System Control Input Research Articles

Vehicle Door Opening Control Model Based on a Fuzzy Inference System to Prevent Motorcycle–Vehicle Door Crashes

The goal of this research is to develop a fuzzy logic-based vehicle door control system to avoid motorcycle–vehicle door crash accidents. Accidents of this nature usually occur when the driver has parked the car, opens the door getting out of the car and collides with a motorcycle approaching from the rear, causing injury to the motorcyclist. In order to prevent such accidents, the fuzzy logic control system inputs the speed (MS) and safety distance (SD) of the motorcycle approaching from the rear, and then the fuzzy inference unit (FIU) calculates the clear output (Crisp) defuzzification Vehicle Door Opening Model (VDOM) value for the central locking system of the car, which can be used to trigger three modes, namely Danger Mode, Caution Mode and Warning Mode. In this study, the VDOM system is designed to trigger reasonable, reliable and consistent door control under different speeds of motorcycles coming from the rear and will be effectively applied to the door control of semi-automatic cars in the future.

A tube-based robust MPC for duty-cycled rotation needle steering systems with bounded disturbances

In this paper, we aim to design the trajectory tracking controller for variable curvature duty-cycled rotation flexible needles with a tube-based model predictive control approach. A non-linear model is adopted according to the kinematic characteristics of the flexible needle and a bicycle method. The modeling error is assumed to be an unknown but bounded disturbance. The non-linear model is transformed to a discrete time form for the benefit of predictive controller design. From the application perspective, the flexible needle system states and control inputs are bounded within a robust invariant set when subject to disturbance. Then, the tube-based model predictive control is designed for the system with bounded state vector and inputs. Finally, the simulation experiments are carried out with tube-based model predictive control and proportional integral derivative controller based on the particle swarm optimisation method. The simulation results show that the tube-based model predictive control method is more robust and it leads to much smaller tracking errors in different scenarios.

Reachability problem considering running cost

AbstractIn this paper, we generalize the definition of reachable set. The classic definition of reachable set is a set of system states that can be driven into the target set within a given time horizon. Sometimes, the concern of researchers is not the time consumption but some other form of cost of driving the system state toward the target set. Under this background, the definition of cost‐limited reachable set is put forward in this paper. In this definition, the cost is the time integral of some form of running cost, and the running cost can be a function of system state and control input. By introducing a value function, a method to compute the cost‐limited reachable sets is proposed. In this method, the value function is approximated using recursion and interpolation. Then the cost‐limited reachable sets can be characterized by a family of level sets of the value function. At the end of this paper, some examples are taken to illustrate the validity and accuracy of the proposed method.

Global optimization control for nonlinear full‐car active suspension system with multi‐performances

In this article, based on feedback linearized approach and linear quadratic regulator optimization control approach using particle swarm optimization, the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization, controllable convergence rate, improved suspension and globally exponential stability multi-performances of highly nonlinear multi-input multi-output full-car uncertain system are simultaneously achieved without applying any nonlinear function approximator including neural network approach and fuzzy logic approach. The article proposes a new method to achieve the optimal control matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally determined via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This study has first proposed the convergence rate formula of the nonlinear system response as the fitness function of LQR approach by using PSO optimization algorithm to take the place of the conventional trial and error method and locally Jacobian linearized approach under the guarantee of globally exponential stability. The simulation results show how the full-car system can be controlled optimally and satisfactorily and confirm the superiority of the proposed method.

Prediction and extraction of tower controller commands for speech recognition applications

Air traffic controllers' (ATCos) workload often is a limiting factor for air traffic capacity. Thus, electronic support systems intend to reduce ATCos' workload. Automatic speech recognition can extract controller command elements from verbal clearances to deliver automatic input for air traffic control systems, thereby avoiding manual input. Assistant Based Speech Recognition (ABSR) with high command recognition rates and low error rates has proven to dramatically reduce ATCos’ workload and increase capacity in approach scenarios. However, ABSR needs accurate hypotheses on expected commands and accurate extractions of command annotations from utterance transcriptions to achieve the required performance. Based on the experience of implementation for approach control, a hypotheses generator and a command extractor have been developed for speech recognition applications regarding tower control communication to face current and future challenges in the aerodrome environment. Three human-in-the-loop multiple remote tower simulation studies were performed with 16 ATCos from Hungary, Lithuania, and Finland at DLR Braunschweig from 2017 to 2019. Roughly 100 h of speech with corresponding radar data were recorded. Around 6000 speech utterances resulting in 16,000 commands have been manually transcribed and annotated. Some parts of the data have been used for training prediction models and command extraction algorithms. Other parts were used for evaluation of command prediction and command extraction. The automatic command extractor achieved a command extraction rate of 96.7%. The hypotheses generator showed operational feasibility with a sufficiently low command prediction error rate of 7.3%.

