International Journal of Adaptive Control and Signal Processing最新文献

This article proposes a fixed-time prescribed performance controller with an adaptive disturbance observer to enhance disturbance rejection and dynamic performance in a permanent magnet synchronous motor (PMSM). Firstly, an adaptive control law designed using a fixed-time prescribed performance function ensures transient behavior and steady-state performance strictly adhere to preset boundary constraints. Secondly, a fixed-time command filter resolves the repeated differentiation issue of virtual control signals in backstepping, while an error compensation signal further improves tracking accuracy. Friction effects and unknown load torque are treated as lumped disturbances, with a novel adaptive disturbance observer developed to suppress these disturbances, thereby improving system robustness. Finally, simulations and experiments validate the algorithm's effectiveness.

Time-varying uncertainties, often with unknown bounds, are ubiquitous in mechanical systems. The lack of a priori knowledge of these uncertainties impedes the evaluation of their effects on system performance, leading to deteriorating control precision and potential instability. To address this challenge, this study presents an adaptive robust model predictive control (ARMPC) strategy and a control switching mechanism to tackle such uncertainties, whether from internal or external origins. First, the nominal system is decoupled from the uncertain components. A feedforward compensation term based on Udwadia-Kalaba approach and an optimal feedback term from the receding horizon control framework are elaborated to achieve rapid convergence, coupled with an uncertainty rejection component to guarantee the uniform boundedness and uniform ultimate boundedness of the closed-loop system. Second, an adaptive robust controller satisfying the input saturation constraint is proposed, serving as a viable alternative when the optimization problem is infeasible. Finally, the efficacy of the ARMPC scheme is demonstrated through simulations of a two-link R-R mechanical manipulator system and a path tracking example, showcasing its superior performance in terms of convergence rate, overshoot, and steady-state error under different levels of uncertainties.

The synchronization control problem of coupled neural networks based on a proportional-integral observer is addressed in this paper. Since the state vector of each node may not be measured directly, a proportional-integral observer is established to estimate the state information of the plant, thus improving the design method of the observer. In addition, considering the limited communication bandwidth, the coding-decoding scheme is adopted to relieve the bandwidth pressure and improve transmission efficiency effectively. The decoding state obtained by the coding-decoding scheme is used as the basis for the designed controller, and it is deduced how the upper bounds of the coding-decoding scheme parameters relate to the exponential ultimate boundedness of the synchronization error system. Finally, an example is offered to demonstrate the validity of the proposed method.

In this paper, an adaptive fuzzy output feedback control method is proposed for stochastic quadrotor unmanned aerial vehicles, which accounts for output constraints and unknown nonlinear functions. In the results of traditional finite-time stability studies, the control strategy often fails to converge faster than the exponential rate when the initial state is far from the origin. To overcome this issue, we present a novel stochastic fast finite-time stable controller for quadrotor unmanned aerial vehicles. During the design process, the output constraint problem is addressed by constructing a barrier Lyapunov function. The design leverages a barrier Lyapunov function to handle output constraints and utilizes the fuzzy logic system to model and approximate nonlinearities. Furthermore, a fuzzy logic system observer is designed based on an adaptive backstepping approach to estimate states that cannot be directly measured. Simulation results demonstrate that the proposed control method ensures fast finite-time stability in probability for the quadrotor system. The validity of the presented scheme is confirmed by simulation results.

This article focuses on an unresolved sampled-data stabilizing control problem pertaining to a concerned nonlinear system with logarithmic quantized input and input unmodeled dynamics. The M-filters are employed to handle the input unmodeled dynamics, while the radial basis function neural networks (RBFNNs) realize the approximation for existing nonlinearities in the backstepping design procedure. Under the appropriate choice of Lyapunov function candidates (LFCs), the analysis demonstrates that the resulting sampled-data stabilizer enables the formulated closed-loop system to have practically asymptotic stability through selecting suitable parameters and an allowable sampling period. Simulation results, which are exported from a power system, reflect the effectiveness of the developed control scheme.

In this work, we investigate the error feedback regulator problem for an unstable wave equation in which the output is anti-collocated with the control input, and all feasible channels have disturbance. Through the implementation of a variable transformation, an auxiliary system is derived where the measured error serves as its boundary output, and all unknown disturbances are positioned at the output terminal. Afterward, an adaptive observer is developed to estimate the state of the auxiliary system, as well as the unknown amplitudes of the disturbance and reference signals, depending on the tracking error. We construct an auxiliary system for this observer, wherein all disturbance estimation terms are consolidated at the end alongside the control. By the backstepping method, the final regulator is obtained which is proved to be applicable. Numerical results are shown for the main results.

Distributed adaptive fault-tolerant consensus control (FTCC) enables nonlinear multi-agent systems (NMASs) to achieve control tasks with expected or slightly reduced performance metrics in the presence of faults in certain components. When dealing with NMASs under sensor faults and input saturation, the control design becomes extremely challenging. Therefore, this paper proposes a novel distributed adaptive FTCC method for a class of strict-feedback NMASs with multiple sensor failures and input saturation. First, to surmount the obstacles presented by multiple sensor failures, a novel constructive design approach combining parameter separation and function recombination is proposed. Then, to tackle the challenges encountered in the design process of the derivative of the virtual control law (VCL), a first-order filter (FOF) is introduced. Furthermore, to alleviate the repercussions caused by input saturation in the system, an auxiliary system is employed to offset the variation between saturated and designed control inputs. Combining the improved backstepping design technique, an adaptive state feedback controller is recursively developed. It has been verified that all signals within the closed-loop NMASs remain bounded, and the output consensus errors converge to an arbitrarily small vicinity of the origin. Finally, simulation experiments validate the efficacy of the proposed controller.

This study addresses the adaptive global predefined-time tracking control problem for a class of switched nonlinear systems with input saturation constraints. Firstly, by introducing the hyperbolic tangent function, a piecewise smooth function is constructed. This function not only effectively approximates the input saturation constraints but also accurately handles them. Subsequently, a multi-dimensional Taylor network (MTN) is utilized to provide a novel estimation method for the nonlinear terms in the backstepping control process. Additionally, a finite-time differentiator is introduced to accurately estimate the first-order derivative of the virtual controller. Notably, the proposed multi-switching-based global predefined-time controller not only ensures that the tracking error converges to a small neighborhood of zero within a predefined time, but also achieves globally uniform ultimate boundedness for all signals in the closed-loop system. Finally, to further validate the effectiveness of the control algorithm, both numerical and simulation examples are provided.

Measurement delays, nonlinearities and external uncertainties cannot be avoided for wind energy system under complex working environment in practical. This article proposes an event-triggered Lyapunov-based model predictive control (LMPC) framework for wind energy systems. To better reflect the practical environment, a nonlinear system model is established for explicitly incorporating measurement delays and external uncertainties. An LMPC method is designed for wind energy system with Lyapunov constraints to improve the control performance. A triggering scheme is proposed for LMPC to reduce optimization computation time in controller design, which makes the proposed LMPC more applicable. The closed-loop stability is demonstrated for the proposed event-triggered LMPC with measurement delays. Finally, simulation experiments are conducted on a 5-MW wind energy system to validate and illustrate the effectiveness of the proposed LMPC approach.