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To meet the ever-increasing demand for next-generation lithography machines, the actuator plays an important role in the achievement of high acceleration of the wafer stage. However, the voice coil motor, which is widely used in high-precision positioning systems, is reaching its physical limits. To tackle this problem, a novel way to design the actuator using the magnetoresistance effect is argued due to the high force densities. However, the strong nonlinearity limits its application in the nan-positioning system. In particular, the hysteresis is coupled with eddy effects and displacement, which lead to a rate-dependent and displacement-dependent hysteresis effect in the reluctance actuator. In this paper, a Hammerstein structure is used to model the rate-dependent reluctance actuator. At the same time, the displacement-dependent of the model is regarded as the interference with the system. Additionally, a control strategy combining inverse model compensation and the disturbance observer-based discrete sliding mode control was proposed, which can effectively suppress the hysteresis effect. It is worthy pointing out that the nonlinear system is transformed into a linear system with inversion bias and disturbance by the inverse model compensation. What is more, the sliding mode controller based on the disturbance observer is designed to deal with the unmodeled dynamics, displacement disturbances, and model identification errors in linear systems. Thus, the tracking performance and robustness to external disturbances of the system are improved. The simulation results show that it is superior to the PI controller combined with an inverse compensator and even to the discrete sliding mode controller connected with inverse compensator, confirming the effectiveness of the novel control method in alleviating hysteresis. [ABSTRACT FROM AUTHOR]

To meet the ever-increasing demand for next-generation lithography machines, the actuator plays an important role in the achievement of high acceleration of the wafer stage. However, the voice coil motor, which is widely used in high-precision positioning systems, is reaching its physical limits. To tackle this problem, a novel way to design the actuator using the magnetoresistance effect is argued due to the high force densities. However, the strong nonlinearity limits its application in the nan-positioning system. In particular, the hysteresis is coupled with eddy effects and displacement, which lead to a rate-dependent and displacement-dependent hysteresis effect in the reluctance actuator. In this paper, a Hammerstein structure is used to model the rate-dependent reluctance actuator. At the same time, the displacement-dependent of the model is regarded as the interference with the system. Additionally, a control strategy combining inverse model compensation and the disturbance observer-based discrete sliding mode control was proposed, which can effectively suppress the hysteresis effect. It is worthy pointing out that the nonlinear system is transformed into a linear system with inversion bias and disturbance by the inverse model compensation. What is more, the sliding mode controller based on the disturbance observer is designed to deal with the unmodeled dynamics, displacement disturbances, and model identification errors in linear systems. Thus, the tracking performance and robustness to external disturbances of the system are improved. The simulation results show that it is superior to the PI controller combined with an inverse compensator and even to the discrete sliding mode controller connected with inverse compensator, confirming the effectiveness of the novel control method in alleviating hysteresis.

Objective: The purpose of this paper is to present a structural decomposition subspace (SDSS) method for decomposition of the joint torque to intrinsic, reflexive, and voluntary torques and identification of joint dynamic stiffness. Methods: First, it formulates a novel state-space representation for the joint dynamic stiffness modeled by a parallel-cascade structure with a concise parameter set that provides a direct link between the state-space representation matrices and the parallel-cascade parameters. Second, it presents a subspace method for the identification of the new state-space model that involves two steps: 1) the decomposition of the intrinsic and reflex pathways and 2) the identification of an impulse response model of the intrinsic pathway and a Hammerstein model of the reflex pathway. Results: Extensive simulation studies demonstrate that SDSS has significant performance advantages over some other methods. Thus, SDSS was more robust under high noise conditions, converging where others failed; it was more accurate, giving estimates with lower bias and random errors. The method also worked well in practice and yielded high-quality estimates of intrinsic and reflex stiffnesses when applied to experimental data at three muscle activation levels. Conclusion: The simulation and experimental results demonstrate that SDSS accurately decomposes the intrinsic and reflex torques and provides accurate estimates of physiologically meaningful parameters. Significance: SDSS will be a valuable tool for studying joint stiffness under functionally important conditions. It has important clinical implications for the diagnosis, assessment, objective quantification, and monitoring of neuromuscular diseases that change the muscle tone. [ABSTRACT FROM AUTHOR]

Tremor is an involuntary motion which is a common complication of Parkinson׳s disease and Multiple Sclerosis. A promising treatment is to artificially contract the muscle through application of induced electrical stimulation. However, existing controllers have either provided only modest levels of suppression or have been applied only in simulation. To enable more advanced, model-based control schemes, an accurate model of the relevant limb dynamics is required, together with identification procedures that are suitable for clinical application. This paper proposes such a solution, explicitly addressing limitations of existing methodologies. These include model structures that (i) neglect critical features, and (ii) restrict the range of admissible control schemes, together with identification procedures that (iii) employ stimulation inputs that are uncomfortable for patients, (iv) are overly complex and time-consuming for clinical use, and (v) cannot be automated. Experimental results confirm the efficacy of the proposed identification procedures, and show that high levels of accuracy can be achieved in a short identification time using test procedures that are suitable for future transference to the clinical domain. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the focus will be on approximating nonlinear large scale mathematical model of process systems using full order block-structured model. Further, the objective is to achieve a reduced order model for the nonlinear large model with reduced computational complexity, while at the same time being the good approximation of nonlinear model. The modeling approach used for this purpose is block structure models. Input–output Hammerstein structure referred to the classical Hammerstein model has been extended to new Hammerstein structure making use of states and inputs, hence called as input-state (IS) Hammerstein structure. In this paper it is shown that expansion of Taylor series leads to IS-Hammerstein structure. The accuracy of the approximation is improved by including higher order (second order) approximation. The input-state Hammerstein structure provides opportunities for model reduction in context of reducing the computational load by order reduction of states and Jacobians. IS-full order Hammerstein model has been implemented on a case study from the process industry namely the high purity distillation column. Within the operational domain of a process, the IS-Hammerstein structure provides a reduced order mode that can be used for online application purposes (i.e., optimization, model predictive control, etc.). [Copyright &y& Elsevier]

Article deals with the tasks of ore beneficiation decentralized control based on the Hammerstein structure with distributed parameters. It is found that the real distributed Hammerstein system can be obtained through the traditional by means of space-time decomposition Simulation results of useful component content in the output product of the magnetic separation were obtained. [ABSTRACT FROM AUTHOR]