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Nirmal Murmu

Bio: Nirmal Murmu is an academic researcher from University of Calcutta. The author has contributed to research in topics: Inverted pendulum & Control theory. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Papers
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Proceedings ArticleDOI
01 Dec 2018
TL;DR: This work proposes a 2-rate controller to stabilize the cart inverted pendulum system (CIPS) and shows that the GM of the overall system is improved considerably as compared to the results obtained using LDTI controllers.
Abstract: In a Multi(2)-rate controller the input and output of the plant are sampled at different rates. In this context, it is important to note that several attempts have been made in the literature to achieve zero-placement using 2-rate control. These attempts, however, involved only the fast-input type of control which, unfortunately, yields unacceptable oscillations in the output. The fast-output type, on the other hand, does not have this problem inherently, but it’s zero placement capability has been established recently by viewing it from a 2-Periodic perspective. This zero-placement capability of 2-rate control ensures robustness of the compensated system in terms of satisfactory gain margin (GM) values. This work proposes a 2-rate controller to stabilize the cart inverted pendulum system (CIPS) and shows that the GM of the overall system is improved considerably as compared to the results obtained using LDTI controllers.

1 citations

Proceedings ArticleDOI
07 Oct 2020
TL;DR: A PSO (Particle Swarm Optimization) based tuning rule for 2-rate controllers for the stabilization of cart-inverted pendulum system to have an efficient and effective search by a group of particles in the solution space to find out the optimal locations of the controller poles and loop-zeros that yield satisfactory loop robustness.
Abstract: This paper proposes a PSO (Particle Swarm Optimization) based tuning rule for 2-rate controllers for the stabilization of cart-inverted pendulum system. In [1] the 2-Rate controller, a special subset of 2-periodic controller, has been designed to achieve poles as well as loop-zero placement leading to an improved gain margin (GM) as compared to its LTI counterpart. In the work of [2], the 2-Rate compensation technique was employed to stabilize the cart inverted pendulum system. However, the controller design approach was based on trial and error method and the GM of the overall system depends on the choice of controller poles and loop-zeros. In order to formalize this problem of selecting the proper locations of the controller poles and loop-zeros, this paper proposes a PSO based approach. The objective is to have an efficient and effective search by a group of particles in the solution space to find out the optimal locations of the controller poles and loop-zeros that yield satisfactory loop robustness.

Cited by
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Proceedings ArticleDOI
07 Oct 2020
TL;DR: A PSO (Particle Swarm Optimization) based tuning rule for 2-rate controllers for the stabilization of cart-inverted pendulum system to have an efficient and effective search by a group of particles in the solution space to find out the optimal locations of the controller poles and loop-zeros that yield satisfactory loop robustness.
Abstract: This paper proposes a PSO (Particle Swarm Optimization) based tuning rule for 2-rate controllers for the stabilization of cart-inverted pendulum system. In [1] the 2-Rate controller, a special subset of 2-periodic controller, has been designed to achieve poles as well as loop-zero placement leading to an improved gain margin (GM) as compared to its LTI counterpart. In the work of [2], the 2-Rate compensation technique was employed to stabilize the cart inverted pendulum system. However, the controller design approach was based on trial and error method and the GM of the overall system depends on the choice of controller poles and loop-zeros. In order to formalize this problem of selecting the proper locations of the controller poles and loop-zeros, this paper proposes a PSO based approach. The objective is to have an efficient and effective search by a group of particles in the solution space to find out the optimal locations of the controller poles and loop-zeros that yield satisfactory loop robustness.