We consider the analytical control design for a pair of switched linear multiple-input multiple-output (MIMO) systems that are subject to arbitrary switching signals. A state feedback controller design method is propo...
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We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a poster...
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An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian ...
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Basis splines enable a time-continuous feasibility check with a finite number of constraints. Constraints apply to the whole trajectory for motion planning applications that require a collision-free and dynamically fe...
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In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a diffic...
In the operating rooms and the intensive care unit, it is crucial to manage the patient’s hemodynamic status, which includes factors like cardiac output and mean arterial pressure. Anesthesiologists confront a difficult task while monitoring high-risk patients. Cardiac output optimization has been found to enhance the result of high-risk patients in terms of hospital stay, mortality rate, post-operative problems, etc. The application of standard control approaches is restricted because the mean arterial pressure response of a patient using vasoactive medicines is modeled by a first-order dynamical system with time-varying parameters and a time-varying delay in the control input. In order to circumvent implementation challenges, this work develops an approximation technique that describes the system using a higher-order model. Predictive control is therefore used to comprehend the practical application of higher-order hemodynamic systems. The effectiveness of this strategy is demonstrated by the simulations and outcomes that are given.
作者：
A. IrawanM. I. Putra AzaharM. A. HashimiRobotics
Intelligent Systems & Control Engineering (RISC) Research Group Faculty of Electrical & Electronics Engineering Technology Universiti Malaysia Pahang Pahang Malaysia
The paper presents the proposed sensorless force estimator design for pneumatic robot fingertip by using gravitational compensation and pressure changed in pneumatic cylinder piston. The approach is done to replace th...
The paper presents the proposed sensorless force estimator design for pneumatic robot fingertip by using gravitational compensation and pressure changed in pneumatic cylinder piston. The approach is done to replace the commercial force sensor that may be expensive for heavy-duty configuration. The formulation was done by considering the torque of robot's finger joint, finger dimension as well as its actuator and the different pressures in cylinder piston. The gravitational force is calculated from the geometry of the robot's finger as dynamic gain for the force of pneumatic cylinder. The proposed method is validated on a heay-duty pneumatic Tri-grasper Robot with the simple basic movement and blocked randomly by human barehand. The results show that the force output by the estimator is almost identical to the loadcell sensor that attached on the fingertip at about 2% error in average. The sensitivity is a bit low for small and fragile material but enough for heavy-duty application that generally with hard and rough surfaces.
While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To th...
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While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees.
The framework of linear parameter-varying (LPV) systems has shown to be a powerful tool for the design of controllers for complex nonlinear systems using linear tools. In this work, we derive novel methods that allow ...
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This paper deals with the development of an empirical model to describe the dynamics of milk acidification process for the production of acid casein. The model is based on the dynamics of the pH profiles during the ac...
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This paper deals with the development of an empirical model to describe the dynamics of milk acidification process for the production of acid casein. The model is based on the dynamics of the pH profiles during the acidification process since pH is a good indicator of the status of the precipitation. Data from laboratory experiments has been used to identify the parameters in the proposed model. Calibration and validation results, with an independent data set, show that the model is able to predict accurately the pH at different temperatures and acid addition rates. Furthermore, the model has been used in a simulation study as an advisory tool to suggest acid addition, proving the parsimony of the model.
In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data. This is achieved by a two-step procedure. In the first step, we learn a...
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