An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network

The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot

Path Tracking Control of an Omni-Directional Service Robot Based on Model Predictive Control of Adaptive Neural-Fuzzy Inference System

In this paper, model predictive control (MPC) based on an adaptive neural-fuzzy inference system (ANFIS) is proposed to realize control of an omni-directional service robot in path tracking. The weight of the cost function in a traditional MPC needs to be manually adjusted, and it is difficult to adjust to a satisfactory value. In order to improve the performance and the control accuracy of MPC, a fuzzy system trained by ANFIS is used to adaptively adjust the weight of MPC’s cost function to reduce the error in the process of path tracking. The different simulation experiments are conducted to verify the performance of the proposed algorithm. The experimental results show that the distance error of MPC based on ANFIS is reduced by more than 50% under different paths compared with a traditional MPC, and the angle error is reduced by more than 70%. Meanwhile, the stability is increased by around 60%. The results show the feasibility and superiority of MPC based on ANFIS

Ammonia sensing properties of different polyaniline-based composite nanofibres

138-144Polyamide 6 (PA6), polyacrylonitrile (PAN) and cellulose acetate (CA) have been prepared by electrospinning. Electrospun CA nanofibres are deacetylated to obtain cellulose nanofibres. Polyaniline (PANI)-based composite nanofibres have been prepared by in situ polymerization of aniline with the electrospun PA6, PAN and cellulose nanofibres separately. Structural and chemical examinations of the prepared composite nanofibres are conducted by scanning electron microscope and Fourier transform infrared spectroscopy. The sensing properties of the PANI-based composite nanofibres to ammonia are evaluated by a home-made sensor test system. It is found that PA6/PANI, PAN/PANI and cellulose/PANI composite nanofibres respond to ammonia. Cellulose/PANI composite nanofibres show the best ammonia sensing properties among them, whose response to 250 ppm ammonia is found to be 2.70, followed by PA6/PANI (1.52) and PAN/PANI’s (1.23)

Ammonia sensing properties of different polyaniline-based composite nanofibres

Polyamide 6 (PA6), polyacrylonitrile (PAN) and cellulose acetate (CA) have been prepared by electrospinning. Electrospun CA nanofibres are deacetylated to obtain cellulose nanofibres. Polyaniline (PANI)-based composite nanofibres have been prepared by in situ polymerization of aniline with the electrospun PA6, PAN and cellulose nanofibres separately. Structural and chemical examinations of the prepared composite nanofibres are conducted by scanning electron microscope and Fourier transform infrared spectroscopy. The sensing properties of the PANI-based composite nanofibres to ammonia are evaluated by a home-made sensor test system. It is found that PA6/PANI, PAN/PANI and cellulose/PANI composite nanofibres respond to ammonia. Cellulose/PANI composite nanofibres show the best ammonia sensing properties among them, whose response to 250 ppm ammonia is found to be 2.70, followed by PA6/PANI (1.52) and PAN/PANI’s (1.23)

Ammonia Sensing Behaviors of TiO2-PANI/PA6 Composite Nanofibers

Titanium dioxide-polyaniline/polyamide 6 (TiO2-PANI/PA6) composite nanofibers were prepared by in situ polymerization of aniline in the presence of PA6 nanofibers and a sputtering-deposition process with a high purity titanium sputtering target. TiO2-PANI/PA6 composite nanofibers and PANI/PA6 composite nanofibers were fabricated for ammonia gas sensing. The ammonia sensing behaviors of the sensors were examined at room temperature. All the results indicated that the ammonia sensing property of TiO2-PANI/PA6 composite nanofibers was superior to that of PANI/PA6 composite nanofibers. TiO2-PANI/PA6 composite nanofibers had good selectivity to ammonia. It was also found that the content of TiO2 had a great influence on both the morphology and the sensing property of TiO2-PANI/PA6 composite nanofibers

Comprehensive analysis of photosynthetic characteristics and quality improvement of purple cabbage under different combinations of monochromatic light

Light is essential for plant growth. Light intensity, photoperiod and light quality all affect plant morphology and physiology. Compared to light intensity, photoperiod, little is known about the effects of different monochromatic lights on crop species. To investigate how different lighting conditions influence crops with heterogeneous colors in leaves, we examined photosynthetic characteristics and quality (regarding edibility and nutrition) of purple cabbage under different combinations of lights. Eight different treatments were applied including monochromic red (R), monochromic blue (B), monochromic yellow (Y), monochromic green (G) and the combination of red and blue (3/1, RB)， red/blue/yellow (3/1/1, RBY)， red /blue/green (3/1/1,RBG) and white light as the control. Our results indicate that RBY (3/1/1) treatment promotes the PSII activity of purple cabbage, resulting in improved light energy utilization. By contrast, both G and Y lights alone have inhibitory effect on the PSII activity of purple cabbage. In addition, RBY (3/1/1) significantly boosts the anthocyanin and flavonoids content compared with other treatments. Although we detected highest soluble protein and vitamin C content under B treatment (increased by 29.99% and 14.29% compared with the control respectively), RBY (3/1/1) appeared to be the second-best lighting condition (with soluble protein and vitamin C content increased by 8.63% and 4.07% respectively compared with the control). Thus we prove that the addition of yellow light to the traditional combination of red/blue lighting conditions is beneficial to synthesizing photosynthetic pigments and enables superior outcome of purple cabbage growth. Our results indicate that the growth and nutritional quality of purple cabbage are greatly enhanced under RBY (3/1/1) light, and suggest that strategical management of lighting conditions holds promise in maximizing the economic efficiency of plant production and food quality of vegetables grown in controlled environments

Novel Phenolic Biosensor Based on a Magnetic Polydopamine-Laccase-Nickel Nanoparticle Loaded Carbon Nanofiber Composite

A novel phenolic biosensor was prepared on the basis of a composite of polydopamine (PDA)-laccase (Lac)-nickel nanoparticle loaded carbon nanofibers (NiCNFs). First, NiCNFs were fabricated by a combination of electrospinning and a high temperature carbonization technique. Subsequently, the magnetic composite was obtained through one-pot Lac-catalyzed oxidation of dopamine (DA) in an aqueous suspension containing Lac, NiCNFs, and DA. Finally, a magnetic glass carbon electrode (MGCE) was employed to separate and immobilize the composite; the modified electrode was then denoted as PDA-Lac-NiCNFs/MGCE. Fourier transform infrared (FT-IR) spectra and cyclic voltammetry (CV) analyses revealed the NiCNFs had good biocompatibility for Lac immobilization and greatly facilitated the direct electron transfer between Lac and electrode surface. The immobilized Lac showed a pair of stable and well-defined redox peaks, and the electrochemical behavior of Lac was a surface-controlled process in pH 5.5 acetate buffer solution. The PDA-Lac-NiCNFs/MGCE for biosensing of catechol exhibited a sensitivity of 25 μA mM^–1 cm^–2, a detection limit of 0.69 μM (S/N = 3), and a linear range from 1 μM to 9.1 mM, as well as good selectivity and stability. Meanwhile, this novel biosensor demonstrated its promising application in detecting catechol in real water samples