Critical reagents for ligand-binding assays: process development methodologies to enable high-quality reagents

Development of biotherapeutics require pharmacokinetic/pharmacodynamic (PK/PD) and immunogenicity assays that are frequently in a ligand-binding assay (LBA) format. Conjugated critical reagents for LBAs are generated conjugation of the biotherapeutic drug or anti-drug molecule with a label. Since conjugated critical reagent quality impacts LBA performance, control of the generation process is essential. Our perspective is that process development methodologies should be integrated into critical reagent production to understand the impact of conjugation reactions, purification techniques and formulation conditions on the quality of the reagent. In this article, case studies highlight our approach to developing process conditions for different molecular classes of critical reagents including antibodies and a peptide. This development approach can be applied to the generation of future conjugated critical reagents.

Variable Gain Prescribed Performance Control for Dynamic Positioning of Ships with Positioning Error Constraints

In this paper, a variable gain prescribed performance control law is proposed for dynamic positioning (DP) of ships with positioning error constraints, input saturation and unknown external disturbances. The error performance index functions are designed to preset the prescribed performance bounds and the error mapping functions are constructed to incorporate the prescribed performance bounds into the DP control design. The variable gain technique is used to limit the output amplitude of the control law to avoid input saturation of the system by dynamically adjusting the control gain of the DP control law according to the positioning errors, and the error mapping function replaces the positioning error as a recursive sliding-mode surface to realize the prescribed performance control of the system and guarantee the stability of the closed-loop system with variable control gains. It has been proved that the proposed DP control law can make the uniformly ultimately boundedness of all signals in the DP closed-loop control system. The numerical simulation results illustrate that the proposed control law can make the ship’s position and heading maintain at the desired value with positioning error constraints, enhance the non-fragility of the DP control law to the perturbation of system’s parameters and improve the system’s rejection ability to external disturbances.

Experimental Investigation of the Performance of a Tuned Heave Plate Energy Harvesting System for a Semi-Submersible Platform

Heave plates are widely used for improving the sea keeping performance of ocean structures. In this paper, a novel tuned heave plate energy harvesting system (THPEH) is presented for the motion suppression and energy harvesting of a semi-submersible platform. The heave plates are connected to the platform though a power take-off system (PTO) and spring supports. The performance of the THPEH was investigated through forced oscillation tests of a 1:20 scale model. Firstly, the hydrodynamic parameters of the heave plate were experimentally studied under different excitation motion conditions, and a force model of the power take-off system was also established through a calibration test. Then, the motion performance, control performance, and energy harvesting performance of the THPEH subsystem were systematically studied. The effects of the tuned period and PTO damping on the performance of the THPEH were analyzed. Finally, a comparison between the conventional fixed heave plate system and THPEH was carried out. The results show that a properly designed THPEH could consume up to 2.5 times the energy from the platform motion compared to the fixed heave plate system, and up to 80% of the consumed energy could be captured by the PTO system. This indicates that the THPEH could significantly reduce the motion of the platform and simultaneously provide considerable renewable energy to the platform.

Sliding Mode Fault Tolerant Control for a Quadrotor with Varying Load and Actuator Fault

In this paper, an adaptive sliding mode fault-tolerant control scheme based on prescribed performance control and neural networks is developed for an Unmanned Aerial Vehicle (UAV) quadrotor carrying a load to deal with actuator faults. First, a nonsingular fast terminal sliding mode (NFTSM) control strategy is presented. In virtue of the proposed strategy, fast convergence and high robustness can be guaranteed without stimulating chattering. Secondly, to obtain correct fault magnitudes and compensate the failures actively, a radial basis function neural network-based fault estimation scheme is proposed. By combining the proposed fault estimation strategy and the NFTSM controller, an active fault-tolerant control algorithm is established. Then, the uncertainties caused by load variation are explicitly considered and compensated by the presented adaptive laws. Moreover, by synthesizing the proposed sliding mode control and prescribed performance control (PPC), an output error transformation is defined to deal with state constraints and provide better tracking performance. From the Lyapunov stability analysis, the overall system is proven to be uniformly asymptotically stable. Finally, numerical simulation based on a quadrotor helicopter is carried out to validate the effectiveness and superiority of the proposed algorithm.

Prescribed Performance Control with Sliding-Mode Dynamic Surface for a Glue Pump Motor Based on Extended State Observers

The actuator of a particleboard glue-dosing system, the glue pump motor, is affected by external disturbances and unknown uncertainty. In order to achieve accurate glue-flow tracking, in this paper, a glue pump motor compound control method was designed. First, the prescribed performance control method is used to improve the transient behaviors, and the error of the glue flow tracking is guaranteed to converge to a preset range, as a result of the design of an appropriate performance function. Second, two extended state observers were designed to estimate the state vector and the disturbance, in order to improve the robustness of the controlled system. To further strengthen the steady-state performance of the system, the sliding-mode dynamic surface control method was introduced to compensate for uncertainties and disturbances. Finally, a Lyapunov stability analysis was conducted, in order to prove that all of the signals are bounded in a closed-loop system, and the effectiveness and feasibility of the proposed method were verified through numerical simulation.

Artificial Intelligence in Electric Machine Drives: Advances and Trends

This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.