In this paper, a position domain cross-coupled iterative learning controller combining proportional–integral–derivative (PID)-type iterative learning control (ILC) and proportional–derivative (PD)-type cross-coupling control (CCC) is presented aiming at non-linear contour tracking in multi-axis motion systems. Traditional individual control methods in the time domain suffer from poor synchronization of relevant motion axes. The complicated computation of coupling gains in CCC and cross-coupled ILC (CCILC) restricts their applications for non-linear contour. The proposed position domain CCILC (PDCCILC) approach introduces a position domain design concept into CCILC to improve synchronization and performance for non-linear contour tracking and it relies less on the accuracy of coupling gains than conventional CCILC. The stability and performance analysis are conducted using a lifted system representation. The contour error vector method is applied to estimate the coupling gains in simulations and experiments. Simulation and experimental results of three typical non-linear contour tracking cases (i.e. semi-circle, parabola and spiral) based on a two-axis micro-motion stage demonstrate superiority and efficacy of the proposed feedback PID and feedforward PDCCILC compared with existing ILC and CCILC in the time domain.
Driving status monitoring is important to safety driving which could be adopted to improve driving behaviors through hand gesture detection by wearable electronics. The soft bimodal sensor array (SBSA) composed of strain sensor array based on ionic conductive hydrogels and capacitive pressure sensor array based on ionic hydrogel electrodes is designed to monitor drivers’ hand gesture. SBSA is fabricated and assembled by the stretchable functional and structural materials through a sol–gel process for guaranteeing the overall softness of SBSA. The piezoresistive strain and capacitive pressure sensing abilities of SBSA are evaluated by the data acquisition system and signal analyzer with the external physical stimuli. The gauge factor (GF) of the strain sensor is 1.638 under stretched format, and –0.726 under compressed format; sensitivity of the pressure sensor is 0.267 kPa−1 below 3.45 and 0.0757 kPa−1 in the range of 3.45–12 kPa, which are sensitive enough to hand gesture detection and driving status monitoring. The simple recognition method for the driver’s status behavior is proposed to identify the driver’s behaviors with the piezoresistive properties of conductive polymers, and the turning angles are computed by the strain and pressure values from SBSA. This work demonstrates an effective approach to integrate SBSA seamlessly into an existing driving environment for driving status monitoring, expanding the applications of SBSA in wearable electronics.
Passive dynamic walking exhibits human-like and energy-efficient gait. Biologically inspired compliance introduced to flexible passivity-based robot would be helpful to generate stable locomotion. However, designing adaptive controller for flexible biped on compliant ground still remains a challenge. This paper aims to design an adaptive and model-free stiffness controller for passivity-based flexible biped on compliant ground, where the hip stiffness is modulated by double deep Q network. One benefit of the double deep Q network is that adaptive stiffness control policy could be directly learned from inputs. At first, passive dynamic walking gait is utilized as a reference trajectory during double deep Q network training. Then the trained double deep Q network is used as adaptive stiffness controller for biped on compliant ground. Simulation results show that the passivity-based biped robot could walk in such walking cases as disturbed initial condition, level compliant ground, downslope slippery compliant surface, and varying compliance environments. The adaptive stiffness controller would be used to make the passivity-based biped robot adapt to the environmental changes.
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