Adaptive-neural-network-based robust lateral motion control for autonomous vehicle at driving limits

Collision avoidance and stabilisation are two of the most crucial concerns when an autonomous vehicle finds itself in emergency situations, which usually occur in a short time horizon and require l...

Emergency steering control of autonomous vehicle for collision avoidance and stabilisation

A review on control system architecture of a SI engine management system

Recent Advancement in Machine Learning Based Internet Traffic Classification

Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness.  In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.

Design Sliding Mode Controller of with Parallel Fuzzy Inference System Compensator to Control of Robot Manipulator

This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm. VSC methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable fuel ratio result and adjust. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the rate of error. The outputs represent fuel ratio, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC which controller coefficient is on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC and proposed method. Simulation results signify good performance of trajectory in presence of uncertainty torque load.  DOI: http://dx.doi.org/10.11591/ijece.v3i2.2092

http://ijece.iaescore.com/index.php/IJECE/article/download/5473/5013

Artificial Tune of Fuel Ratio: Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

Both fuzzy logic and computed fuel ratio can compensate the steady-state error of proportional- derivative (PD) method. This paper presents parallel computed fuel ratio compensation for fuzzy plus PID control management with application to internal combustion (IC) engine. The asymptotic stability of fuzzy plus PID control methodology with first-order computed fuel ratio estimation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding- mode is guaranteed.

/pdf/robust-fuzzy-pd-method-with-parallel-computed-fuel-ratio-ytcej0hrjl.pdf

Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine

Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain ( ) and linear inner loop gain ( ). Since the sliding surface gain ( ) and linear inner loop gain ( ) are adjusted by linear error-based tuning method. In this research new and new are obtained by the previous and multiple gains updating factor . The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.

/pdf/evaluation-performance-of-ic-engine-linear-tunable-gain-1oqbc0u8ge.pdf

Evaluation Performance of IC Engine: Linear Tunable Gain Computed Torque Controller vs. Sliding Mode Controller

Internal combustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. Closed loop combustion control is a key technology that is used to optimize the engine combustion process to achieve this goal. In order to conduct research in the area of closed loop combustion control, a control oriented cycle-to-cycle engine model, containing engine combustion information for each individual engine cycle as a function of engine crank angle, is a necessity. In this research, the IC engine is modeled according to fuel ratio, which is represented by the mass of air. In this research, a multi-input-multi- output baseline computed fuel control scheme is used to simultaneously control the mass flow rate of both port fuel injection (PFI) and direct injection (DI) systems to regulate the fuel ratio of PFI to DI to desired levels. The control target is to maintain the fuel ratio at stoichiometry and the fuel ratio to a desired value between zero and one. The performance of the baseline computed fuel controller is compared with that of a baseline proportional, integral, and derivative (PID) controller.

/pdf/supervised-optimization-of-fuel-ratio-in-ic-engine-based-on-zagquwancc.pdf

Supervised Optimization of Fuel Ratio in IC Engine Based on Design Baseline Computed Fuel Methodology

In this research, model reference fuzzy based control is presented as robust controls for IC engine. The objective of the study is to design controls for IC engines without the knowledge of the boundary of uncertainties and dynamic information by using fuzzy model reference PD p lus mass of air while improve the robustness of the PD p lus mass of air control. A PD plus mass of air provides for eliminate the mass of air and ultimate accuracy in the presence of the bounded disturbance/uncertainties, although this methods also causes some oscillation. The fuzzy PD plus mass of air is proposed as a solution to the problems crated by unstability. Th is method has a good performance in presence of uncertainty.

/pdf/design-novel-model-reference-artificial-intelligence-based-1vl6m64c32.pdf

Saeed Zare

Papers

Artificial Tune of Fuel Ratio: Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine

Evaluation Performance of IC Engine: Linear Tunable Gain Computed Torque Controller vs. Sliding Mode Controller

Supervised Optimization of Fuel Ratio in IC Engine Based on Design Baseline Computed Fuel Methodology

Design Novel Model Reference Artificial Intelligence Based Methodology to Optimized Fuel Ratio in IC Engine