Speed Gradient Method and Its Applications

Andrievsky, Boris; Fradkov, Alexander L.

doi:10.1134/s0005117921090010

Cited by 23 publications

(12 citation statements)

References 152 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, drivers mainly focus on three factors: driving safety, travel efficiency, and ride comfort in the vehicle movement process [29]. Andrievsky et al [30] and Zu et al [31] apply a curving line to describe the relationship between driving safety and velocity gradient, and they support that driving safety is enhanced when the velocity gradient is decreased. Jung et al [17] and Constantinescu et al [32] construct a risky driving model to monitor driving risk based on detecting the distance between ego-vehicle and peripheral vehicles.…”

Section: Local Fsm Modelmentioning

confidence: 99%

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Fu¹,

Fu²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

This paper is to explore the problems of intelligent connected vehicles (ICVs) autonomous driving decision-making under a 5G-V2X structured road environment. Through literature review and interviews with autonomous driving practitioners, this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system. Secondly, situated on this framework, it builds a hierarchical finite state machine (HFSM) model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method (EWM) and analytic hierarchy process method (AHP) and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA). The HFSM model is composed of two layers: the global FSM model and the local FSM model. The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module, meanwhile pulsating feedback to the former as real-time refresh data. To identify different traffic scenarios in a cerebrum-like way, the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events, and the local FSM model is designed as 16 states and 8 characteristic events. In respect to designing a cerebrum-like algorithm for state transition, this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event; then, it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition. To verify the feasibility, reliability, and safety of the HFSM model as well as its TOPSIS-GRA state transition algorithm, this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform. The results display that the accuracy of obstacle detection gets 98%, lane line prediction is beyond 70 m, the speed of collision avoidance is higher than 45 km/h, the distance of collision avoidance is less than 5 m, path planning time for obstacle avoidance is averagely less than 50 ms, and brake deceleration is controlled under 6 m/s 2 . These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may 1052 CMC, 2023, vol.75, no.1 bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

show abstract

Section: Local Fsm Modelmentioning

confidence: 99%

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Fu¹,

Fu²

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…When a system's explicit model is not available, and control strategies must be learnt by trial and error, RL techniques are appropriate. A computational paradigm called fuzzy logic handles information that is ambiguous or imprecise (Andrievsky, 2021). Fuzzy logic controllers (FLCs) make choices and produce control signals by using language variables and rules.…”

Section: Overview Of Artificial Intelligence In Control Systemsmentioning

confidence: 99%

“…This integration enhances the performance and adaptability of the control system. In real-time settings, when control actions must be planned and carried out in a short amount of time, AI approaches are used to control systems (Andrievsky, 2021). Effective algorithms, hardware implementation considerations, and interaction with sensors for data collecting and feedback are all necessary for real-time control.…”

Section: Overview Of Artificial Intelligence In Control Systemsmentioning

confidence: 99%

Artificial Intelligence (AI) Techniques for Intelligent Control Systems in Mechanical Engineering

Dosari,

Abouellail

2023

Am. J. Smart Technol. Sol.

View full text Add to dashboard Cite

In mechanical engineering, control systems are essential to the dependable and effective operation of mechanical equipment and processes. The advantages of integrating AI into control systems include energy savings, greater product quality, increased process effectiveness, and adaptive and predictive control. Various AI approaches, including machine learning methods like decision trees, neural networks, and support vector machines, are extensively studied for system identification, modelling, and adaptive control. AI techniques enhance these applications' control performance, energy efficiency, problem discovery, and maintenance. The methodology uses metaheuristic techniques for creating intelligent control systems, such as simulated annealing, ant colony optimisation, and harmony search. With the help of these algorithms, the solution space is efficiently searched for ideal control strategies while avoiding local optima. The importance of evaluating the learned control algorithm on a different dataset or running experiments to determine its performance is also underlined. Performance assessment benchmarks for tracking error, settling time, overshoot, and energy efficiency are provided in the results section. This paper investigates the use of AI techniques in mechanical engineering intelligent control systems. The paper also offers areas for additional research in intelligent control systems and identifies research directions for future advancements.

show abstract

“…To obtain the adaptive tuning law for control law (23), the adjustable parameters m, kx , ky , kh are employed with the Speed-gradient (SG) method, cf. [52][53][54][55].…”

Section: Synthesis Of Uav Adaptation Lawmentioning

confidence: 99%

“…To prove this, we can check the conditions under which the SG scheme is operable [52][53][54][55] Function Qt should be convex on adjustable parameters m, kx , ky , kh . This condition is met because Qt is linear with respect to the adjustable parameters.…”

Section: Proof Of Propositionmentioning

confidence: 99%

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

et al. 2022

View full text Add to dashboard Cite

The paper is devoted to the problem of the decentralized control of unmanned aerial vehicle (UAV) formation in the case of parametric uncertainty. A new version of the feedback linearization approach is proposed and used for a point mass UAV model transformation. As result, a linear model is obtained containing an unknown value of the UAV mass. Employing the speed-gradient design method and the implicit reference model concept, a combined adaptive control law is proposed for a single UAV, including the UAV’s mass estimation and adaptive tuning of the controller parameters. The obtained new algorithms are then used to address the problem of consensus-based decentralized control of the UAV formation. Rigorous stability conditions for control and identification are derived, and simulation results are presented to demonstrate the quality of the closed-loop control system for various conditions.

show abstract

Speed Gradient Method and Its Applications

Cited by 23 publications

References 152 publications

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Artificial Intelligence (AI) Techniques for Intelligent Control Systems in Mechanical Engineering

Speed-Gradient Adaptive Control for Parametrically Uncertain UAVs in Formation

Contact Info

Product

Resources

About