Control with rhythms: A CPG architecture for pumping a swing

Abstract

This paper presents a bio-inspired central pattern generator (CPG) architecture for feedback control of rhythmic behavior. The CPG circuit is realized as a coupled oscillator feedback particle filter. The collective dynamics of the filter are used to approximate a posterior distribution that is used to construct the optimal control input. The architecture is illustrated with the aid of a model problem involving pumping up of a swing, modeled as a parametrically forced pendulum. For this problem, the coupled oscillator particle filter is designed and its control performance demonstrated in a simulation environment.

Q-learning for POMDP: An application to learning locomotion gaits

“…The control policy (20) is implemented in [20] Algorithm 1 Q-learning for Optimal Contol of Two-body System Input: Parameters in Table I and a simulator for (3a)-(3b)-(4) Output: Optimal control policyû * (θ (N) ; w). 1: Initialize particles {θ i 0 } N i=1 ∼ Unif([0, 2π]); 2: Initialize weight vector w 0 according to (22). 3: for k = 0 to t T /∆t − 1 do 4:…”

“…At each time, one only has access to partial noisy measurements of the state. The proposed control architecture builds upon our prior work on phase estimation [22] and its use for optimal control of bio-locomotion [20]. As in [20], the control problem is modeled as an optimal control problem.…”

A coupled oscillators-based control architecture for locomotory gaits

“…The gain function K is obtained via a solution of a certain boundary value problem; cf., [25]. In terms of these particles, the expectation in (10) is approximated as…”

“…The methodology of this paper is a synthesis of several papers from our research group at the University of Illinois: the feedback particle filtering methodology for diffusion appears in [30], [29], [28]; its application to the problem of phase estimation is described in [24], [26]; a feedback particle filter-based approach to optimal control of partially observed diffusions is the subject of [21], [25]. In the present paper, we bring together these research threads to propose a CPG architecture for control of locomotion.…”