This article investigates the event-triggered model predictive control (ETMPC) problem for nonlinear systems with the bounded disturbance. First, a novel adaptive event-triggered mechanism without Zeno behaviors, in which the triggering threshold can constantly be adjusted with the change of the system state, is proposed for computational load reduction. Then, an adaptive prediction horizon update strategy is proposed to further reduce the computational complexity of the optimization problem at each triggering instant. Moreover, a dual-mode ETMPC algorithm is developed, and sufficient conditions on the algorithm feasibility and the system robust stability are provided. Through a simulation example, the results show that the proposed scheme can use fewer computational resources and a shorter calculation time for solving the optimization problem while ensuring satisfactory system performances than the existing ones.

A stick-slip/inchworm hybrid rotary piezomotor based on a symmetric triangular driving mechanism, which can simultaneously achieve the benefits of both stick-slip and inchworm motors, was reported in this letter. It is based on the principle of stick-slip motors, and, inspired by the clamping-releasing actions from inchworm motors, it employs a symmetric triangular driving mechanism to generate a clamping action during the stick phase and a releasing action during the slip phase. Compared with stick-slip motors, it involves a clamping action during the stick phase and a releasing action during the slip phase, thus resulting in a larger driving force. Compared with inchworm motors, which require active control and coordination of clamping/releasing modules with feeding modules, it involves the control and operation of only one feeding piezoactuator without any actively controlled clamping/releasing module. Therefore, the control is easier, and a much larger operation frequency and driving speed can be achieved. Under the sawtooth waveform voltage of 90 V at 2600 Hz with a self-holding torque of 4 N m, the prototype achieved a no-load speed higher than 0.6 rad/s, a load torque capacity larger than 1.8 N m, and a weight carrying capacity more than 100 kg for both clockwise and anticlockwise directions. Compared with load torque capacity and weight carrying capacity in the reported stick-slip and inchworm rotary piezomotors, the current levels in terms of the same driving speed have been improved over 60 times and 12 times, respectively, in the proposed hybrid motor.

In this paper, a new discrete-time data-driven distributed learning control strategy for frequency/voltage regulation and active/reactive power sharing of islanded microgrids is proposed. Instead of using the static droop relationship and the conventional primary-secondary hierarchical control structure, a new control framework is adopted and a neural network is used to learn the control law. The neural network is tuned online using the operational system input/output data with no training phase. As a result, the transient performance of microgrids is improved and a remarkable plug-and-play capability is also achieved. Moreover, the stability of the closed-loop system is analyzed through the Lyapunov approach, where the interactions between different distributed energy resources are considered. The effectiveness of the proposed method is demonstrated by real-time hardware-in-the-loop experiment of a typical microgrid.

In this paper, identification and control for a class of nonlinear systems with unknown constant or variable control gains are investigated. By reformulating the original system dynamic equation into a new form with a unit control gain and introducing a set of filtered variables, a novel neural network (NN) estimator is constructed and a new estimation error is used to update the augmented weights. Based on the identification results, two singularity-free NN indirect adaptive controllers are developed for nonlinear systems with unknown constant control gains or variable control gains, respectively. Because the singularity problem is eradicated, the proposed methods remove limitations on parameter estimates that are used to guarantee the positiveness of the estimated control gain. Consequently, a more accurate estimation result can be achieved and the system state can track the given reference signal more precisely. The effectiveness of the proposed identification and control algorithms are tested and the superiority of the proposed singularity-free approach is demonstrated by simulation results.

Discontinuous reference signal is common in electromechanical systems, which can degrade control performances and raise system safety issues. To deal with these problems, this article proposes a safe dual-layer nested adaptive prescribed performance control approach for nonlinear systems, which ensures predefined transient and steady-state performances for the discontinuous reference signal. A monitoring mechanism is first established to detect discontinuous instants of the reference signal, and then the initial values of the prescribed performance boundaries are reformulated to cope with the discontinuities and maintain system stability. Different from the traditional prescribed performance control approaches, a novel dual-layer nested adaptive sliding mode compensation technique is introduced into the controller design to effectively handle system uncertainties without requiring prior upper bounds information. Simulation and experimental results on nonlinear servo systems are presented to validate the effectiveness of the proposed method.