A method to produce a desired current pattern in a multiple-source EIT system using voltage sources is presented. Application of current patterns to a body is known to be superior to the application of voltage patterns in terms of high spatial frequency noise suppression, resulting in high accuracy in conductivity and permittivity images. Since current sources are difficult and expensive to build, the use of voltage sources to apply the current pattern is desirable. An iterative algorithm presented in this paper generates the necessary voltage pattern that will produce the desired current pattern. The convergence of the algorithm is shown under the condition that the estimation error of the linear mapping matrix from voltage to current is small. Simulation results are presented to illustrate the convergence of the output current.

Given a discrete-time controlled bilinear systems with initial state and output function , we investigate the maximal output set Θ(Ω) = { ∈ ℝ , ∈ Ω, ∀ ≥ 0} where Ω is a given constraint set and is a subset of ℝ . Using some stability hypothesis, we show that Θ(Ω) can be determined via a finite number of inequations. Also, we give an algorithmic process to generate the set Θ(Ω). To illustrate our theoretical approach, we present some examples and numerical simulations. Moreover, to demonstrate the effectiveness of our approach in real-life problems, we provide an application to the SI epidemic model and the SIR model.

With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model - based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.

Traditional rigid robots face significant challenges in congested and tight environments, including bulky size, maneuverability, and safety limitations. Thus, soft continuum robots, inspired by the incredible capabilities of biological appendages such as octopus arms, starfish, and worms, have shown promising performance in complex environments due to their compliance, adaptability, and safety. Different actuation techniques are implemented in soft continuum robots to achieve a smoothly bending backbone, including cable-driven actuators, pneumatic actuators, and hydraulic actuation systems. However, designing and developing efficient actuation mechanisms, motion planning approaches, and control algorithms are challenging due to the high degree of redundancy and non-linearity of soft continuum robots. This article profoundly reviews the merits and drawbacks of soft robots' actuation systems concerning their applications to provide the readers with a brief review reference to explore the recent development of soft robots' actuation mechanisms technology. Moreover, the authors have surveyed the recent review studies in controller design of continuum robots as a guidance for future applications.