# Advancements in Semi-Active Automotive Suspension Systems with Magnetroheological Dampers: A Review
## 4 Semi-Active Control of Suspension Systems
### Intelligent Control Strategies
#### 4.3.1 Fuzzy Control  
In the field of intelligent control, fuzzy control stands out for its strong capability in managing nonlinear systems. For systems characterized by strong nonlinearity and uncertainty, such as vehicle semi-active suspension systems employing MRDs as actuators, fuzzy control emerges as an exceptionally suitable control algorithm. As early as 1965, the American scholar [PERSON] pioneered fuzzy set theory [81]. Grounded in this theoretical framework, fuzzy control theory articulates control and state variables in fuzzy language. By translating engineering expertise and expert knowledge into fuzzy rules, it establishes mappings between control variables and state variables, thus effectively controlling the system [82, 83]. Fuzzy controllers do not necessitate exhaustive knowledge of all state variables; instead, they adeptly regulate highly nonlinear systems with minimal sensors based on control logic.  
[PERSON] et al. employed fuzzy logic in the control of semi-active suspension, with vehicle body velocity and suspension velocity as inputs, and damping force as the output [18]. The study indicated that semi-active suspension systems based on fuzzy control exhibited markedly superior comfort performance compared to passive suspensions. [PERSON] and [PERSON] developed a self-tuning fuzzy controller for active suspension systems [84]. [PERSON] et al. proposed a fuzzy skyhook damping controller based on the quarter-vehicle model [85]. This controller optimally adjusted the characteristics of the skyhook damping suspension in response to changing road conditions. [PERSON] utilized a fuzzy controller for systematic vibration control in an eight-degrees-of-freedom nonlinear vehicle model [86]. [PERSON] et al. utilized fuzzy control to optimize the hyperparameters of the PID controller [87]. This approach effectively mitigated the challenge of real-time PID parameter adjustment. [PERSON] et al. conducted comparative studies on passive suspension, PID-controlled semi-active suspension, and fuzzy PID-controlled semi-active suspension [88]. The research findings indicated that the control effectiveness of fuzzy PID was significantly superior to the former two methods. [PERSON] and [PERSON] developed a fuzzy controller for a vehicle suspension system, focusing on damping force as the controlled variable [89]. Notably, this controller operated effectively without the need for an inverse model of the MRD. [PERSON] et al. introduced a Takagi-Sugeno fuzzy controller based on a state observer. Simulation studies demonstrated the controller's proficient management of suspension performance [90]. [PERSON] et al. designed an adaptive fuzzy inference system based on an MR semi-active suspension model to formulate a non-parametric damper model [91]. Additionally, they developed a Takagi-Sugeno fuzzy controller. [PERSON] et al. proposed an optimal fuzzy control approach that optimized the control law using genetic algorithms [92]. They validated the control algorithm through simulation based on a three-dimensional nonlinear dynamics model. [PERSON] et al. proposed a fuzzy sliding mode control (FSMC) strategy for a semi-active air suspension system based on MRDs (shown in Figure 14), investigating the application of fuzzy control to adjust the sliding mode control boundary layer [93].  
Experimental results indicated that the semi-active air suspension with FSMC exhibited improved damping performance and ride comfort.