Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization

doi:10.23919/JSEE.2021.000057

Abstract

Abstract:

The trajectory optimization of an unpowered reentry vehicle via artificial emotion memory optimization (AEMO) is discussed. Firstly, reentry dynamics are established based on multiple constraints and parameterized control variables with finite dimensions are designed. If the constraint is not satisfied, a distance measure and an adaptive penalty function are used to address this scenario. Secondly, AEMO is introduced to solve the trajectory optimization problem. Based on the theories of biology and cognition, the trial solutions based on emotional memory are established. Three search strategies are designed for realizing the random search of trial solutions and for avoiding becoming trapped in a local minimum. The states of the trial solutions are determined according to the rules of memory enhancement and forgetting. As the iterations proceed, the trial solutions with poor quality will gradually be forgotten. Therefore, the number of trial solutions is decreased, and the convergence of the algorithm is accelerated. Finally, a numerical simulation is conducted, and the results demonstrate that the path and terminal constraints are satisfied and the method can realize satisfactory performance.

Key words: trajectory optimization, adaptive penalty function, artificial emotion memory optimization (AEMO), multiple constraint

Shengnan FU, Liang WANG, Qunli XIA. Trajectory optimization of a reentry vehicle based on artificial emotion memory optimization[J]. Journal of Systems Engineering and Electronics, 2021, 32(3): 668-680.

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Optimization algorithm	Minimum solution	Maximum solution	Average solution	Uptime	Similarity of the optimal solution/%
AEMO	1.0e-3	2.1e-3	1.2e-3	24	12
DE	4.5e-2	0.52	0.27	31	41
PSO	8.9e-4	7.6e-2	2.4e-2	37	36
GA	45.19	262.82	148.76	20	39

Optimization algorithm	Distribution proportion in the memory system/%			Proportion of forgetting memory cells to all cells/%
Optimization algorithm	Instantaneous system	Short-term system	Long-term system	Proportion of forgetting memory cells to all cells/%
Unimproved AEMO	12.6	25.7	61.7	33.5
Improved AEMO	27.4	32.6	40.0	52.5

Optimization function	Minimum solution	Maximum solution	Average solution	Uptime
Ackley	1.0e-3	1.4e-3	1.1e-3	25
Rastrigin’s	1.0e-3	1.1e-2	1.1e-2	20
Rosenbrock	6.5	13.6	9.8	26
Schwefel2.21	1.0e-3	1.1e-2	1.1e-2	29
Schwefel2.22	1.0e-3	1.3e-2	1.2e-2	32

Optimization function	DE	PSO	GA
Ackley	1.0e-2(22)	1.64	19.72
Rastrigin’s	70.15	71.86	225.37
Rosenbrock	351.41	2.5e+5	5.4e+5
Schwefel2.21	4.99	70.92	68.24
Schwefel2.22	3.33	4.8e+12	7.11e+20

Optimization function	AEMO	DE	PSO	GA
Ackley	3	4	2	1
Rastrigin’s	4	3	2	3
Rosenbrock	4	3	2	1
Schwefel2.21	4	3	1	2
Schwefel2.22	4	3	2	1
Total score	19	16	8	9
Average score	3.8	3.2	1.8	1.6