Design for LQR Controller of Active Suspension Based on Chaos Quantum-behaved Particle Swarm Optimization Algorithm
Author(s):
Y. X. Luo & N. V. Boisyev
Source:
Y. X. Luo & N. V. Boisyev, “Design for LQR Controller of Active Suspension Based on Chaos Quantum-behaved Particle Swarm Optimization Algorithm”, Journal of Mechanical Engineering Research and Developments, vol. 40, no. 1, pp. 127-133, 2017.
ABSTRACT: Active suspension represents the major development direction of automotive suspension in the future. When we design the LQR controller of active suspension, it is the key to select the weighting coefficient of various evaluation indices. This work, therefore, proposed chaos quantum-behave particle swarm optimization algorithm by constructing the dynamic penalty function based on the judgment of local convergence for swarm fitness variance. In this way, the weighting coefficient of various indices could be determined with improved search efficiency. With LOG controller, the active suspension effectively reduced the dynamic travel of suspension and the dynamic displacement of tire. Therefore, the riding comfort and handling stability of vehicle were improved with the overall performance of suspension upgraded. The design avoids the subjectivity and blindness of current method in selecting weighting coefficient.
Keywords: Active suspension; LQR controller; Quantum-behaved particle swarm optimization (QPSO); Chaos searching; Particle swarm optimization (PSO).