Modelling and Development of Linear and Nonlinear Intelligent Controllers for Anti-lock Braking Systems (ABS)

 نظام  منع انغلاق المكابح (ABS)  يستخدم كجزء مهم في المركبات الحديثة لمنع الاطار من الغلق بعد تعشيق المكابح. الاداء العام لنظام سيطرة  منع انغلاق المكابح مستفيدا من كون النظام خطياً او غير خطي موضحاً في هذا البحث. من اجل تصميم نظام السيطرة، تم اشتقاق نموذج ديناميكي لاخطي لمانع الانزلاق استناداً على طبيعه نظامه الفيزيائي. النموذج الديناميكي متكون من عدة معادلات تحكم عمل النظام الميكانيكي.  نظامين سيطرة محتلفين تم استخدامهم للسيطرة على اداء  منع انغلاق المكابح، الاول تم الاستفادة من المسيطر الخطي نوع (PID) مع استخدام تقنية تحويل النظام من اللاخطي الى الخطي حول نقطة معينه للسيطرة على النظام اللاخطي. بينما تم استخدام المسيطر اللاخطي الثاني نوع (discrete time) للسيطرة على النظام الديناميكي اللاخطي بشكل مباشر. هذه الدراسة اعطت معلومات اضافية حول كيفية محاكاة هذين المسيطرين، و اعطت افضلية واضحة للمسيطر التقليدي (PID) على المسيطر نوع (discrete time) في السيطرة و التحكم بنظام منع انغلاق المكابح.Antilock braking systems (ABS) are utilized as a part of advanced autos to keep the vehicle’s wheels from deadlocking when the brakes are connected. The control performance of ABS utilizing linear and nonlinear controls is cleared up in this research. In order to design the control system of ABS a nonlinear dynamic model of the antilock braking systems is derived relying upon its physical system. The dynamic model contains set of equations valid for simulation and control of the mechanical framework. Two different controllers technique is proposed to control the behaviors of ABS. The first one utilized the PID controller with linearized technique around specific point to control the nonlinear system, while the second one used the nonlinear discrete time controller to control the nonlinear mathematical model directly. This investigation contributes to more additional information for the simulation of the two controllers, and demonstrates a clear and reasonable advantage of the classical PID controller on the nonlinear discrete time controller in control the antilock braking system

Modelling of automatic car braking system using fuzzy logic controller

The increasing rate of road accident is alarming and any vehicle without an effective brake system is prone to accident with apparently disastrous effect following. This is due to human errors in driving which involves reaction time delays and distraction. Automatic braking system will be developed to keep the vehicle steerable and stable and also prevent wheel lock and collision with an obstacle. The objectives of this study are to: design an obstacle detection model using ultrasonic sensors, model an antilock braking system, develop fuzzy logic rules for both detection and antilock braking system, and simulate the developed model using Simulink in MATLAB software to achieve high braking torque, optimal slip ratio and shorter stopping distance and time. The results show 22% improvement in braking torque thereby giving a shorter stopping time and distance when compared to the normal PID control.Keywords: Slip ratio, Model, Ultrasonic Sensor, Antilock Braking System, Fuzzy logic, wheel loc

Modelling & development of antilock braking system

Antilock braking systems are used in modern cars to prevent the wheels from locking after brakes are applied. The dynamics of the controller needed for antilock braking system depends on various factors. The vehicle model often is in nonlinear form. Controller needs to provide a controlled torque necessary to maintain optimum value of the wheel slip ratio. The slip ratio is represented in terms of vehicle speed and wheel rotation. In present work first of all system dynamic equations are explained and a slip ratio is expressed in terms of system variables namely vehicle linear velocity and angular velocity of the wheel. By applying a bias braking force system, response is obtained using Simulink models. Using the linear control strategies like P - type, PD - type, PI - type, PID - type the effectiveness of maintaining desired slip ratio is tested. It is always observed that a steady state error of 10% occurring in all the control system models

