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Block diagram of proposed novel tanh-PID controller.

Journal: Biomimetics

Article Title: Intelligent Control of Magnetic Ball Suspension Systems via a Novel Hyperbolic Tangent PID Controller Tuned by the Artificial Lemming Algorithm

doi: 10.3390/biomimetics11030205

Figure Lengend Snippet: Block diagram of proposed novel tanh-PID controller.

Article Snippet: The eight decision variables represent the tunable parameters of the tanh-PID controller: proportional gain K p , integral gain K i , derivative gain K d , filter coefficient N , and four additional shaping parameters τ 1 , G 1 , τ 2 and G 2 that influence the nonlinear dynamics of the control system.

Techniques: Blocking Assay

Block diagram of proposed novel tanh-PID controller.

Journal: Biomimetics

Article Title: Intelligent Control of Magnetic Ball Suspension Systems via a Novel Hyperbolic Tangent PID Controller Tuned by the Artificial Lemming Algorithm

doi: 10.3390/biomimetics11030205

Figure Lengend Snippet: Block diagram of proposed novel tanh-PID controller.

Article Snippet: The eight decision variables represent the tunable parameters of the tanh-PID controller: proportional gain K p , integral gain K i , derivative gain K d , filter coefficient N , and four additional shaping parameters τ 1 , G 1 , τ 2 and G 2 that influence the nonlinear dynamics of the control system.

Techniques: Blocking Assay

Comparative responses of tanh-PID controllers optimized by different algorithms.

Journal: Biomimetics

Article Title: Intelligent Control of Magnetic Ball Suspension Systems via a Novel Hyperbolic Tangent PID Controller Tuned by the Artificial Lemming Algorithm

doi: 10.3390/biomimetics11030205

Figure Lengend Snippet: Comparative responses of tanh-PID controllers optimized by different algorithms.

Article Snippet: The eight decision variables represent the tunable parameters of the tanh-PID controller: proportional gain K p , integral gain K i , derivative gain K d , filter coefficient N , and four additional shaping parameters τ 1 , G 1 , τ 2 and G 2 that influence the nonlinear dynamics of the control system.

Techniques: