effect of random load on parameter adaption, stiffness online identification algorithm is employed in improving the tension tracking performance. To compensate for nonlinear mapping part and uncertain nonlinearity caused by random load, neural adaptive estimator is effectively integrated with the robust integral term in backstepping design. Moreover, Lyapunov stability of the proposed controller is guaranteed. Finally, comparative experimental results of six controllers are completed to verify the effectiveness of the proposed controller in the static and random load mode. [ABSTRACT FROM AUTHOR]

This paper studies a time-varying formation control problem of a swarm of autonomous surface vehicles (ASVs) with prescribed constraints. Every ASV suffers from unavailable velocities, saturated inputs, internal uncertainties, and external disturbances. A distributed finite-time performance-prescribed time-varying formation control method is proposed for ASVs. Specifically, an adaptive fuzzy state observer (AFSO) is firstly proposed to recover the unavailable velocity by using saturated inputs and estimated disturbances from the fuzzy logic system. Secondly, a tunnel prescribed performance (TPP) function is designed to limit tracking errors with a concise form and smaller overshoot. At the kinematic level, with TPP-based error transformation, a C 1 finite-time time-varying guidance law is proposed by using the smooth switch functions and recovered velocities. Next, a finite-time command filter based on Levant differentiator is presented to smooth the guidance signals. At the kinetic level, a C 1 finite-time saturated control law is developed with the estimated velocities and disturbances. Then, it proves that tracking errors of the proposed closed-loop system converge into a small neighborhood around the equilibrium within finite time while evolving under TPP constraints regardless of saturated inputs. Finally, comparison results are provided to demonstrate the effectiveness of the proposed distributed finite-time performance-prescribed time-varying formation control method. • Distributed finite-time performance-prescribed formation control method is proposed for ASVs. • The TPP with a concise form is developed to achieve smaller overshoot in a positive manner. • An adaptive fuzzy state observer is designed to recover unavailable velocities. [ABSTRACT FROM AUTHOR]