Passive and active assistive writing devices in suppressing hand tremor

Patients with hand tremor disease frequently experience difficulties in performing their daily tasks, especially in handwriting activities. In order to prevent the ingestion of drugs and intervention of surgeries, a non-invasive solution was presented to improve their writing capabilities. In this study, there were two novel inventions of the hand-held device named as TREMORX and Active Assistive Writing Device (AAWD) with the approaches of passive and active elements respectively. For validation, the patient with tremor was assisted in using a normal pen and TREMORX to perform a handwriting task at the sitting and standing postures. For AAWD, the active suppressing element was the servo motor to control the hand tremor act on the writing tool tip and an accelerometer will measure the necessary parameters values for feedback control signal. The classic Proportional (P) controller and Proportional-Integral- Derivative (PID) were presented. The P controller was tuned with a meta-heuristic method by adjusting the parameters into several values to examine the response and robustness of the controller in suppressing the tremor. The evaluation was based on decreasing the coherence magnitude on the frequency response analysis. To optimise the performances, two types of Evolutionary Algorithms (EA) were employed which were Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The optimisation techniques were integrated into the PID controller system to generate the optimum performances in controlling the tremor. For the simulation study, the parametric model representing the actual system of the AAWD was presented. The main objectives of this analysis were to determine the optimum value of PID parameters based on EA optimisation techniques. The determined parameters for both optimisations were then injected into the experimental environment to test and evaluate the performance of the controllers. The findings of the study exhibited that the PID controller for both EA optimisation provided excellent performances in suppressing the tremor signal act on the AAWD in comparison to the classic pure P controller. Based on the fitness evaluation, the GA optimisation significantly enhanced the PID controller performance compared to PSO optimisation. The handwriting performance using both TRREMORX and AAWD was recorded and from a visual justification, it showed that the quality of legibility was improved as compared with using normal handwriting devices. These outcomes provided an important contribution towards achieving novel methods in suppressing hand tremor by means of the invention of the handheld writing devices incorporated with intelligent control techniques

Optimal abs frenleme kontrolcüsünün geliştirilmesi

Günümüzde taşıtlarda kullanılan ticari ABS sistemleri, frenleme sırasında tekerleklerin dönme hızlarını ölçmekte, tekerleklerin yavaşlama ivmesi belli bir kritik eşik değerini aştığında fren basıncına müdahale ederek tekerleklerin kilitlenme tehlikesi olmadan yavaşlamalarını sağlamaktadır. Bu sistem, kilitlenmeyi önlemekle birlikte tekerleklerin maksimum frenleme potansiyelini tam olarak ortaya çıkaramamaktadır. Frenleme performansını iyileştirecek çeşitli ABS algoritmaları akademik düzeyde geliştirilmeye devam edilmektedir. Bu süreçteki en önemli zorluk, tekerleklerin farklı yol koşullarında farklı karakteristiklerde davranış göstermesidir. Maksimum frenleme performansını sağlayacak bir ABS algoritması yol koşullarını göz önüne almalı fakat yol koşullarının önceden ölçümü ya da kestiriminin zorluğu, bu amacın önünde bir engel olarak durmaktadır. Bu projede, yol koşulları bilgisine ihtiyaç duymadan optimum frenleme performansını sağlayan bir ABS kontrol algoritması geliştirilmiş ve bu algoritma deneysel bir sisteme uygulanmıştır. Geliştirilen algoritmanın temeli literatürde Ekstremum Arama Algoritması (EAA) olarak adlandırılan yöntemdir. Bu algoritma, bir sistemin optimum çalışma noktasının önceden bilinmediği durumlarda kullanılabilmektedir. Örneğin acil durum frenlemesinde ihtiyaç duyulduğu gibi bilinmeyen yol koşullarında tekerlek ile yol arasındaki frenleme kuvvetlerinin maksimize edilmesi problemi EAA’nın uygulama alanına girmektedir. Projede EAA temelli bir ABS kontrol algoritması geliştirilmiştir. Literatüre getirilen bir yenilik olarak, arama algoritmasında kullanılan parametreler uyarlamalı hale getirilerek Uyarlamalı Ekstremum Arama Algoritması tabanlı ABS kontrolcüsü geliştirilmiştir. Bu şekilde algoritmanın hızlı bir şekilde optimum çalışma noktasını bulması, bulduktan sonra ise parametrelerin uyarlanarak optimum nokta etrafında düşük genlikli salınımlar ile frenleme performansının önemli ölçüde iyileştirilmesi hedeflenmiştir. Ayrıca literatürde önerilen ABS kontrol algoritmalarının hemen hemen hepsi sadece bilgisayarda gerçekleştirilen simulasyonlar ile doğrulanmakta, gerçek bir sistemde gerçek zamanlı uygulanabilirlikleri soru işareti olarak kalmaktadır. Bu projede bir ABS deney düzeneği satın alınmış ve algoritmanın gerçek zamanlı testleri gerçekleştirilmiştir.Commercial ABS systems used in vehicles today measure rotational speed of the wheels. When the wheel deceleration value exceeds acertain critical threshold, the ABS intervenes to the brake pressure to slow down the wheel without the danger of locking. While this system prevents locking of the wheels, it does not provide full braking potential of the tires. The most important challenge in this process is that the wheels show different characteristics in different road conditions. For maximum braking performance, an ABS algorithm must take into account current road conditions, but the difficulty of pre-measurement or estimation of road conditions stands as an obstacle on this goal.In this project, an ABS control algorithm was developed and implemented in an experimental setup. The algorithm provides optimal braking performance without any road condition information. The basis of the algorithm is Extremum Seeking Algorithm (ESA). ESA can be used for the situations where the optimum operating point of a system is not known in advance. For example in emergency braking cases, the problem of maximizing the braking force between the wheel and the road in an unknown road condition is an example of the application area of the ESA. In this project, an ESA-based ABS control algorithm was developed. As a novelty to the literature, by making the parameters of the search algorithm adaptive, an Adaptive Extremum Seeking Algorithm based ABS controller was proposed. In this way, the algorithm finds quickly the optimum operating point, and after finding it, by adapting the parameters, braking with low-amplitude oscillations around the optimum operating point is accomplished. In addition, an ABS experimental setup was purchased and real time tests of the algorithm were conducted in this setup.TÜBİTA

