Integral pseudo sliding mode control for nonlinear systems

Aiming at the problem that the motion characteristics of ships sailing at sea or inland rivers are time-varying and nonlinear, and the heading is difficult to control in real time and accurately, this paper proposes a heading control strategy based on neural network PID controller. The neural network PID controller imitates the structure of neural network. A series of formula derivations can prove that the output of the controller has proportional, integral and differential effects on the error. The parameters of this neural network PID controller are adaptively adjusted in real time online according to the error and the rate of error change. Therefore, the ship control system can still output the optimal control strategy in real time under the complicated and changeable operating environment. Matlab / Simulink is used for simulation. The simulation results prove that the heading control strategy based on neural network PID controller has obvious superiority in real-time and precise control of heading.

With the continuous development of vehicle intelligence technology and active safety technology, the autonomous emergency braking pedestrian (AEB-P) system has emerged as a crucial element of contemporary automotive active safety systems. However, there are still some deficiencies in the control strategies utilized by the current AEB-P system. It is imperative to design an AEB-P system that can adapt the safety threshold and control strategy in real time based on the current driving conditions. The objective of this paper is to propose an AEB-P system with an adaptive control strategy and analyse its effectiveness from the perspective of pedestrian head injuries. Initially, an algorithm for adaptive collision time threshold model is proposed, and the AEB-P control strategy integrating this threshold is designed. Subsequently, a finite element model of the human head is developed to predict pedestrian head injuries in pedestrian-vehicle collision (PVC) scenarios with AEB-P intervention. Additionally, a simplified finite element model of PVC is established, and real PVC scenarios are reconstructed to assess the effectiveness of various AEB-P control strategies in mitigating pedestrian head injuries. The results demonstrate that the conventional control strategy is ineffective in reducing the risk of pedestrian head injuries at medium to high speeds. Conversely, the AEB-P system, which incorporates an adaptive control strategy, proves to be effective in decreasing the risk of pedestrian head injuries. Overall, the AEB-P system with adaptive control strategy enhances pedestrian safety, particularly in scenarios where PVCs are inevitable.

The human hand along with its fingers possess one of the highest numbers of nerve endings in the human body. It thus has the capacity for the richest tactile feedback for positioning capabilities. This article shares a new technique of controlling slippage. The sensing system used for the detection of slippage is a modified force sensing resistor (FSR®). The control system is a fuzzy logic control algorithm with multiple rules that is designed to be processed on a mobile handheld computing platform and integrated/working alongside a traditional Electromyography (EMG) or Electroencephalography (EEG) based control system used for determining position of the fingers. A 5 Degrees of Freedom (DOF) hand, was used to test the slippage control strategy in real time. First a reference EMG signal was used for getting the 5 DOF hand to grasp an object, using position control. Then a slip was introduced to see the slippage control strategy at work. The results based on the plain tactile sensory feedback and the modified sensory feedback are discussed.

BackgroundPattern recognition (PR) based strategies for the control of myoelectric upper limb prostheses are generally evaluated through offline classification accuracy, which is an admittedly useful metric, but insufficient to discuss functional performance in real time. Existing functional tests are extensive to set up and most fail to provide a challenging, objective framework to assess the strategy performance in real time.MethodsNine able-bodied and two amputee subjects gave informed consent and participated in the local Institutional Review Board approved study. We designed a two-dimensional target acquisition task, based on the principles of Fitts’ law for human motor control. Subjects were prompted to steer a cursor from the screen center of into a series of subsequently appearing targets of different difficulties. Three cursor control systems were tested, corresponding to three electromyography-based prosthetic control strategies: 1) amplitude-based direct control (the clinical standard of care), 2) sequential PR control, and 3) simultaneous PR control, allowing for a concurrent activation of two degrees of freedom (DOF). We computed throughput (bits/second), path efficiency (%), reaction time (second), and overshoot (%)) and used general linear models to assess significant differences between the strategies for each metric.ResultsWe validated the proposed methodology by achieving very high coefficients of determination for Fitts’ law. Both PR strategies significantly outperformed direct control in two-DOF targets and were more intuitive to operate. In one-DOF targets, the simultaneous approach was the least precise. The direct control was efficient in one-DOF targets but cumbersome to operate in two-DOF targets through a switch-depended sequential cursor control.ConclusionsWe designed a test, capable of comprehensively describing prosthetic control strategies in real time. When implemented on control subjects, the test was able to capture statistically significant differences (p < 0.05) in control strategies when considering throughputs, path efficiencies and reaction times. Of particular note, we found statistically significant (p < 0.01) improvements in throughputs and path efficiencies with simultaneous PR when compared to direct control or sequential PR. Amputees could readily achieve the task; however a limited number of subjects was tested and a statistical analysis was not performed with that population.

