## 1 Introduction
Adaptive and self-tuning intelligent FLC solutions have also been introduced in various studies [13; 14]. In [15] a genetic neural FLC is designed, where the algorithm requires the reference wheel slip profile. The approach with the estimation of road parameters is used in [16], where various roads can be identified to keep the optimal slip by the controller. This controller demonstrated good performance but its operation was illustrated for simple straight braking maneuvers. Many studies have presented the validation of fuzzy-based ABS algorithms through tests on a hardware-in-the-loop (HIL) experimental setup or ABS test bench connected to the real-time vehicle software simulator. For example, in [17] the authors introduced the fuzzy ABS with the road friction estimation algorithm. Consequently, the experiments were conducted for the variable road conditions proving the FLC robustness. In [18] the ABS algorithm holds the optimal wheel slip for different roads and is validated for quarter-car HIL systems. For complex braking maneuvers on split-\(\upmu\) surfaces and curved roads the activation of the ESP system can be required. In this field, fuzzy logic methods are also finding wide application. For example, patents have been issued to FLC control algorithm, where yaw rate and steering wheel angle signals were considered the control inputs to maintain vehicle stability during braking [19]. Nevertheless, the described controller is still P or PD FLC, which requires the reference input. In addition, many known examples are validated for specific maneuvers only. In particular, the PID FLC for yaw motion control [20; 21] was investigated on double line maneuvers. The FLC neural network [22] shows the experimentation results conducted for split-\(\upmu\) straight road maneuvers. The T-S FLC [23] and fuzzy robust Hoo [24] methods were tested on line change maneuvers. The authors in [25] have integrated yaw moment and active front steering controllers based on the FLC. The results are demonstrated by the single line change maneuvers. It should be noted that the use of simple maneuvers for the controller validation cannot demonstrate the FLC robustness. In reality, the driver deals with different road scenarios. Therefore, the controller applicability has to be studied on more complex and different braking maneuvers such as emergency braking on curved split-\(\upmu\) or variable road surfaces to assure controller robustness. Moreover, the reference slip direct control does not guarantee safety assistance on the split-\(\upmu\) surfaces as the steerability is not preserved. This issue must also be addressed. As suggested in many previous studies, controllers as well as the currently installed systems in the vehicles set the slip value at 20% for any kind of roads. On the one hand, such an approach can be enough to maintain the steering ability. On the other hand, it leads to a decline in braking performance. For example, on average the optimal slip for icy roads is between 7% and 10%. Thus \(\lambda=20\%\) would cause a more than 50% loss of braking performance and the vehicle operation would become unstable, i.e., a diminution in steerability. Therefore, by holding the optimal wheel slip value and avoiding the controller restriction with reference variable, as it is proposed in the current paper, the effectiveness and energy efficiency of the braking process is maintained [1] (pp. 74-94). This paper contributes to the advancement of ABS and brake-based ESP systems using FLC. In particular, the article describes the research results connected with the following topics:  
* ESP and ABS control combination, both designed using fuzzy theory. * Use of a 10 degrees-of-freedom (10 DOF) four-wheel vehicle model in the controller. * Demonstration of the control robustness on different road surfaces and profiles. The paper is organized as follows. The next section is dedicated to vehicle dynamics and model parameterization. Section 3 explains the FLC design. The Section 4 is devoted to the experimental facilities.