AN INTERACTIVE ANALYSIS OF 3D TECHNOLOGY AND ITS VAST APPLICATION TO HUMAN

The word 3D is defined as three dimensional models that display a picture or item in a form that appears to be physically present with a designated structure. Three-dimensional (3D), can also describe any object that occurs on a three-axis Cartesian coordinate system, a Cartesian system is basically a fancy way of describing the X and Y axes which are horizontal and vertical axis, but the inclusive of the third axis Z which is make it to be 3D of which the Z axis represent the depth.3D interaction is a form of human-machine interaction where users are able to move and perform interaction in 3D space. Both human and machine process information where the physical position of elements in 3D space. The main features of these 3D applications are immersion, interactivity, and involvement. Immersion holds the user’s attention. Interactivity is related to how responsive the application is to user actions and Involvement has to do with engaging the user’s interest in the underlying activity.3D interaction techniques are selection and manipulation, navigation, system control and symbolic inputs. Its application is in the area of Exploring Complex Data, Visualizations with the Cubic Mouse, Multimodal Interfaces in VEs Multimodal interaction, and VEs for Design Education Architectural design. KEYWORDS:interaction,cartesian,information,application,interface,virtual,physical,technique,immersion,involvement,design,navigation and data.

Control of Vehicle’s Mixed Longitudinal and Lateral Stability with Engine Dynamics Using Super Twisting Control Algorithm

Vehicle stability control is one of the most important subjects in the control engineering field. Many research activities have been done to develop more comfort and safe travel for passengers. In this paper, vehicle mixed stability in longitudinal and lateral motion has been investigated. Four-wheel seven degrees of freedom model of vehicle is considered to extract the dynamic equations and closed-loop system simulation. Dugoff’s nonlinear model has been used to simulate the behavior of tires and road, and Cho’s engine model with two state variables has been used for vehicle power system simulation, so it makes the input torque to wheels to be more realistic. Because of the good robustness properties of sliding mode control, the second-order sliding mode with a super-twisting algorithm has been used for calculation of control inputs. This method is proved to be so appropriate and useful in the case of uncertainty in a complicated vehicle dynamic model and multiple disturbances in vehicle motion. Engine throttle angle and yaw moment have been considered as a longitudinal system and lateral system control inputs respectively. The longitudinal slip coefficient and yaw rate are considered as system output. Simulation results show the effectiveness of the proposed method.

Thermal energy constant temperature control system of building energy system based on dynamic analysis method

If the intelligent building?s indoor environment?s constant temperature is accurately controlled, the comfort of the building can be improved. When we perform constant temperature control, there will be large fluctuations in the supply air temperature, which results in the traditional methods that cannot control the temperature within a reasonable range. Therefore, the paper proposes an optimal control method for the indoor environment constant temperature of intelligent buildings. In the IoT environment, we integrate the multi-agent technology to design the temperature fuzzy control structure, determine the input and output variables of the intelligent building temperature control system and its fuzzy set, use the dynamic analysis method to modify the fuzzy rules, and integrate it with bilinear The control algorithm builds a dynamic temperature control model for intelligent buildings to maintain the indoor temperature at the set value when the supply air temperature fluctuates significantly. This method makes up for the shortcomings that the current system cannot adapt to the intelligent building environment changes. The simulation results show that compared with the traditional algorithm, the improved algorithm can significantly improve the robustness of the intelligent building constant temperature control, and the temperature control stability is vital.

Feedback Linearized Optimal Control Design for Quadrotor With Multi-Performances

Design of tracking controller for quadrotor is an important issue for many engineering fields such as COVID-19 epidemic prevention, intelligent agriculture, military photography and rescue nowadays. This study applies the feedback linearized method and linear quadratic regulator (LQR) method using particle swarm optimization (PSO) to analysis and stabilize the highly nonlinear quadrotor system without applying any nonlinear function approximator that includes neural network approach and fuzzy approach. The article proposes a new method based on the firstly proposed convergence rate formula to achieve the optimal weighting matrices of LQR such that the composite controller can reduce the amplitudes of system control inputs. Determination of the LQR tuning parameters is conventionally achieved via trial and error approach. In addition to being very troublesome, it is difficult to find the globally best tuning matrices with LQR method. This article firstly uses the convergence rate formula of the nonlinear system as the fitness function of LQR approach by using PSO to take the place of the trial and error method. The generalities and implications of proposed approach are globally valid, whereas the Jacobian linearized approach is locally valid due to the Taylor expansion theorem. In addition to these two major achievements, the significant innovation of the proposed method is to possess “simultaneously” additional performances including the almost disturbance decoupling, input amplitude reduction, tuning parameter optimization and globally exponential stability performances. Comparative examples show that the convergence rate with our proposed optimal controller using the PSO algorithm is larger than the fuzzy method, and better than the singular perturbation method with high-gain feedback.

