International Journal of Adaptive Control and Signal Processing最新文献

This article studies the problem of finite impulse response (FIR) system identification with binary sensor and the either-or communication, together with the packet drop for quantized inputs. We propose online identification algorithms for two cases of known and unknown probability of packet drop, and prove strong convergence and asymptotic normality of the algorithms. Additionally, we introduce metrics to describe the speed of the algorithm's convergence when the probability of packet drop is unknown. Finally, the effectiveness of the theoretical results is verified by experiment.

In this article, a fixed-time periodic adaptive event-triggered control for a robotic manipulator is proposed. Combined with the backstepping framework and finite-time control theory, an adaptive fixed-time controller is constructed to achieve the fast convergence performance of the robotic manipulator while maintaining system stability. Besides, a radial basis function neural network (RBFNN) is employed to approximate the uncertainties of the system. Further, Practical robotic applications frequently encounter critical challenges related to limited communication resources, especially during the execution of complex tasks. In contrast to the traditional event-triggered control (ETC) mechanisms that require continuous system monitoring, a novel periodic adaptive event-triggered control (PAETC) is proposed to schedule control signal transmission. The innovative PAETC strategy not only preserves system performance but also substantially reduces both communication burdens and continuous monitoring requirements, thereby enhancing practical implementability. Finally, both theoretical analysis and simulations are conducted to demonstrate the validity of the developed method.

This paper addresses the tracking control problem for a class of constrained parametric nonlinear systems. By employing a constructed nonlinear mapping (NM), the system with time-varying parameters and extended full-state constraints is transformed into an unconstrained nonlinear system. Subsequently, the controller and adaptive law are designed using a modified dynamic surface control (DSC) approach. At each stage of the controller design, a compensating signal is introduced to mitigate the error resulting from the substitution of the linear filter's output for the derivative of the virtual control. This methodology reduces the difficulty of controller design and the complexity of stability analysis. The proposed control algorithm ensures the superior tracking performance while adhering to the extended full-state constraints, and guarantees that all signals are semi-global uniformly ultimately bounded (SGUUB). The effectiveness of the proposed control strategy is validated through stability analysis and numerical simulations.

The accuracy of moving horizon state estimation significantly deteriorates when measurements are contaminated by outliers. Existing moving horizon estimation (MHE) methods that address this issue are restricted to linear systems. This paper proposes an outlier-robust MHE method to address the state estimation problem for nonlinear systems subject to measurement outliers. Specifically, at each sampling instant, the method solves a set of least-squares cost functions to eliminate potentially contaminated measurements one by one. The state estimate corresponding to the optimal cost is retained, and the process repeats as new information becomes available. Subsequently, the concept of uniform observability is introduced within this estimation framework. Applying the uniform observability property and choosing appropriate design parameters, the stability of the proposed estimator is proved. Additionally, a robustness condition is derived, ensuring that the estimator remains resilient to outliers, provided they are sufficiently large. Finally, the parameter design method to achieve stability and robustness in the estimator implementation is presented. The simulation results show the effectiveness of the proposed estimation approach in case the measurements are contaminated.

A Double Internal Loop Recurrent Neural Network (DILRNN) with an adaptive learning rate is proposed for the modeling and control of non-linear dynamical plants. The structure of DILRNN is an alteration of the Fully Connected Recurrent Neural Network (FCRNN). DILRNN contains three feedback loops taken primarily from the context layer to the hidden layer, the time delay of the output layer to the hidden layer, and the time delay of output to output. The parameters of DILRNN are updated using the gradient descent-based dynamic Back-Propagation (BP) algorithm. The Adaptive Learning Rate (ALR) scheme is implemented to ensure that the learning rate value is determined properly in each iteration and improves the performance of the learning algorithm. The effectiveness of the suggested strategy is assessed by considering both the Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) systems and comparing them with state-of-the-art methods. The simulation outcomes show that the proposed model outperforms the Feed Forward Neural Network (FFNN) and other Recurrent Neural Network (RNN) models, which were taken into evaluation in terms of their output and error. Next, a DILRNN controller is implemented using the proposed model to control non-linear dynamical systems. The controller's response is evaluated both in the absence and presence of a disturbance signal to assess the recovery capability of the controller. The response and error of the proposed controller are compared with other neural network controllers and elementary PID controllers.

This paper investigates the adaptive neural network- controlled course tracking of an unmanned surface vehicle (USV) with quantization of signal input. As a first step, the characteristics of the ship's rudder servo system are fully considered and combined with the mathematical representation of the ship's heading control system. This is done to develop a nonlinear third-order response model. The Radial Basis Function (RBF) neural network is constructed to estimate and approximate the unknown functions within a mathematical model of the system, and nonlinear damping terms are employed to counteract external disturbances. Subsequently, a design method for a neural network adaptive quantization controller is proposed. This controller can enable real-time learning and adjustment to address performance degradation caused by signal quantization errors. Based on the Lyapunov theorem, the designed controller has been validated for its dynamic response capability and system stability, ensuring long-term reliable and stable operation. In addition, semiglobally, uniformly bound signals are used in closed-loop systems. Tracking errors are lowered through parameter tuning to trim levels arbitrarily. As a final result, simulation results confirmed the effectiveness and feasibility of the RBF neural network-based adaptive quantification control method for USVs.

This paper mainly investigates the joint estimation of the parameters and states for the fractional-order Wiener state space model. Based on the Kalman filter principle, a generalized recursive least squares algorithm with a forgetting factor is proposed. In addition, the filtering-based generalized recursive least squares algorithm is presented, which reduces the influence of colored noise on the parameter estimation. A gradient identification algorithm is introduced to estimate the order of the fractional-order. Under the persistent excitation conditions, the analysis indicates that the proposed parameter estimation algorithm can estimate the fractional-order Wiener state space system. A simulation example is given to confirm that the proposed algorithms are effective.

Task allocation in multi-robot systems has been a critical area of research, with applications spanning various industries, such as logistics, agriculture, and manufacturing. The allocation of tasks to multi-robots improves the system performance, which generally minimizes total resource consumption or cost needed for performing a group of tasks. In dynamic multi-robot systems, efficient task allocation is critical for optimizing system performance, especially in response to environmental changes like faults or the actions of other robots. Therefore, a new approach called reptile honey badger optimization algorithm_deep quantum neural network (RHBA_DQNN) is framed for task allocation in multi-robot systems. At first, the tasks are grouped utilizing the fuzzy local information C-means (FLICM) clustering model. Then, the assignment of tasks for the group of robots is conducted using the devised RHBA, where monetary cost, distance, time, and completion time are considered objective functions. The proposed RHBA is the combination of the reptile search algorithm (RSA) and honey badger algorithm (HBA). Finally, the penalty cost is decided based on the deep quantum neural network (DQNN). Moreover, the RHBA_DQNN has obtained a minimum overall cost, execution time, distance, and monetary cost of 81.251, 9.99, 1.600, and 0.249, respectively.