## 4 Case Studies
### Case Study: Composite Intelligent Learning Control of Strict-Feedback Systems with Disturbance  
This case study focuses on a paper entitled \"Composite Intelligent Learning Control of Strict-Feedback Systems With Disturbance\" by [PERSON] and [PERSON], published in the _IEEE Transactions on Cybernetics_ in 2018. The paper presents an innovative approach to the dynamic surface control of uncertain nonlinear systems. The researchers address the dynamic surface control of uncertain nonlinear systems using composite intelligent learning and a disturbance observer. This approach is particularly relevant in the presence of unknown system nonlinearity and time-varying disturbance. The researchers constructed a serial-parallel estimation model that incorporates intelligent approximation and disturbance estimation. This model was used to obtain the prediction error, which in turn facilitated the construction of the composite law for updating weights. A nonlinear disturbance observer was developed using intelligent approximation information. The disturbance estimation was guaranteed to converge to a bounded compact set. One of the highlights of this study is the inclusion of the transparency of the intelligent approximation and disturbance estimation in the control scheme. This is a departure from previous work, which was directly aimed at asymptotic stability. The researchers analyzed the uniformly ultimate boundedness stability using the Lyapunov method. Simulation verification demonstrated that composite intelligent learning with a disturbance observer could efficiently estimate the effect caused by system nonlinearity and disturbance. The proposed approach achieved better performance with higher accuracy. The paper concludes that composite intelligent learning control using a disturbance observer is an effective approach for uncertain strict-feedback systems with time-varying disturbances. The novel weight updating method is designed by constructing a prediction error, while disturbance estimation is constructed based on the intelligent approximation. The closed-loop stability is rigorously established. The simulation of third-order nonlinear dynamics demonstrated that the disturbance observer-composite fuzzy learning (DOB-CFL) could closely estimate the effect of unknown system nonlinearity and time-varying disturbance, achieving higher tracking accuracy. For future work, the researchers expressed interest in constructing more efficient composite learning algorithms. They also suggested that the design presented in this paper could be extended to other dynamics. One important topic for future exploration is how the method could work in robot dynamics and flight dynamics. Case Study: Improved LVS Guidance and Path-Following Control for Unmanned Sailboat Robot with the Minimum Triggered Setting  
This case study focuses on a paper entitled \"Improved LVS Guidance and Path-Following Control for Unmanned Sailboat Robot with the Minimum Triggered Setting\" by [PERSON], [PERSON], [PERSON], and [PERSON]. The paper presents a dynamic event-triggered control algorithm for an unmanned sailboat robot (USR) to carry out waypoint-based path-following missions. The researchers proposed a scheme that includes a guidance module and a control module. The guidance module uses an improved logic virtual ship (LVS) guidance law to plan the reference route for the USR, taking into account the marine environment with time-varying wind direction. The navigation zones for the USR are divided into crosswindand non-crosswind zones, which helps the sailboats achieve tracking and gybe in different navigation zones. The control part of the scheme uses dynamic event-triggered control proposed by setting a tuning threshold parameter. This approach helps decrease resource waste and channel occupancy. Compared to the existing event-triggered control with a static threshold parameter, the proposed algorithm performs better in data transmission. The closed-loop control system has been proven to have semi-global uniform ultimate bounded (SGUUB) stability.