In this paper, a particle filter design scheme for a robust nonlinear control system of uncertain heat exchange process against noise and communication time delay is presented. The particle filter employs a cluster of particles and associated weights to approximate the posterior distribution of states and is capable of handling nonlinear and non-Gaussian issues. However, when the realistic given noise is much larger than that of the one modeled by the particle filter, the estimated posterior distribution is no longer reliable. Considering that, the exponential weights take the place of the original absolute particle weights in this paper, which act as an adjustment to the particle filter weights for a better state estimation. This adjustment for the weight of the particle filter takes into account the practical significance and can ensure the stability, tracking performance, and continuous operation of the control process incorporated with the particle filter. The simulation verifies the feasibility and usefulness of the method.

This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The research of the viscoelasticity can improve the development of industrial robots, rehabilitation and sports etc. So far, some results have been shown using filtered human arm viscoelasticity measurements. That is, human motor command is removed. As a result, the dynamics of human voluntary component during movements is omitted. In this paper, based on the feedforward characteristics of human multi joint arm, a model is obtained by considering human voluntary components using a support vector regression technique. By employing the learned model, a nonlinear intelligent control of two link robot arm is proposed. Experimental results confirm the effectiveness of this proposal.

In this paper, a nonlinear control approach for an unstable networked plant in the presence of actuator and sensor limitations using robust right coprime factorization is proposed. The actuator is limited by upper and lower constraints and the sensor in the feedback loop is subjected to network-induced unknown time-varying delay and noise. With this nonlinear control method, we first employ right coprime factorization based on isomorphism and operator theory to factorize the plant, so that bounded input bounded output (BIBO) stability can be guaranteed. Next, continuous-time generalized predictive control (CGPC) is utilized for the unstable operator of the right coprime factorized plant to guarantee inner stability and enables the closed-loop dynamics of the system with predictive characteristics. Meanwhile, a second-DoF (degrees of freedom) switched controller that satisfies a perturbed Bezout identity and a robustness condition is designed. By using the CGPC controller that possesses predictive behavior and the second-DoF switched stabilizer, the overall stability of the plant subjected to actuator limitations is guaranteed. To address sensor limitations that exist in networked plants in the form of delay and noise which often cause system performance degradation, we implement an identity operator definition in the feedback loop to compensate for these adverse effects. Further, a pre-operator is designed to ensure that the plant output tracks the reference input. Finally, the effectiveness of the proposed design scheme is demonstrated by simulations.

In this paper, the robustness of a system with sundry disturbed open loop dynamics is investigated by employing robust right coprime factorization (RRCF). These sundry disturbed open loop dynamics are present not only in the feed forward path, but also within the feedback loop. In such a control framework, the nominal plant is firstly right coprime factorized and a feed forward and a feedback controllers are designed based on Bezout identity to ensure the overall stability. Subsequently, considering the sundry disturbed open loop dynamics, a new condition formulated as a disturbed Bezout identity is put forward to achieve the closed loop stability of the system, even in the presence of disturbances existing in sundry open loops, where in the feedback loop a disturbed identity operator is defined. This approach guarantees the system robustness if a specific inequality condition is satisfied. And, it should be noted that the proposed approach is applicable to both linear and nonlinear systems with sundry disturbed open loop dynamics. Simulations demonstrate the effectiveness of our methodology.

Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants. However, the theoretical understanding of its power from the view of optimization is still rare. This study opens the door to a deep understanding of LSR by initiating the analysis. In this paper, we analyze the convergence behaviors of stochastic gradient descent with label smoothing regularization for solving non-convex problems and show that an appropriate LSR can help to speed up the convergence by reducing the variance. More interestingly, we proposed a simple yet effective strategy, namely Two-Stage LAbel smoothing algorithm (TSLA), that uses LSR in the early training epochs and drops it off in the later training epochs. We observe from the improved convergence result of TSLA that it benefits from LSR in the first stage and essentially converges faster in the second stage. To the best of our knowledge, this is the first work for understanding the power of LSR via establishing convergence complexity of stochastic methods with LSR in non-convex optimization. We empirically demonstrate the effectiveness of the proposed method in comparison with baselines on training ResNet models over benchmark data sets.