This paper addresses the problem of adaptive fixed-time attitude stabilization for uncertain rigid spacecraft with inertia uncertainties, external disturbances, actuator saturations, and faults. A nonsingular fixed-time sliding mode surface is constructed so that the settling time of the established surface is independent of the system initial states. By employing an exponential function in the controller design, an adaptive fixed-time control scheme is proposed to shorten the time during which the system states reach the sliding mode surface. With the proposed control method, the information on the bound of the lumped uncertainty is not needed in prior but estimated by the designed update laws. The fixed-time convergence of both the attitude and angular velocity is established, and comparative simulations are presented to illustrate the effectiveness of the proposed control scheme.

In this article, an echo state network (ESN)-based backstepping adaptive iterative learning control scheme is proposed for nonlinear strict-feedback systems performing the same operation repeatedly over a finite-time interval. Different from most of the output tracking approaches, an error-tracking approach is presented using the backstepping technique, such that the tracking error can follow a prespecified error trajectory without any requirement on the initial value of system states. Then, a novel Lyapunov function is constructed to deal with the unknown state-dependent gain function of the controller design. The uncertain nonlinearities are approximated by employing ESNs with simple feedback structures, and the weight update laws are developed by combining the parameter adaptation in the time domain and iteration domain. Moreover, the proposed control scheme is further extended to handle the strict-feedback systems with input saturations. Through the Lyapunov-like synthesis, the closed-loop stability and error convergence of the proposed error-tracking control scheme are analyzed in the presence of the approximation errors. Numerical simulations are provided to verify the effectiveness of the proposed scheme.

The paper mainly concerns the performance improvement of active flux (AF) based sensorless control for permanent magnet synchronous machines (PMSMs) in the low-speed range. An adaptive position observer based on the AF model is adopted to provide rotor position estimate. A convenient implementation of phase voltage measurement circuit mainly consists of a divider and a low-pass filter (LPF) is designed. Then the AF-based observer uses the measured phase voltages directly instead of command voltages. Meanwhile, the phase shift and amplitude attenuation due to the hardware LPF are compensated. Moreover, a robust online resistance and inductance identification strategy is integrated to the position observer. The high-frequency sinusoidal voltages are imposed on the αβ-axis to identify the d- and q-axis inductances, and the DC voltage injected on the α-axis is used to identify the resistance. The experimental results carried out on a 150-W PMSM drive demonstrate the effectiveness of the proposed sensorless control strategy. With the use of phase voltage measurements, the low-speed operating range can be extended and speed reversal can also be implemented. And the online multiple parameter identification can improve the accuracy of the estimated position.

In this paper, an adaptive repetitive learning control (ARLC) scheme is proposed for permanent magnet synchronous motor (PMSM) servo systems with bounded nonparametric uncertainties, which are divided into two separated parts. The periodically nonparametric part is involved in an unknown desired control input, and a fully saturated repetitive learning law with a continuous switching function is developed to ensure that the estimate of the unknown desired control input is continuous and confined with a prespecified region. The non-periodically nonparametric part is transformed into the parametric form and compensated by designing the adaptive updating laws, such that a prior knowledge on the bounds of uncertainties is not required in the controller design. With the proposed ARLC scheme, a high steady-state tracking accuracy is guaranteed, and comparative experiments are provided to demonstrate the effectiveness and superiority of the proposed method.

This paper concerns with the adaptive iterative learning control using neural networks for systems performing repetitive tasks over a finite time interval. Two standing issues of such iterative learning control processes are addressed: one is the initial condition problem and the other is that related to the approximation error. Instead of the state tracking, an error tracking approach is proposed to tackle the problem arising from arbitrary initial shifts. The desired error trajectory is prespecified at the design stage, suitable to different tracking tasks. The initial value of the desired error trajectory for each cycle is required to be the same as that of the actual error trajectory. It is just a requirement for the initial value of the desired error trajectory, but does not pose any requirement for the initial value of the actual error trajectory. It is shown that the actual error trajectory is adjustable and is able to converge to a prespecified neighborhood of the origin, while all variables of the closed-loop system are of uniform boundedness. The robustness improvement in case of nonzero approximation error is made possible due to the use of a deadzone modified Lyapunov functional. The resultant estimation for the bound of the approximation error avoids deterioration in tracking performance. The effectiveness of the designed learning controller is validated through an illustrative example.