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In this paper, we leverage a reinforcement learning approach to address the motion control problem of Four In-Wheel Motor Actuated Vehicles aimed at achieving precise control while optimizing energy efficiency. Our control architecture consists of four adaptive Proportional-Integral-Derivative controllers, each assigned to an independent vehicle wheel. We train these controllers using an actor-critic framework in two standard driving scenarios: acceleration and braking, as well as a double lane-change maneuver. This method eliminates the need for a detailed mathematical model of the complex vehicle dynamics. Moreover, the adaptive mechanism enables controllers to dynamically adapt to varying operating conditions. After training, the resulting controllers are tested in unseen scenarios to validate their robustness and adaptability beyond the training environment. The testing results show that our controllers achieve precise velocity and trajectory tracking while maintaining low energy consumption.

The noise and linearity performance of a MEMS accelerometer is a critical issue for land seismic acquisition applications. Incorporating closed-loop force feedback is an effective way to enhance those performances. However, additional electrode to exert the electrostatic force is typically not available and residue displacement of proof mass would introduce significant nonlinearity to the closed-loop transfer function, impairing the efficiency of the method. This paper proposes a switched-capacitor closed-loop readout interface for MEMS accelerometer which greatly alleviates those problem. First, the proposed system incorporates a time-multiplexing technique, thus the sensing and force feedback can be realized using the same electrode and get separated in time sequence. Furthermore, the cross-coupling of high-voltage feedback signal and weak sensing signal can be minimized. Second, a correlated double sampling (CDS) technique and a PID control mechanism is introduced in the loop. Thus, the two sources of residue displacement: interface mismatch and insufficient loop gain can be well-suppressed. The test results show that the proposed closed-loop MEMS accelerometer can achieve an SNR better than 120 dB, with an in-band noise spectral density lower than 500 ng/Hz${}^{1/2}$.

In this paper, a novel adaptive tuning method of PID neural network (PIDNN) controller for nonlinear process is proposed. The presented method utilizes an improved gradient descent method to adjust PIDNN parameters where the margin stability will be employed to get high tracking performance and robustness with regard to external load disturbance and parameter variation. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.

In this paper, based on the combination scheme of dual-sampling and adaptive predictive PID control, a digital controller for improving the transient performance of Buck DC–DC converters is designed. Due to the inherent loop delay in analog-to-digital (A/D) conversion, the calculation process of the digital controller and digital pulse width modulator (DPWM) of conventional digitally-controlled Buck DC–DC converters limits the system bandwidth and this makes the transient response lower. The designed digital controller can reduce the delay time in analog-to-digital converter (ADC), the digital controller and DPWM of digitally-controlled Buck DC–DC converters. Adaptive predictive control is used to eliminate the delay time of ADC and the digital controller, while dual-sampling scheme is used to reduce the delay time of DPWM in this paper. These are two new control schemes, and they show better performance in improving the transient response than other existing control schemes. Both simulation and experimental results demonstrate that the designed digital controller based on dual-sampling and adaptive predictive PID control is effective in improving the transient performance of Buck DC–DC converters. During experimental verification, for a load step between 0.5A and 1.0A, the fastest transient recovery time and the overshoot voltage are found to be 102$\mu$s and 120mV, respectively. Compared with the conventional digital PID controller, the transient recovery time and the overshoot voltage of the digital controller designed in this paper are decreased by 40.0% and 27.3%, respectively.

The effectiveness of cross entropy (CE) method has been investigated on both combinatorial and continuous optimization problems, though it lacks exploitative search to refine solutions. Hybrid with local search (LS) method can greatly improve the performance of evolutionary algorithm. This paper proposes a parameter-less framework combining CE with LS method. Four LS methods are chosen and four combination algorithms are obtained after combining them with the CE method. We first study the performance of the four combinations on a set of twenty eight mathematical functions including both unimodal and multimodal functions. CE hybrid with Powell’s method (CE-Pow) is identified as the most effective algorithm. Then the CE-Pow algorithm is applied to resolve proportional, integral, and derivative (PID) controller design problem and Lennard-Jones potential problem. Its performance has been verified by comparing with four state of the art evolutionary algorithms. Experimental results show that CE-Pow significantly outperforms other benchmark algorithms.

Integrated processes between automatic process control (APC) and statistical process control (SPC) have been of interest to researchers in the process control area as both are complementary to each other and share the common objective of reduction of process variation. In this paper, a control system with damping property functions is designed as an integral controller and is combined with SPC charts in order to reduce process variability. The proposed controller is compared with other existing and widely-used controllers including minimum mean square error controllers and proportional, integral and derivative controllers under the integration of APC and SPC. The results show that indeed a damping controller with optimum parameters results in a more significant reduction in process variation than the widely-used ones. Numerical examples are given to demonstrate the applicability of the proposed controller.

