The genetic algorithm approach is widely recognized as an effective and flexible optimization method for system identification. The flexibility of a genetic algorithm allows various strategies to be applied to it. One of the strategies applied is the modified genetic algorithm which relies on, among other things, the separation of the population into groups where each group undergoes mutual recombination operations. The strategy has been shown to be better than the simple genetic algorithm and conventional statistical method, but it contains inadequate justification of how the separation is made. The usage of objective function values for separation of groups does not carry much flexibility and is not suitable since different time-dependent data have different levels of equilibrium and thus different ranges of objective function values. This paper investigates the optimum grouping of chromosomes by fixed group ratios, enabling more efficient identification of dynamic systems using a NARX (Non-linear AutoRegressive with eXogenous input) model. Several simulated systems and real-world timedependent data are used in the investigation. Comparisons based on widely used optimization performance indicators along with outcomes from other research are used. The issue of model parsimony is also addressed, and the model is validated using correlation tests. The study reveals that, when recombination and mutation are used for different groups, equal composition of both groups produces a better result in terms of accuracy, parsimony, speed, and consistency.
Field test approach of steer-by-wire (SBW) technologies by using actual vehicle can be very dangerous. This is due to the fact that stability of the vehicle is very sensitive to the steering wheel input. Less optimum parameters of the steering controllers or system failure may lead to dangerous road accident. In this study, hardware-in-the-loop simulation (HiLS) is used to bridge the gap between simulation and experimentation of SBW system. In the proposed HiLS system, SBW test rig is set up to communicate in real time with 14 degree-of-freedom vehicle model. Proportional-integral-derivative control optimized with Ziegler-Nichols method is used to control the stepper motor of the SBW test rig. From simulation and experimental results, SBW system developed has the ability to closely follow the steering trajectory of the conventional steering system with acceptable errors. By using HiLS, both controller algorithm and the functionality of the steering actuator of SBW system can be tested in a semireal driving condition as preliminary testing.
<span lang="EN-US">System identification (SI) is a method of determining a mathematical model for a system given a set of input-output data. A representation is made using a mathematical model based on certain specified assumptions. In SI, model structure selection is a step where a model structure perceived as an adequate system representation is selected. A typical rule is that the final model must have a good balance between parsimony and accuracy. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the effectiveness of GA operators. This paper presents a mating technique named single parent mating (SPM) in GA for use in a real robotic SI. This technique is based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that eases the search for the optimal model. The results show that using three different objective functions (Akaike information criterion, Bayesian information criterion and parameter magnitude–based information criterion 2) respectively, GA with the mating technique is able to find more optimal models than without the mating technique. Validations show that the selected models using the mating technique are acceptable.</span>
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