Abstract
Resolving the trade-offs between suspension travel, ride comfort, road holding, vehicle handling and power consumption is the primary challenge in the design of active vehicle suspension system. Multi-loop proportional + integral + derivative controllers’ gains tuning with global and evolutionary optimization techniques is proposed to realize the best compromise between these conflicting criteria for a nonlinear full-car electrohydraulic active vehicle suspension system. Global and evolutionary optimization methods adopted include: controlled random search, differential evolution, particle swarm optimization, modified particle swarm optimization and modified controlled random search. The most improved performance was achieved with the differential evolution algorithm. The modified particle swarm optimization and modified controlled random search algorithms performed better than their predecessors, with modified controlled random search performing better than modified particle swarm optimization in all aspects of performance investigated both in time and frequency domain analyses.
Highlights
Good active vehicle suspension system (AVSS) is a control challenge in the design of vehicle suspension systems
The challenge includes the determination of the optimal trade-offs between conflicting suspension performance parameters like suspension travel, ride comfort, road holding and vehicle handling
Compromise must be reached when a hard suspension with limited suspension travel is required for good road holding, and a soft suspension is desired for a smooth and comfortable ride
Summary
Good active vehicle suspension system (AVSS) is a control challenge in the design of vehicle suspension systems. After the above steps are completed, the procedure is continued until the stopping criterion is met.[46] The procedure for CRS global optimization algorithm is summarized in the following steps: Step 1 Generate a randomly distributed population set that uniformly spans the search space. This will lead to early convergence and limit the success rate (number of times the weakest individual xw is replaced) of the algorithm To overcome this shortfall, three random individuals will be selected from the solution space S, as opposed to the n þ 1 individuals that were previously chosen, these individuals will be in ascending order according to their fitness values with x1 being the fittest individual followed by x2 and x3, respectively. AVSS: active vehicle suspension system; DE: differential evolution; MCRS: modified controlled random search; MPSO: modified particle swarm optimization; PID: proportional þ integral þ derivative
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