Immune algorithm based active PID control for structure systems

Lee, Young Jin; Cho, Hyun Cheol; Lee, Kwon Soon

doi:10.1007/bf03027576

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…The total B-cell concentration generated in this process is evaluated as follows. 30 where, n represents the generation of antibody and antigen reproduction, cfalse(nfalse) is B-cell concentration (or stimulus) at nth generation, THfalse(nfalse) is the concentration of TH cells, TSfalse(nfalse) is the concentration of TS cells, dfalse(nfalse) is the concentration of antigens, mfalse(.false) is a pre-configured nonlinear function that dynamically modifies the inhibition-rate of antibodies, and γ and λ are positive constants that decide the damping strength and response-speed of the immune system. The function mfalse(.false) is inversely proportional to the antigen concentration.…”

Section: Online Adaptation-gain Modulationmentioning

confidence: 99%

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations

2022

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This paper formulates an enhanced Model-Reference-Adaptive-Controller (MRAC) that is augmented with a fuzzy-immune adaptive regulator to strengthen the disturbance-attenuation capability of closed-loop under-actuated systems. The proposed scheme employs the conventional state-space MRAC and augments it with a pre-configured fuzzy-immune mechanism that acts as a superior regulator to dynamically modulate the adaptation gains of the Lyapunov gain-adjustment law. The immunological computations increase the controller's adaptability to flexibly manipulate the damping control effort under exogenous disturbances. The efficacy of the proposed Immune-MRAC law is comparatively analyzed under practical disturbance conditions by conducting real-time hardware experiments on the QNET rotary pendulum. The experimental outcomes validate the faster transient-recovery behavior and stronger damping effort of the proposed control law against the exogenous disturbances while preserving the system's asymptotic stability and control energy efficiency.

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Section: Online Adaptation-gain Modulationmentioning

confidence: 99%

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations

2022

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“…In IA n number of antibodies generated randomly [17]. While affinity of all antibodies is known new population is generated through three steps: replacement, cloning and hyper-mutation.…”

Section: Evolutionary Optimization Techniquesmentioning

confidence: 99%

EOTs Based Fixed Order Controller Design and Performance Analysis

Mahar¹,

Ali²,

Hussain³

et al. 2012

IJMLC

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Evolutionary optimizations techniques (EOTs) are stochastic search techniques that direct a population of solutions towards best possible results by using the natural principles. In recent years, these algorithms have grown to be an accepted optimization tool for many areas of scientific and engineering research, together with control system engineering design. Significant research exists concerning evolutionary algorithms to control system design and robustness performance analysis of controller. But, little work has been done with evolutionary optimization algorithms because of the problems related with robustness performance in early period of the evolution of controllers. Moreover, until recently the robustness performance of controllers based on evolutionary algorithms has not been researched in stipulate. The scope of this research covers generalized robustness performance condition and detailed analysis of the resultant control system.

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“…The biologically-inspired artificial-immune system is a computationally intelligent adaptive mechanism that efficiently rejects the exogenous disturbances [ 55 ]. They mimic the self-regulation capability of biological immune systems to adaptively tune the controller parameters which optimizes the controller’s adaptability to environmental indeterminacies [ 56 ].…”

Section: Introductionmentioning

confidence: 99%

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

2021

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This paper presents an experimental comparison of four different hierarchical self-tuning regulatory control procedures in enhancing the robustness of the under-actuated systems against bounded exogenous disturbances. The proposed hierarchical control procedure augments the ubiquitous Linear-Quadratic-Regulator (LQR) with an online reconfiguration block that acts as a superior regulator to dynamically adjust the critical weighting-factors of LQR’s quadratic-performance-index (QPI). The Algebraic-Riccati-Equation (ARE) uses these updated weighting-factors to re-compute the optimal control problem, after every sampling interval, to deliver time-varying state-feedback gains. This article experimentally compares four state-of-the-art rule-based online adaptation mechanisms that dynamically restructure the constituent blocks of the ARE. The proposed hierarchical control procedures are synthesized by self-adjusting the (i) controller’s degree-of-stability, (ii) the control-weighting-factor of QPI, (iii) the state-weighting-factors of QPI as a function of “state-error-phases”, and (iv) the state-weighting-factors of QPI as a function of “state-error-magnitudes”. Each adaptation mechanism is formulated via pre-calibrated hyperbolic scaling functions that are driven by state-error-variations. The implications of each mechanism on the controller’s behaviour are analyzed in real-time by conducting credible hardware-in-the-loop experiments on the QNET Rotary-Pendulum setup. The rotary pendulum is chosen as the benchmark platform owing to its under-actuated configuration and kinematic instability. The experimental outcomes indicate that the latter self-adaptive controller demonstrates superior adaptability and disturbances-rejection capability throughout the operating regime.

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Immune algorithm based active PID control for structure systems

Cited by 6 publications

References 6 publications

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations

Robustification of the state-space MRAC law for under-actuated systems via fuzzy-immunological computations

EOTs Based Fixed Order Controller Design and Performance Analysis

An experimental comparison of different hierarchical self-tuning regulatory control procedures for under-actuated mechatronic systems

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