Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

Design of a Noninvasive Bladder Urinary Volume Monitoring System Based on Bio-Impedance

For the needs of bladder urinary volume noninvasive monitoring in clinical, we present a noninvasive bladder urinary volume monitoring system based on bio-impedance. The system uses a four-electrode structure,which is composed of a pair of excitation electrodes and a pair of measurement electrodes. The Direct Digital Frequency Synthesis (DDS) is applied to generate a 50 kHz sine current excitation source. The impedance information extracted from phase sensibility demodulation technology is transferred to a computer through Zigbee wireless technology for real-time monitoring. Two experiments are taken to verify the accuracy and feasibility of the system. The experiments results show that the system can accurately measure the corresponding electrical impedance change of the bladder. The system provides a new way to continuously and noninvasively monitor the bladder urinary volume of patients with bladder dysfunction.

Challenges and solutions in automotive powertrain systems

Automotive powertrain mainly consisting of combustion engine,motor and battery(i.e.special for hybrid powertrain)is a very complicated integration system,and the research on the automotive powertrain control techniques remains hot-spot in past decades.This paper proposes some challenging issues and control solutions of automotive powertrain system from the perspective of the dynamic system theory.The typical characteristics of automotive powertrain system are analysed for control development,and the several control applications using model-based and model-free control design are demonstrated with sufficient experimental validation.In addition,some open issues for future powertrain control development are summarised.

Revisiting the benchmark problem of starting control of combustion engines

Starting of combustion engines is a typical transient operating mode that has sigrdficant influence to the engine performance.Due to the distinct variations in the pathes of air intake and fuel injection,the model of the engine system contains considerable uncertain parameters.To search effective control schemes that guarantee desired performance,engine starting control is proposed as a benchmark challenge problem.As a challenging result,a model-based control scheme is developed perviously.In this work,the benchmark problem is revisited and a modification for the fuel injection path control of the previous work is proposed by integrating a time sequence regressive based parameter tuning strategy.Validation by the benchmark problem simulator shows that although the new strategy has simple structure,similar control performance is obtained.Especially,the new strategy has potential extensibility with learning based methods to further improve the performance of the benchmark problem on engine starting control.

Speed planning and energy management strategy of hybrid electric vehicles in a car-following scenario

The development of intelligent connected technology has brought opportunities and challenges to the design of energy management strategies for hybrid electric vehicles.First,to achieve car-following in a connected environment while reducing vehicle fuel consumption,a power split hybrid electric vehicle was used as the research object,and a mathematical model including engine,motor,generator,battery and vehicle longitudinal dynamics is established.Second,with the goal of vehicle energy saving,a layered optimization framework for hybrid electric vehicles in a networked environment is proposed.The speed planning problem is established in the upper-level controller,and the optimized speed of the vehicle is obtained and input to the lower-level controller.Furthermore,after the lower-level controller reaches the optimized speed,it distributes the torque among the energy sources of the hybrid electric vehicle based on the equivalent consumption minimum strategy.The simulation results show that the proposed layered control framework can achieve good car-following performance and obtain good fuel economy.