International Journal of Adaptive Control and Signal Processing最新文献

In this paper, an indirect filtering fault-tolerant control method is proposed for interconnected time-delay systems with multiple sensor and actuator faults. As is well known, directly utilizing the state information obtained from sensors with faults to constrain the actual state poses great challenges. This article utilizes conservative sensor fault loss information to indirectly apply time-varying asymmetric constraints to the actual state while ensuring compliance with fault state constraints. In addition, the conservatism requirement of the initial state is reduced by utilizing carefully constructed delay constraint functions. To solve the problem of “explosion of complexity,” the derivative of the output signal of the filter is used to replace the derivative of the virtual control rate. The Nussbaum function can effectively alleviate the impact of multiple actuator failures and unknown control directions on the system. The effectiveness of the proposed control strategy is verified through simulation examples.

In this article, an optimized inverse dead-zone control using reinforcement learning (RL) is developed for a class of nonlinear dynamic systems. The dead-zone is frequently occurred in the nonlinear control system, and it can affect the control performance and even cause the system instable. Hence, it is very requisite to consider the effect of dead-zone in the design of control strategy. In this proposed optimized inverse dead-zone control, the basic idea is to find the optimized control as input and the adaptive algorithm to estimate the unknown parameters for the inverse dead-zone function, so that the available dead-zone input for system control can be derived. Comparing with traditional methods, on the one hand, the proposed dead zone inverse method is with fewer adaptive parameters, on the other hand, the RL under identifier-critic-actor architecture is with the simplified algorithm. Finally, theoretical and simulation results manifest the feasibility of the proposed method.

This article considers the trajectory tracking problem of automated guided vehicles with unknown-but-bounded noises and proposes a set-membership filtering (SMF) based model predictive control strategy. A 2-DOF vehicle model is first established with considering the unknown-but-bounded process and measurement noises. Then, a trajectory tracking control scheme is proposed which consists of two parts, that is, a set-membership observer and a model predictive controller. The proposed set-membership observer is able to deal with the unknown-but-bounded noises and obtain the state estimation ellipsoid for the considered automated guided vehicle. Further, the designed model predictive controller utilizes the estimated ellipsoid to compute the control sequence. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method.

This article is aimed to study the parameter identification of the ExpARX system. To overcome the computational complexity associated with a large number of feature parameters, a parameter separation scheme based on the different features of the identification model is introduced. In terms of the phenomenon that the coupling parameters lead to the inability of algorithms, a separable synchronous interactive estimation method is introduced to eliminate the coupling parameters and perform parameter estimation in accordance with the hierarchical principle. For the purpose of achieving high-accuracy performance and reducing complexity, a separable synchronous gradient iterative algorithm is derived by means of gradient search. In order to improve the identification accuracy, a separable synchronous multi-innovation gradient iterative algorithm is proposed by introducing the multi-innovation identification theory. In order to improve the convergence speed, a separable synchronous multi-innovation conjugate gradient iterative algorithm is proposed by introducing the conjugate gradient theory. Finally, a simulation example and a real-life example of piezoelectric ceramics are used to verify the effectiveness of the proposed algorithm.

The thesis studies a stochastic $��{�H�}_{�\infty �}�$ finite-time bounded (S$��{�H�}_{�\infty �}�$FTB) filter design method for time-varying delay Markov jump systems (MJSs) with partially unknown transition rates. First, a Lyapunov–Krasovskii functional with triple integral is built, the free weight matrix is added for lowering the conservatism. And the condition of S$��{�H�}_{�\infty �}�$FTB of the error system is analyzed. Then, according to linear matrix inequalities (LMIs), a new filter design method is presented. Moreover, the dimension of the filter obtained is free, and the filter ensures that the error system is S$��{�H�}_{�\infty �}�$FTB. Finally, the validity and effectiveness of the conclusion of this paper are demonstrated by simulation examples.

In this article, for non-parameterized nonlinear continuous (NPNC) multiple-input multiple-output (MIMO) systems, two combined iteration-domain and time-domain adaptive iterative learning control (ILC) algorithms are proposed to track iteration-varying reference trajectories repetitively over a finite time interval. Different from the general requirement in adaptive control community that the control gain matrices of the controlled systems are real symmetric and positive-definite, only the nonsingular property of the control gain matrices is assumed. Moreover, there are just two adaption parameters and one adaption parameter involved in the proposed two adaptive ILC algorithms respectively such that the computation load and memory-space are greatly saved. A simulation example is utilized to illustrate the effectiveness of the two proposed adaptive ILC algorithms with less adaption parameters.