We consider the problem of designing an integral sliding mode controller to reduce the disturbance terms that act on nonlinear systems with state-dependent drift and input matrix. The general case of both, matched and unmatched disturbances affecting the system is addressed. It is proved that the definition of a suitable sliding manifold and the generation of sliding modes upon it guarantees the minimization of the effect of the disturbance terms, which takes place when the matched disturbances are completely rejected and the unmatched ones are not amplified. A simulation of the proposed technique, applied to a dynamically feedback linearized unicycle, illustrates its effectiveness, even in presence of nonholonomic constraints.

Introduction: Blood pressure (BP) control in the United States among persons with hypertension (HTN) is less than 50%. Performance and quality measures to improve care and outcomes for patients with high BP were published in 2019. The American Heart Association (AHA) recently completed its federally funded cooperative agreement, the National Hypertension Control Initiative (NHCI) to improve BP control in 350 community health centers (CHCs) with 1.5 million persons with HTN. Hypothesis: Use of a population health management platform can provide CHCs with more actionable, real-time Electronic Health Record (EHR) data to advance quality improvement (QI) efforts and evaluation capabilities. Methods: AHA identified a population health management platform to source EHR-derived data from 38 of 350 CHCs participating in NHCI. The objective was to demonstrate the value of readily accessing and using established measures for BP control in a project and system not already set up to do so. Eighteen clinical measures were used based on established performance and quality measures, aligning with Healthcare Effectiveness Data Information Set (HEDIS) where possible. Data for chosen measures was pulled from CHC EHRs on a monthly, quarterly, and ad hoc basis. This data expanded beyond what is required to be reported by CHCs to the Uniform Data System (UDS). Data sharing agreements were secured from 38 NHCI CHCs and 12 non-NHCI CHCs, as a comparison group, and all measures were made available to participating CHCs and other users. Results: Data was received for 12 quarters (January 2021 through December 2023) from the 50 CHCs on over 4.3 million encounters for 2.8 million patients. Data show a 48.7% relative improvement in the number of CHCs taking a BP reading at each patient encounter from Q1 2021-Q4 2023 (57.4% to 85.4%). Additionally, CHC BP control &lt;140/90 mm Hg and &lt;130/80 mm Hg reached 65.4% and 27.9%, respectively, in Q4 2023, as compared to the UDS reported 2023 BP control rate &lt;140/90 mm Hg of 60.9% for 350 NHCI CHCs. Conclusion: The expanded 18 measures allowed for more timely, frequent, detailed, and potentially actionable data to inform QI efforts. The timeliness and higher frequency of data should enable CHCs to understand the effects of their BP control strategies in real time. Using a population health management platform that emphasizes clinical performance measures based on “real-time” EHR data may strengthen and accelerate BP control QI and evaluation efforts.

Intelligent transportation systems (ITS) typically rely on massive sources of data to monitor the transportation system, estimate its current state, forecast its future state, and react with a set of control and management strategies in real time. This requires the prompt processing of a variety of data sources that is consistently changing with time and typically challenging to combine (and make sense of) in real time. This paper presents thoroughly the studies related to big data applications in ITS and its associated challenges.

This paper proposes two real-time feedback control strategies based on hourly measurements of effluent NH4-N and NOX-N concentrations. Using modified sigmoid functions to decide the DO setpoint, a control structure similar to the cascade-type control loop was selected as the real-time feedback NH4-N control strategy. For the realtime feedback NOX-N control strategy, a proper external carbon dose flow rate could be calculated based on the estimated NOX-N concentration in anoxic reactor by using the empirical equation. A control system, which included two proposed control strategies, was developed and applied in the pilot-scale A2/O process. As a result, the effluent NH4-N and NOX-N concentrations were maintained stably lower than the target values of 3 and 5 mg/L, respectively. Moreover, because the manipulated variables for removing the NH4-N and NOX-N concentrations were divided in the control strategies, the two different control strategies could be successfully applied together in the A2/O process.

