Imitative learning based emotional controller for unknown systems with unstable equilibrium.

*(English)*Zbl 1197.93032Summary: Intelligent control for unidentified systems with unstable equilibriums is not always a proper control strategy, which results in inferior performance in many cases. Because of the existing trial and error manner of the procedure in former duration of learning, this exploration for finding the appropriate control signals can lead to instability. However, the recent proposed emotional controllers are capable of learning swiftly; the use of these controllers is not an efficient solution for the mentioned instability problems. Therefore, a solution is needed to evade the instability in the preliminary phase of learning. The purpose of this paper is to propose a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers.

An existing controller (model-based controller) with limited performance is used as a mentor for the emotional learning controller in the first step. This learning phase prepares the controller to control the plant as well as mentor, while it prevents any instability. When the emotional controller can imitate the behavior of model based one properly, the employed controller is gently switched from model based one to an emotional controller using a Fuzzy Inference System (FIS). Also, the emotional stress is softly switched from the mentor-imitator output difference to the combination of the objectives. In this paper, the emotional stresses are generated once by using a nonlinear combination of objectives and once by employing different stresses to a FIS modulating the stresses, and makes a subset of these objectives salient regarding the contemporary situation.

The proposed model free controller is employed to control an inverted pendulum system and an oscillator with unstable equilibrium. It is noticeable that the proposed controller is a model free one, and does not use any knowledge about the plant. The experimental results on two benchmarks show the superiority of proposed imitative and emotional controller with fuzzy stress generation mechanism in comparison with model based originally supplied controllers and emotional controller with nonlinear stress generation unit – in control of pendulum system – in all operating conditions.

There are two test beds for evaluating the proposed model free controller performance which are discussed in this paper: a laboratorial inverted pendulum system, which is a well-known system with unstable equilibrium, and Chua’s circuit, which is an oscillator with two stable and one unstable equilibrium point. The results show that the proposed controller with the mentioned strategy can control the systems with satisfactory performance.

In this paper, a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers is proposed. This approach is based on imitative learning in preliminary phase of learning and soft switching to an interactive emotional learning. Moreover, FISs are used to model the linguistic knowledge of the ascendancy and situated importance of the objectives. These FISs are used to attentionally modulate the stress signals for the emotional controller. The results of proposed strategy on two benchmarks reveal the efficacy of this strategy of model free control.

An existing controller (model-based controller) with limited performance is used as a mentor for the emotional learning controller in the first step. This learning phase prepares the controller to control the plant as well as mentor, while it prevents any instability. When the emotional controller can imitate the behavior of model based one properly, the employed controller is gently switched from model based one to an emotional controller using a Fuzzy Inference System (FIS). Also, the emotional stress is softly switched from the mentor-imitator output difference to the combination of the objectives. In this paper, the emotional stresses are generated once by using a nonlinear combination of objectives and once by employing different stresses to a FIS modulating the stresses, and makes a subset of these objectives salient regarding the contemporary situation.

The proposed model free controller is employed to control an inverted pendulum system and an oscillator with unstable equilibrium. It is noticeable that the proposed controller is a model free one, and does not use any knowledge about the plant. The experimental results on two benchmarks show the superiority of proposed imitative and emotional controller with fuzzy stress generation mechanism in comparison with model based originally supplied controllers and emotional controller with nonlinear stress generation unit – in control of pendulum system – in all operating conditions.

There are two test beds for evaluating the proposed model free controller performance which are discussed in this paper: a laboratorial inverted pendulum system, which is a well-known system with unstable equilibrium, and Chua’s circuit, which is an oscillator with two stable and one unstable equilibrium point. The results show that the proposed controller with the mentioned strategy can control the systems with satisfactory performance.

In this paper, a novel approach for controlling unstable systems or systems with unstable equilibrium by model free controllers is proposed. This approach is based on imitative learning in preliminary phase of learning and soft switching to an interactive emotional learning. Moreover, FISs are used to model the linguistic knowledge of the ascendancy and situated importance of the objectives. These FISs are used to attentionally modulate the stress signals for the emotional controller. The results of proposed strategy on two benchmarks reveal the efficacy of this strategy of model free control.

##### MSC:

93A10 | General systems |

68T05 | Learning and adaptive systems in artificial intelligence |

93C42 | Fuzzy control/observation systems |

70Q05 | Control of mechanical systems |

##### Software:

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\textit{M. J. Roshtkhari} et al., Int. J. Intell. Comput. Cybern. 3, No. 2, 334--359 (2010; Zbl 1197.93032)

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##### References:

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