Intelligent active force control of human hand tremor using smart actuator

Patients suffering from Parkinson’s disease (PD) experience tremor which may generate a functional disability impacting their daily life activities. In order to provide a non-invasive solution, an active tremor control technique is proposed to suppress a human hand tremor. In this work, a hybrid controller which is a combination of the classic Proportional-Integral (PI) control and Active Force Control (AFC) strategy was employed. A test-rig is utilized as a practical test and verification platform of the controller design. A linear voice coil actuator (LVCA) was utilized as the main active suppressive element to control the tremor of hand model in collocation with the sensor. In order to validate the AFC scheme in real-time application, an accelerometer was used to obtain the measured values of the parameter necessary for the feedback control action. Meanwhile, a laser displacement sensor was used to quantify the displacement signal while hand shaking. To optimize the controller parameters, three different optimization techniques, namely the genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) techniques were incorporated into the hybrid PI+AFC controller to obtain a better performance in controlling tremor of the system. For the simulation study, two different models were introduced to represent the human hand in the form of a mathematical model with four degree-of-freedom (4 DOF) biodynamic response (BR) and a parametric model as the plant model. The main objective of this investigation is to optimize the PI and AFC parameters using three different types of intelligent optimization techniques. Then, the parameters that have been identified were tested through an experimental work to evaluate the performance of controller. The findings of the study demonstrate that the hybrid controller gives excellent performance in reducing the tremor error in comparison to the classic pure PI controller. Based on the fitness evaluation, the AFC-based scheme enhances the PI controller performance roughly around 10% for all optimization techniques. Besides that, an intelligent mechanism known as iterative learning control (ILC) was incorporated into the AFC loop (called as AFCAIL) to find the estimated mass parameter. In addition, a sensitivity analysis was presented to investigate the performance and robustness of the voice coil actuator with the proposed controller in real-time environment. The results prove that the AFCAIL controller gives an excellent performance in reducing the hand tremor error in comparison with the classic P, PI and hybrid PI+AFC controllers. These outcomes provide an important contribution towards achieving novel methods in suppressing hand tremor by means of intelligent control

Control of a single-link flexible manipulator

RESUMEN: En aplicaciones de robótica es común utilizar elementos mecánicos y eslabones rígidos. Esto se realiza así especialmente porque simplifica enormemente el modelado matemático, así como la obtención de controladores dinámicos y cinemáticos. Todo esto conlleva el poder obtener manipuladores que permiten una elevada precisión en el movimiento y en el posicionamiento. Sin embargo, cada día es más frecuente que los robots interaccionen con los operadores humanos en diferentes tareas. Ejemplos de esto pueden encontrarse en las aplicaciones industriales donde los robots colaborativos tienen mucho éxito, pero también en aplicaciones médicas y de servicio a personas discapacitadas, donde un robot puede hacer tareas de atención que conlleven una interacción con la persona. Es en estos campos de interacción con las personas donde un robot que incorpore segmentos mecánicos flexibles, tales que el contacto con las personas sea totalmente inocuo, presenta un futuro de interés (además de las aplicaciones espaciales). En el presente trabajo se analizarán y diseñarán distintos controladores basados en redes neuronales, lógica difusa y control GPI con el objetivo de evaluar su funcionamiento en un sistema que incluya eslabones mecánicos flexibles.ABSTRACT: In robotics applications it is common to use mechanical elements and rigid links. This is done especially because it greatly simplifies mathematical modeling, as well as obtaining dynamic and kinematic controllers. All this leads to manipulators that allow high precision in movement and positioning. However, it is becoming increasingly common for robots to interact with human operators in different tasks. Examples of this can be found in industrial applications where collaborative robots are very successful, but also in medical and service applications for disabled people, where a robot can perform care tasks that involve interaction with the person. It is in these fields of interaction with people that a robot incorporating flexible mechanical segments, such that contact with people is completely harmless, presents a future of interest (in addition to space applications). In this work, different controllers based on neural networks, fuzzy logic and GPI control will be analyzed and designed in order to evaluate their performance in a system including flexible mechanical links.Grado en Ingeniería en Electrónica Industrial y Automátic

