Two-Stage Switching Hybrid Control Method Based on Improved PSO for Planar Three-Link Under-Actuated Manipulator

Abstract

The planar three-link passive-active-active (PAA) under-actuated manipulator does not satisfy the small-time local controllability (STLC), which makes it more difficult to control than other planar under-actuated manipulators. This paper presents a two-stage switching hybrid control method based on improved particle swarm optimization (PSO) for the PAA under-actuated manipulator. According to the model reduction strategy, the control process is divided into two stages. The workspace of the manipulator is analyzed to guarantee the target position within the reachable region. In order to reduce the probability of falling into local optimum, we introduce the Metropolis criterion of simulated annealing algorithm and crowding factor of artificial fish swarm algorithm into PSO algorithm to calculate the target angles of two active joints. At each control stage, we design the Lyapunov function to maintain the angle of an active joint unchanged while we build the sliding surface and select the power function as reaching law to make the other active joint converge to the target angle. The control law switches to next stage when the rotated joint converges to the target angle. The proposed method not only retains the rapidity of sliding mode control, but also reduces the total chattering of the system by the Lyapunov function method. Simulation results demonstrate that the proposed method has shorter steady-state adjustment time than that of existing typical control methods, and its overshoot is almost zero. INDEX TERMS The planar three-link passive-active-active (PAA) under-actuated manipulator, the improved particle swarm optimization (PSO), hybrid control method, Lyapunov direct method, sliding mode control.

A Sliding Mode Control Strategy for an ElectroHydrostatic Actuator with Damping Variable Sliding Surface

“…State observers are commonly applied to control systems for the tasks of system uncertainties estimation and compensation [23][24][25]. In addition, Gao et al constructed a two-stage sliding mode control to retain its rapidity and employed an improved Lyapunov function to reduce system chattering [26]. Ren and Zhou presented a sliding mode controller with a fuzzy logic reasoning strategy, which provides faster adjustment time and higher accuracy [27].…”

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“…An excellent controller needs well parameter tuning. And a lot of metaheuristic parameter searching algorithm, such as particle swarm optimization (PSO) algorithm [31], bat algorithm (BA) [32], grey wolf optimization algorithm (GWO) [34], genetic algorithms (GA) [35][36] and cuckoo search (CS) algorithm [37] are adopted in optimization of parameter of controller for its powerful performance for tough optimization problems. T. M. Ahmed et al [16] used PSO to optimize the parameters of fractional-order PID controller.…”

Operational Space Control for Planar PAN–1 Underactuated Manipulators Using Orthogonal Projection and Quadratic Programming

“…Their operational space control design was significantly different from common practice in fully-actuated or redundant manipulators, e.g., using operational space frameworks in [14]. Firstly, the control was not accomplished at the task space level, but at the joint space level; optimization or searching methods were used to determine the desired joint configuration with respect to the desired end-effector position, such as genetic algorithm [8], differential evolution algorithm [9] and particle swarm optimization [10], [11]. Secondly, several control strategies were designed to force the joints to achieve the desired ones, such as Lyapunov function methods [8], [11] and model reduction to planar Acrobot [10].…”

“…Firstly, the control was not accomplished at the task space level, but at the joint space level; optimization or searching methods were used to determine the desired joint configuration with respect to the desired end-effector position, such as genetic algorithm [8], differential evolution algorithm [9] and particle swarm optimization [10], [11]. Secondly, several control strategies were designed to force the joints to achieve the desired ones, such as Lyapunov function methods [8], [11] and model reduction to planar Acrobot [10]. However, the resulting controllers in [10], [11] were two-stage control with switching.…”

“…Secondly, several control strategies were designed to force the joints to achieve the desired ones, such as Lyapunov function methods [8], [11] and model reduction to planar Acrobot [10]. However, the resulting controllers in [10], [11] were two-stage control with switching. And [9], [10] required the explicit relation between the passive joint and active joints for their planning process, which may hinder their methods from being scaled up to a general case due to extensive derivation efforts.…”