To study the nonlinear characteristics simulation and experiment of multimode driving of intelligent vehicle, an improved neural network PID controller is proposed to realize the safe and active braking requirements of cruise vehicles, according to the study of vehicle dynamics of different active braking forms and the joint simulation of road condition, driving condition, and braking mechanical structure. On the basis of the current research, the four-layer neural network combined with PID control is proposed in this paper, which can better control the braking compared with the traditional PID controller and BP neural network, safety, efficiency and stability have been improved in the following cars. According to the animation simulation demonstration of CAR SIM software, it can be determined that the improved neural network PID controller in the mechanical system can realize the safety and active braking requirements of cruise vehicle, and it is more accurate and efficient than the traditional BP neural network PID controller. Relevant parameters of the existing millimeter-wave radars are set by the radar sensors, and the long-distance horizontal detection range is plus or minus 10 degrees. The radar is installed on the longitudinal axis of the front bumper and has a height of 442mm. According to Car SIM’s preset radar sensor module parameters, the sensors can independently collect and transmit relevant data, it is also possible to cooperate with other sensors for the same variable parameter to increase the accuracy and avoid abnormal errors caused by the failure of a certain sensor. Compared with the traditional PID controller and BP neural network, the combination of four-layer neural network and PID control can carry out better braking control and improve the safety, efficiency, and stability of the following cars.

In proportional-integral-derivative (PID) control, it is well known that the selection of a method to deal with noise is an important issue and various methods have been proposed. However, similar methods to determine the response to noise have not been studied in probability theory. In this paper, a new method called “weak form” is proposed and a probabilistic analysis of filtered derivatives is performed using this method. The method discussed in this paper is considered effective when it is not a feedback control.

The coupled tank system is the most widely used sub-component in chemical process industries. Fluid mixing is a major step in chemical processes that alters the material properties and cost. Fluid flow and its level regulation between several tanks are important control problems. As the first step, this paper addresses the level regulation problem using classical integer order proportional, derivative, integral (PID), fractional order PID controllers. As a second step, model-based robust fractional order controllers are derived using sliding mode approach in order to achieve the desired response, parameters of the proposed controllers are tuned using genetic algorithm. Finally, system performance under all variants of control schemes has been tested using numerical simulations.

Three different controllers for dissolved oxygen setpoint regulation were implemented in a sequencing batch reactor used to treat toxic wastewater. In particular, these were a PID controller with anti-windup, a linearizing plus PID controller, and a fuzzy PD controller. They were tested and designed in simulation using a nonlinear fourth order model and implemented in real time in a pilot scale laboratory bioreactor with promising results.

A fuzzy PID control strategy is brought forward for typical temperature control system, which can save the investment on equipment and achieve the least set time on condition that it does not overshoot. To adapt to the mentioned instance, it improves the traditional fuzzy control strategy and the tuning of PID controllers. Simulation results show that the solution of the controller design is simple, practical and effective.

Accurate control of the air-fuel ratio is the most pivotal technology in modern automotive gasoline engine control. Most electronically-controlled gasoline engines run under conditions of partial load. Researching the air-fuel ratio control system of electronically-controlled engines under partial load condition is thus of great significance for reducing emissions and improving fuel economy. In this paper, after analyzing the target air-fuel ratio control, a mathematical model of the engine is first established. Then, air-fuel ratio PID control system is designed under the partial load condition, and the simulation model of the engine air-fuel ratio control is established. Finally, the control effect of the air-fuel ratio was compared with the conventional control system which didn't utilize the control strategy. Simulation results show that under partial load conditions, when using the PID algorithm, the control effect is better, control accuracy is higher, and the control performance is optimal.

In order to improve GC thermal conductivity detector’s sensitivity, expand its linear range, we designed a constant average temperature power supply control system with analog and digital dual closed-loop. Control performance was evaluated directly based on the temperature of controlled object in this paper. Experiments proved that the system can effectively improve the temperature control stability and accuracy of thermal conductivity detector.

This paper proposes the use of a PID controller to tackle the problem of stabilizing a double integral process with time delay. As the proportional gain reaches the extreme value, the closed-loop system contains a double pole on the non-negative imaginary axis. Using this property, the admissible range of the proportional gain is determined and the corresponding integral gain and derivative gain are obtained. For a fixed value of the proportional gain, the stability region in the plane of the integral and derivative gains is determined analytically. Subsequently, a numerical example is used illustrate the proposed method.

Based on the DSP, a kind of induction heating power supply of high frequency is designed. The switching element is IGBT. The control technology of PID adopted can adjust the power in time. The application of digital phase-locked loop technology in high frequency occasion which is based on DSP and DPLL makes the phase lock has fast dynamic property and steady-state performance of high precision. It realizes reliable load frequency tracing and reliable inverter mode control. At the same time, it increases work efficiency and power factor of the inverter. Thus, it helps achieve the purpose of promoting the digital control of high frequency induction heating power supply. It guarantees the heating efficiency, stability and anti-jamming capability in the system. In a word, it is of high utility value.