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

Real-time multi-domain optimization controller for multi-motor electric vehicles using automotive-suitable methods and heterogeneous embedded platforms

Los capítulos 2,3 y 7 están sujetos a confidencialidad por el autor. 145 p.In this Thesis, an elaborate control solution combining Machine Learning and Soft Computing techniques has been developed, targeting a chal lenging vehicle dynamics application aiming to optimize the torque distribution across the wheels with four independent electric motors.The technological context that has motivated this research brings together potential -and challenges- from multiple dom ains: new automotive powertrain topologies with increased degrees of freedom and controllability, which can be approached with innovative Machine Learning algorithm concepts, being implementable by exploiting the computational capacity of modern heterogeneous embedded platforms and automated toolchains. The complex relations among these three domains that enable the potential for great enhancements, do contrast with the fourth domain in this context: challenging constraints brought by industrial aspects and safe ty regulations. The innovative control architecture that has been conce ived combines Neural Networks as Virtual Sensor for unmeasurable forces , with a multi-objective optimization function driven by Fuzzy Logic , which defines priorities basing on the real -time driving situation. The fundamental principle is to enhance vehicle dynamics by implementing a Torque Vectoring controller that prevents wheel slip using the inputs provided by the Neural Network. Complementary optimization objectives are effici ency, thermal stress and smoothness. Safety -critical concerns are addressed through architectural and functional measures.Two main phases can be identified across the activities and milestones achieved in this work. In a first phase, a baseline Torque Vectoring controller was implemented on an embedded platform and -benefiting from a seamless transition using Hardware-in -the -Loop - it was integrated into a real Motor -in -Wheel vehicle for race track tests. Having validated the concept, framework, methodology and models, a second simulation-based phase proceeds to develop the more sophisticated controller, targeting a more capable vehicle, leading to the final solution of this work. Besides, this concept was further evolved to support a joint research work which lead to outstanding FPGA and GPU based embedded implementations of Neural Networks. Ultimately, the different building blocks that compose this work have shown results that have met or exceeded the expectations, both on technical and conceptual level. The highly non-linear multi-variable (and multi-objective) control problem was tackled. Neural Network estimations are accurate, performance metrics in general -and vehicle dynamics and efficiency in particular- are clearly improved, Fuzzy Logic and optimization behave as expected, and efficient embedded implementation is shown to be viable. Consequently, the proposed control concept -and the surrounding solutions and enablers- have proven their qualities in what respects to functionality, performance, implementability and industry suitability.The most relevant contributions to be highlighted are firstly each of the algorithms and functions that are implemented in the controller solutions and , ultimately, the whole control concept itself with the architectural approaches it involves. Besides multiple enablers which are exploitable for future work have been provided, as well as an illustrative insight into the intricacies of a vivid technological context, showcasing how they can be harmonized. Furthermore, multiple international activities in both academic and professional contexts -which have provided enrichment as well as acknowledgement, for this work-, have led to several publications, two high-impact journal papers and collateral work products of diverse nature

The potentional application of artificial intelligence in motor vehicles braking system performance

