Algorithms for simultaneous motion control of multiple T. pyriformis cells: Model predictive control and Particle Swarm Optimization

Abstract

This paper investigates the use of single control signal (magnetic field direction) and PSO-MPC algorithm to control multiple magnetized Tetrahymena pyriformis (T. pyriformis) cells to move from their initial positions to their target positions simultaneously while avoiding the obstacle. The magnetized T. pyriformis cells are generated by adding iron-oxide spherical particles into the cells. We control the cells' moving direction by changing the magnetic field direction. Based on Model Predictive Control (MPC) algorithm, we define a cost function which is composed of the target cost function and the obstacle potential function. The target cost function is to measure the sum of differences between cells' predicted positions and their target positions. The obstacle potential function is used to measure the repulsive force of the obstacle. The input variables of the cost function are the sequence of control signals. We use Particle Swarm Optimization (PSO) method to find a cost value which is close to the global minimum of the cost function. In the experimental result section, we show the control of three m3pi robots to move from their initial positions to their target positions with avoiding the obstacle. Since the similar control strategy has successfully controlled one T. pyriformis cell in our previous work, we believe our PSO-MPC algorithm is applicable on the multiple T. pyriformis cells' control task.