This paper investigates the safety-certified cooperative path following problem for second-order nonlinear systems with multiple-input multiple-output strict-feedback form subject to safety, state, and input constraints. A safety-certified learning control approach is proposed to achieve collision-free cooperative path following based on command optimization, online learning, and self-triggered communication. Specifically, an extended-state-observer-aided learning neural predictor is developed to simultaneously identify nonlinear functions and unknown input gains without measuring state derivatives. Control barrier functions are then employed to ensure safety through forward invariant sets. Next, command optimization is utilized to generate the optimal virtual control signals that satisfy safety constraints, state constraints, and input constraints. A neurodynamic optimization technique is employed to solve the quadratic optimization problem in real time. Additionally, a self-triggered mechanism is introduced in path variable coordination to reduce the listening and triggering times. By using the proposed safety-certified cooperative path following approach, a safe formation is guaranteed for input-to-state safety. Simulation results are provided to illustrate the effectiveness of the proposed method. Note to Practitioners-This paper addresses the safety-certified cooperative path following problem of multi-agent systems, which has practical implications in various applications. These applications include formation patrol, cargo transportation, search and rescue missions, swarm robotics, and agricultural tasks. By coordinating the movements of multiple agents, cooperative path following enhances efficiency in these scenarios. The challenges posed by safety, state, and input constraints are commonly encountered in practical. To address these challenges, the command optimization approach is proposed in this paper. The optimization problem is efficiently solved in real-time using neurodynamic optimization technique. Furthermore, to enhance feasibility in a limited communication environment, a self-triggered mechanism is introduced to reduce the communication burden. Therefore, the aforementioned effective scheme is suitable for implementation in industrial applications.

This article addresses the synchronized path maneuvering problem of uncertain nonlinear systems in multiple-input multiple-output (MIMO) strict-feedback form subject to state and input constraints, unknown input gains, and limited communication resources. An aperiodic synchronized path maneuvering control architecture is proposed based on data-driven neural estimation, command optimization, and self-triggered path coordination. Specifically, a data-driven neural predictor is designed for identifying unknown input gains, in addition to unknown nonlinear functions. In the path maneuvering control law design, an optimization-based command governor is used to obtain the optimal virtual and actual control laws within the state and input constraints. In the path variable coordination part, a self-triggered path update law is developed to reduce the transmission load. Based on cascade stability theory, the closed-loop system is proven to be input-to-state stable, and all signals are uniformly ultimately bounded. An application to formation control of multiple vehicles is provided to substantiate the efficacy of the proposed command optimization method.

In this paper, an anti-disturbance output feedback dynamic surface control (DSC) method is proposed for the position tracking of interior permanent magnet synchronous motor subject to unknown nonlinearities and time-varying disturbances. Specifically, a nonlinear extended-state-observer (NLESO) based on exponential functions is designed to estimate the total disturbance composed of system uncertainties and external disturbances. Then, by compensating for the total disturbance via the NLESO, an anti-disturbance output feedback law is designed based on the DSC approach. The salient features of the proposed approach is twofold. First, the total uncertainty including internal and external disturbances can be accurately estimated by an NLESO in real time. Second, the desired anti-disturbance performance of the servo control system can be achieved regardless of the position measurement only. The stability of the closed-loop control system is proved by using the cascade theory and input-to-state stability theory. Both simulation and experiment results are conducted to illustrate the effectiveness of the proposed control method.

This paper is concerned with the adaptive position tracking of permanent magnet synchronous motors (PMSMs) subject to model uncertainties and unknown loads. A modular neural dynamic surface control (MNDSC) method is used to devise the position tracking controller. Specifically, a predictor module based on neural networks (NNs) is designed, which is able to fast identify the unknown nonlinearities and uncertainties of PMSMs without introducing high-frequency oscillations in the learning process. Next, a position tracking controller module is designed based on a modified dynamic surface control where a second-order nonlinear tracking differentiator (NLTD) instead of a first-order filter is used to extract the time derivatives of virtual control law. The salient features of the proposed position tracking controller for PMSMs are as follows. First, the transient performance can be improved compared with the previous control method. Second, the unknown nonlinearities of the PMSM can be approximated by the NNs. Third, the predictor module and the controller module are decoupled by using the modular design. The stability of the position tracking system cascaded by the predictor module and the controller module is proved by cascade theory and input-to-state stability theory. Finally, the performance of the proposed MNDSC strategy for PMSMs is verified through simulations. (C) 2019 Elsevier B.V. All rights reserved.

This paper is concerned with maximum power tracking of variable-speed wind energy conversion systems under low wind speed. First, by introducing a prescribed performance function and an error transformation technique, a maximum power tracking method with prescribed performance is proposed, such that the steady state and transient performance of the system can be analyzed quantitatively. Meanwhile, an extended state observer is utilized for real-time estimation of system uncertainties including aerodynamic torque and disturbances. Then, the cascade system theory and input-to-state stability are used to analyze the stability of the closed-loop system. Finally, simulation results demonstrate the feasibility of the proposed maximum power tracking controller based on prescribed performance function and extended state observer.