Osnovni zahtevi koji se postavljaju pred današnje kočne sisteme motornih i priključnih vozila, u pogledu bezbednosti vozila i saobraćaja, se odnose na njihovo dalje unapređenje kroz razvoj novih, inteligentnih, rešenja. Suština ovih zahteva jeste da se omogući pomoć vozaču kroz inteligentno upravljanje sistemima na vozilu, odnosno njihovim performansama u različitim, dinamički promenljivim, radnim uslovima. Pošto kočne performanse vozila zavise od performansi kočnica, koje funkcionišu na principima trenja i samim tim imaju vrlo nepredvidiv karakter, i od usklađenosti tih performansi sa trenutnim uslovima prijanjanja u kontaktu pneumatika sa tlom, koji se mogu intenzivno menjati tokom samo jednog ciklusa kočenja, realizacija ovih zahteva je izuzetno kompleksna. To je osnovni razlog za sprovođenje istraživanja u pogledu razvoja i implementacije inteligentnijih načina upravljanja performansama kočnog sistema na osnovu uslova prijanjanja u kontaktu pneumatik–tlo. U ovoj doktorskoj disertaciji su istraživane mogućnosti primene tehnika iz oblasti veštačke inteligencije u cilju modeliranja složenih dinamičkih uticaja radnih režima kočnica motornih vozila i uslova u kontaktu pneumatik–tlo, kao i predviđanja ovih uticaja u cilju upravljanja performansama kočnica, a time i performansama kočnog sistema, u toku ciklusa kočenja. Zbog nemogućnosti modeliranja složenih dinamičkih uticaja radnih režima kočnica motornih vozila na njihove izlazne performanse, odnosno na vrednosti klizanja u kontaktu pneumatika i puta pomoću klasičnih matematičkih metoda, uvedena je nova inteligentna metoda bazirana na dinamičkim veštačkim neuronskim mrežama i fazi logici. U skladu sa time, u ovoj disertaciji su istraživane mogućnosti primene dinamičkih veštačkih neuronskih mreža i fazi logike u cilju modeliranja, predviđanja i inteligentnog upravljanja performansama kočnica, odnosno performansama kočnog sistema. Predmetno istraživanje je usmereno ka razvoju sposobnosti kočnog sistema ka inteligentnom prilagođavanju sile kočenja dinamičkim promenama podužnog klizanja točka (pneumatika) u kontaktu sa putem u toku ciklusa kočenja. Ovakav koncept upravljanja performansama kočnog sistema, na osnovu prethodnih i trenutnih vrednosti posmatranih uticajnih veličina i identifikovanih uslova prijanjanja tokom kočenja, podrazumeva predviđanje potrebne vrednosti pritisaka aktiviranja kočnica, na prednjoj i zadnjoj osovini, za date uslove kočenja (vrednosti pritiska aktiviranja kočnice, vrednosti brzine točka na prednjoj/zadnjoj osovini, temperature u kontaktu frikcionog para kočnice na prednjoj/zadnjoj osovini i vrednosti klizanja u kontaktu pneumatik–tlo) kako bi se u kontaktu pneumatika i tla postiglo željeno (optimalno) klizanje u podužnom pravcu.In terms of vehicle and traffic safety, the main demands imposed to the braking systems of motor vehicles and trailers are related to their further improvement through development of new, intelligent, solutions. It could enable the driver assistance function through an intelligent control of the vehicle systems performance in different and dynamically changing operating conditions. Since the braking performance of vehicles depend on the performance of the brakes, which based their function on the friction, it is a difficult to control stochastically changed the brakes performance. Furthermore, harmonization of that performance with the actual conditions in the tire-road contact, which is also intensively changed during a braking cycle, the realization of demands towards an intelligent control the braking system performance is very complex. This is the main reason for conducting research regarding development and implementation of more intelligent ways for control of the braking system performance. In this doctoral thesis, possibilities for employing of an artificial intelligence have been investigated in order to model and predict the impact of the brakes operating regimes and the complex conditions in the tire-road contact in order to provide intelligent controlling of the braking system performance during a braking cycle. Due to the impossibility for modeling of complex dynamic influences of brakes’ operating conditions on their performance and consequently on the value of the longitudinal wheel slip using conventional mathematical methods, a new method has been introduced based on an integration of dynamic neural networks and fuzzy logic. Accordingly, this thesis investigated possibilities for the proper integration of dynamic artificial neural networks and fuzzy logic in modeling, prediction, and intelligent control of the brakes’ performance, i.e. performance of the braking system. It should provide inherent capabilities of the braking system towards an intelligent adaptation of the braking forces to the dynamic changes of the longitudinal slip ratio in the tire–road contact during a braking cycle. This concept for control of the braking system performance, based on previous and current values of observed influential factors, means predicting of the brake applied pressure values, on the front and rear axle, for the given braking conditions (brake applied pressure, wheel speed on the front/rear axle, brake interface temperature on the front/rear axle, and wheel slip) in order to achieve the desired and/or optimal slip level in the longitudinal direction. Furthermore, the braking system should continuously learn about the complex and stochastic influences between these factors during a braking cycle. Since this is especially important for commercial vehicles, the focus of research has been directed on possibilities for improving the performance of electronically controlled braking system. It is done not only to achieve the optimal value of the longitudinal wheel slip in the tire-road contact, but also enables later optimization of the lateral wheel slip