Neural network-based H∞ sliding mode control for nonlinear systems with actuator faults and unmatched disturbances

Discrete sliding mode control for nonlinear systems using feedback linearization

Landing is daunting control design problem because it demands high accuracy and precision by pilot. Among the various states to control for stability and landing in an Aircraft, one is its altitude to handle during landing. Since the linearized model of aircraft is considered and is used for altitude control design. For best tracking performance during glide and flare phase, Integral State feedback Sliding Mode Control (ISSMC) is applied, which is based on unit vector approach. To eliminate error between responded altitude and referenced altitude, a dynamic demand first order filter is used. There has been noted a significant improvement in performance as compared with PID control. When comparing the Integral State feedback Sliding Mode Control with PID during landing phase, the ISSMC yields the best response of glide and flare maneuverability for smooth touch down during landing on runway.

This article presents a novel reinforcement learning (RL)-based control scheme for trajectory tracking of robot manipulators, incorporating an uncertainty estimator and an optimal tracking controller. A momentum observer (MO) is implemented in conjunction with an integral sliding mode control (ISMC) to facilitate uncertainty estimation for compensation. A proportional-derivative-like (PD-like) control plus an actor-critic neural network (NN)-based feedforward control is utilized for trajectory tracking. An NN parameter selection scheme is adopted to circumvent the time-consuming adjustments of its activation functions and initial weights, thereby ensuring the admissibility of the initial control policy and improving its efficiency. Lyapunov function analysis demonstrates stability of the closed-loop system, in which all error signals are shown to remain bounded and eventually settle into a small residual set. The proposed control scheme is compared with a conventional PD approach consisting of a feedforward controller and an NN controller with adaptive radial basis functions (RBFs). Comparison is also made with a recent approach featuring a feedforward super-twisting sliding mode control (FSTSMC). Simulation and experimental results show superior tracking performance of the presented control scheme against the comparative counterparts, validating the new approach and further supporting its effectiveness and feasibility.

In this paper, sliding mode controller (SMC) and integral sliding mode controller (ISMC) are designed for a variable speed wind turbine. The main objective of the controller is to optimize the energy captured from the wind at below rated power. To overcome the steady state error, the ISMC is introduced. However, in the presence of large uncertainties, ISMC control approach produces oscillatory phenomenon due to the higher needed gain. To eliminate the tradeoffs between the ISMC tracking performance and the high gain at the control input, we have introduced a new method based on the modification of the output tracking error through the use of the exponential function in order to eliminate the reaching phase. The stability of the proposed ISMC is analyzed by Lyapunov theory and the control action didn't show any chattering. The simulation results of ISMC are presented and the control performance is compared with conventional SMC.

This paper presents the real-time control strategies developed to regulate both the ammonia and nitrate concentration in the effluent of the new Vitoria WWTP (Spain). Nitrate control aims at the optimal use of the denitrification potential at any moment. For this purpose, the proposed control algorithm continuously adapts the internal recycle flow in order to maintain a desired nitrate set-point in the anoxic zone. Ammonia control aims at maintaining the required average concentration of ammonia in the effluent by manipulating the Dissolved Oxygen (DO) set-point. The control strategies have been based on a hierarchical structure where a high-level or supervisory control selects the set-point of the low-level or conventional controllers. The design of the controllers was carried out using the Quantitative Feedback Theory QFT for the design of robust control systems. Moving average values of some variables have been introduced in order to eliminate the perturbations associated with the daily 24-hour profiles. The controllers have been verified using long-time dynamic simulations based on a mathematical model previously calibrated in pilot plant. Influent load and temperature used in the simulations correspond to the real values measured in the full-scale WWTP during 12 months. The results obtained in the simulations show the good performance and stability of the control strategies independently from external disturbances. A short-time experimental verification of the controllers in pilot plant with real wastewater is also presented.

In this paper, we propose a lateral stability control strategy for four-wheel independent drive (4WID) electric vehicles. The control strategy adopts a hierarchical structure. First, a seven-degree-of-freedom (7DOF) 4WID electric vehicle model is established. Then, the upper controller adopts the integral sliding mode control (ISMC) method to obtain the desired yaw moment by controlling both the yaw rate and the sideslip angle. A new sliding mode reaching law (NSMRL) is designed to reduce chattering and make state variables converge faster, and the superiority of NSMRL is verified by theoretical analysis. The lower controller proposes a new optimal allocation algorithm, which selects the tire utilization rate and the standard deviation coefficient of the tire utilization rate as the objective function. The safety performance of vehicle is improved, and the instability caused by the significant difference in the stability margin between the four wheels under extreme road conditions is avoided. Finally, a simulation is carried out to verify the effectiveness of the proposed control strategy under single-lane-change and J-turn maneuvers.