By applying the fractional Lyapunov direct method, we investigate the robust consensus tracking problem for a class of uncertain fractional-order multiagent systems with a leader whose input is unknown and bounded. More specifically, multiple fractional-order systems with heterogeneous unknown nonlinearities and external disturbances are considered in this paper, which include the second-order multiagent systems as its special cases. First, a discontinuous neural network-based (NN-based) distributed robust adaptive algorithm is designed to guarantee the consensus tracking error exponentially converges to zero under a fixed topology. Also the derived results are further extended to the case of switching topology by appropriately choosing multiple Lyapunov functions. Second, a continuous NN-based distributed robust adaptive algorithm is further proposed to eliminate the undesirable chattering phenomenon of the discontinuous controller, where the consensus tacking error is uniformly ultimately bounded and can be reduced as small as desired. It is worth noting that all the proposed NN-based robust adaptive algorithms are independent of any global information and thus are fully distributed. Finally, numerical simulations are provided to validate the correctness of the proposed algorithms.

This paper focuses on composite nonlinear feedback (CNF) controller design for tracking control problem of strict-feedback nonlinear systems with input saturation to address the improvement of transient performance. First, without considering the input saturation, a stabilisation control law is designed by using standard backstepping technique for the nonlinear system, then a feedforward control law is added to the backstepping-based stabilisation control law to construct a tracking control law. The tracking control law is tuned to drive the output of the closed-loop system to track a command input with quick response. Then, an additional nonlinear feedback law is constructed and combined with the tracking control law to obtain a CNF control law. The role of this additional nonlinear feedback law is to smoothly change the damping ratio of the closed-loop system while the system output approaches the command input, and to reduce overshoot caused by the tracking control law. It is shown that the extra-adding nonlinear feedback part does not cause the loss of stability of the closed-loop system in its attractive basin.

This paper investigates the control problem of making an autonomous vehicle modeled as a nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new formal control framework based on the control barrier function (CBF) and potential field method is proposed, which aims at eliminating the singular points caused by the conflict between the target and the obstacle in traditional CBF methods. A feasibility detection mechanism algorithm is proposed to detect the task in terms of workspace topology and determines the structure of the framework. On the one hand, obstacle avoidance can always be fulfilled such that the safety of the system is guaranteed. On the other hand, satisfaction for the target formulas is discussed in terms of obstacles, workspace topology, and formula preference. The proposed control framework generates controllers based on different feasibility types of tasks automatically, which shows global performance in both safety guarantee and task achievement. Finally, the effectiveness of the overall framework is illustrated by two examples.

The problem of how to replace an existing continuous-time PI controller with a first-order linear active disturbance rejection controller is investigated. A parametric tuning approach, which is based on the parameters of the PI controller, is proposed. With the first-order linear active disturbance rejection controller generated by the approach, the control system can have almost same gain crossover frequency and phase margin to with the PI controller, while have better performance on rejecting measurement noise and attenuating overshoot when phase margin is not enough.