Identification and control design for a class of non-minimum phase dead-time systems based on fractional-order Smith predictor and genetic algorithm technique

2019 ◽  
Vol 7 (3) ◽  
pp. 914-925 ◽  
Author(s):  
Hocine Bouyedda ◽  
Samir Ladaci ◽  
Moussa Sedraoui ◽  
Mohamed Lashab
Keyword(s):  
Genetic Algorithm ◽  
Fractional Order ◽  
Dead Time ◽  
Control Design ◽  
Smith Predictor ◽  
Minimum Phase ◽  
And Control ◽  
Time Systems

scholarly journals Fractional Order PIλDµ Control Design for a Class of Cyber-Physical Systems with Fractional Order Time-Delay models

2019 ◽  
Vol 1 (2) ◽  
pp. 1-18
Author(s):  
Marwa Boudana ◽  
Samir Ladaci ◽  
Jean-Jacques Loiseau
Keyword(s):  
Time Delay ◽  
Fractional Order ◽  
Control Design ◽  
Cyber Physical Systems ◽  
Performance Criteria ◽  
Delay Systems ◽  
Time Delay Systems ◽  
Physical Systems ◽  
System Configurations ◽  
And Control

The control of cyber-physical systems (CPS) is a great challenge for researchers in control theory and engineering mainly because of delays induced by merging computation, communication, and control of physical processes. Consequently, control solutions for time-delay systems can be applied efficiently for many CPS system configurations. In this article, a fractional order PIλ and PIλDµ control design is investigated for a class of fractional order time-delay systems. The proposed control design approach is simple and efficient. The controller parameter's adjustment is achieved in two steps: first, the relay approach is used to compute satisfactory classical PID coefficients, namely kp, Ti and Td. Then, the fractional orders λ and µ are optimized using performance criteria. Simulation results show the efficiency of the proposed design technique and its ability to enhance the PID control performance.

GA BASED SENSOR PLACEMENT AND CONTROL DESIGN FOR REACTIVE NAVIGATION OF MOBILE ROBOTS

2005 ◽  
Vol 02 (02) ◽  
pp. 77-91 ◽  
Author(s):  
XIAOCHUAN WANG ◽  
SIMON X. YANG ◽  
MAX Q.-H. MENG
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robots ◽  
Control Structure ◽  
Controller Design ◽  
Sensor Placement ◽  
Control Design ◽  
Simulation Studies ◽  
Optimal Sensor Placement ◽  
Reactive Navigation ◽  
And Control

In this paper, a novel genetic algorithm based approach is proposed for optimal sensor placement and controller design of a mobile robot to facilitate its reactive navigation and obstacle avoidance in unknown environments. The mobile robots considered in this paper have flexible sensor and control structure. A genetic algorithm is developed to evolve the parameters of optimal sensor placement and controller design simultaneously. The effectiveness of the proposed GA based co-evolution approach to robot sensor placement and control design is demonstrated by simulation studies.

Unified Smith predictor for dead-time systems 1

2003 ◽  
Vol 36 (19) ◽  
pp. 83-86
Author(s):  
Qing-Chang Zhong
Keyword(s):  
Dead Time ◽  
Smith Predictor ◽  
Time Systems

scholarly journals Analysis and Control Design for a Class of Fractional Order Time-Delay Systems

2020 ◽  
Vol 5 (1) ◽  
pp. 18-24
Author(s):  
Marwa BOUDANA ◽  
Samir LADACI ◽  
Jean Jacques LOISEAU
Keyword(s):  
Time Delay ◽  
Fractional Order ◽  
Pid Control ◽  
Control Design ◽  
Performance Criteria ◽  
Delay Systems ◽  
Time Delay Systems ◽  
Control Performance ◽  
And Control ◽  
Fractional Orders

In this paper, we consider a class of fractional order time-delay systems and propose a fractional order control design for their stabilization. The controller parameter’s adjustment is achieved in two steps: first, the relay approach is used to compute satisfactory classical PID coefficients, namely kp, Ti and Td. Then, the fractional orders ʎ and µ are optimized using performance criteria. Simulation results show the efficiency of the proposed design technique and its ability to enhance the PID control performance.

Structure-specified IIR filter and control design using real structured genetic algorithm

2009 ◽  
Vol 9 (4) ◽  
pp. 1285-1295 ◽  
Author(s):  
Chih-Wei Tsai ◽  
Ching-Huei Huang ◽  
Chun-Liang Lin
Keyword(s):  
Genetic Algorithm ◽  
Control Design ◽  
Iir Filter ◽  
And Control
Sign in / Sign up

Export Citation Format

Share Document