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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In the field of AGV, a path planning algorithm is always a heated area. However, traditional path planning algorithms have many disadvantages. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constraint A* algorithm and the following dynamic window approach algorithm. The kinematical constraint A* algorithm can plan the global path. Firstly, the node optimization can reduce the number of child nodes. Secondly, improving the heuristic function can increase efficiency of path planning. Thirdly, the secondary redundancy can reduce the number of redundant nodes. Finally, the B spline curve can make the global path conform to the dynamic characteristics of AGV. The following DWA algorithm can be dynamic path planning and allow the AGV to avoidance moving obstacle. The optimization heuristic function of the local path is closer to the global optimal path. The simulation results show that, compared with the fusion algorithm of traditional A* algorithm and traditional DWA algorithm, the fusion algorithm reduces the length of path by 3.6%, time of path by 6.7% and the number of turns of final path by 25%.

With the rapid growth of railways in China, the focus has changed to the maintenance of large-scale rail structures. Multi-agent systems (MASs) based on wireless sensor network (WSNs) with soft multi-functional sensors (SMFS) are adopted cooperatively for the structural health monitoring of large-scale rail structures. An MAS framework with three layers, namely the sensing data acquisition layer, sensor data processing layer, and application layer, is built here for collaborative data collection and processing for a rail structure. WSN nodes with strain, temperature, and piezoelectric sensor units are developed for the continuous structural health monitoring of the rail structure. The feature data at different levels are extracted for the online monitoring of the rail structure. Experiments carried out at the Rail Transmit Base at East China Jiaotong University verify that the WSN nodes with SMFS are successfully assembled onto a 100-m-long track for damage detection. Based on the sensing data and feature data, a neural network data fusion agent (DFA) is applied to calculate the damage index value of the track for comprehensive decisions regarding rail damage. The use of WSNs with multi-functional sensors and intelligent algorithms is recommended for cooperative structural health monitoring in railways.