This paper proposes a discrete-time repetitive optimal control of electrically driven robotic manipulators using an uncertainty estimator. The proposed control method can be used for performing repetitive motion, which covers many industrial applications of robotic manipulators. This kind of control law is in the class of torque-based control in which the joint torques are generated by permanen...

Abstract. This paper is a continuation of the work in [11] and [2] on the problem of estimating by a linear estimator, N unobservable input vectors, undergoing the same linear transformation, from noise-corrupted observable output vectors. Whereas in the aforementioned papers, only the matrix representing the linear transformation was assumed uncertain, here we are concerned with the case in wh...

This paper considers a single channel speech enhancement algorithm, which is based on our previous work on βorder minimum mean square error (MMSE) spectral estimation. We propose to make β a function of both local and frame signal-to-noise ratios (SNRs) in order to achieve more effective preservation of weak speech components. Moreover, by taking into account the speech-presence uncertainty in ...

We prove convergence and quasi-optimality of a lowest-order adaptive boundary element method for a weakly-singular integral equation in 2D. The adaptive meshrefinement is driven by the weighted-residual error estimator. By proving that this estimator is not only reliable, but under some regularity assumptions on the given data also efficient on locally refined meshes, we characterize the approx...

This paper presents a new approach for estimating the state of a linear dynamic system when two different types of uncertainties are present simultaneously. The first type of uncertainty is a stochastic process with given distribution. The second type of uncertainty is only known to be bounded, the exact underlying distribution is unknown. This includes inequality constraints between state vari...

this paper presents a stable new algorithm for force/position control in robot manipulators. in this algorithm, position vectors are measured by sensors and then used in the control law. since using force sensor has some issues such as high costs and technical problems, an approach is presented to overcome these issues. in this respect, force sensor is replaced by an adaptive fuzzy estimator to...

This note investigates the control of stochastic nonlinear systems with parametric uncertainty. The class of systems considered are single-input–single-output and in strict-feedback form, with the performance measured with respect to a risk-sensitive cost criterion. The uncertainty in the system description is assumed to be linearly parameterized, where the unmeasured parameters are generated b...

This paper proposes an extreme learning machine (ELM)-based adaptive sliding mode control strategy for the receiver-side buck converter system in wireless power transfer subjecting to lumped uncertainty. The proposed utilizes a singularity-free fixed-time (FTSM) feedback control, which ensures convergence both variable and voltage tracking error. An ELM-based uncertainty bound estimator is furt...

Type-2 fuzzy set theory is one of the most powerful tools for dealing with the uncertainty and imperfection in dynamic and complex environments. The applications of type-2 fuzzy sets and soft computing methods are rapidly emerging in the ecological fields such as air pollution and weather prediction. The air pollution problem is a major public health problem in many cities of the world. Predict...