Quasi‐LPV PI control of TRMS subject to actuator saturation

This study is concerned on the design of a quasi-linear parameter varying (qLPV) proportional–integral (PI) controller for twin rotor multiple-input multiple-output systems (TRMS). The non-linear model is represented as a qLPV polytopic plant with an affine dependence on a non-linear parametric function of the pitch angle. This representation retains the exact model as opposed to the conventional linearisation around an operating point. Due to the availability of the pitch angle measurement, the non-linear parameter can be obtained in real-time and the controller is designed using qLPV technique. To deal with limited control input for such systems, the proposed controller design also considers the actuator saturation that yields controller with practical gains without any additional gain bound criterion. Further, the transient tracking performance is also considered in the design by using closed-loop eigenvalues assignment in desired damping regions. The control synthesis problem is formulated in the form of linear matrix inequalities for L 2 gain based performance criterion. The designed controller is validated on a two-degree of freedom helicopter experimental setup. Finally, to demonstrate the effectiveness of the proposed design, a comparative analysis is done with the existing algorithms. Also, the efficacy of the decentralised controller vis-a-vis the centralised one is presented.

Robust motion trajectory optimization of overhead cranes based on polynomial chaos expansion

In this paper, we present a polynomial chaos-based framework for the trajectory optimization of an overhead crane system under uncertainty. The main research described in this paper is as follows. First, the deterministic trajectory optimization problem formulation of a two-dimensional overhead crane model is constructed. Based on this basic mathematical formulation, the uncertainty trajectory optimization problem is formed considering the uncertainty of initial state and system parameter. Then, to solve the uncertainty trajectory optimization problem efficiently, a robust trajectory optimization problem formulation is proposed. However, it is difficult to solve the robust trajectory optimization problem directly because it contains stochastic function terms, such as stochastic dynamic equations, constraint functions and objective functions. We consider both the system state and control input as functions of uncertainty and use polynomial chaos expansion to quantify these stochastic functions. An augmented deterministic trajectory optimization problem which can be solved directly is finally obtained. Based on the proposed robust trajectory optimization formation, the motion trajectory optimization of an overhead crane system under two different uncertainty types of is solved. All simulation results are compared with traditional sampling-based Monte Carlo simulations to demonstrate the feasibility and effectiveness of the proposed method.

CASCADE CONTROL OF A MULTI INPUT AND MULTI OUTPUT (MIMO) THERMAL CONTROL SYSTEM

Cascade control of a “Multi Input and Multi Output (MIMO) Thermal Control System” is developed for better speed, better reduction of noise, lower overshoot. The thermal object consists of 4 thermally connected modules (three with a heater and one with a cooler), which is a MIMO object – 4 inputs and 4 outputs. The control system is realized with CPU and contains developing of PID control, Autotune system, history data.

A Multiple-node Hormone Regulation of Neuroendocrine-PID (MnHR-NEPID) Control for Nonlinear MIMO Systems

This paper proposed a multiple-node hormone regulation neuroendocrine-proportional-integration- differential (MnHR-NEPID) controller based on adaptive safe experimentation Dynamics (ASED) for nonlinear multi-input-multi-output (MIMO) systems. In the existing neuroendocrine-PID (NEPID) controller of the single-input-single-output (SISO) system, only a single node of hormone regulation is used due to a single control variable. Meanwhile, in the MIMO system, since having many control variables, it is worth introducing an MnHR-NEPID for better controller performance by prioritizing each node's control regulation from their level of error. In particular, instead of considering its own hormone regulation, each node's hormone regulation is also generated based on the change of error from other control variables or nodes if the error of that corresponding control variable exceeds the given error threshold. Here, the relation between hormone regulation and the change of error is adopted based on the normalized Gaussian function. As a result, better prioritize control regulation with heightened control accuracy can be subsequently achieved due to interactions between multiple nodes of hormones available for the nonlinear MIMO system. The performance error and control input for several nonlinear MIMO systems were further tracked to assess the proposed controller's performance. Standard PID, NEPID, and sigmoid-based secretion rate neuroendocrine-PID (SbSR-NEPID) controllers were also compared. Thus, this simulation work has acknowledged higher accuracy within the design of an MnHR-NEPID controller, with comparatively superior objective function and total norm of error resulted in better control performance.

Controlling Anesthesia Hardware With Simple Hand Gestures: Thumbs Up or Thumbs Down?

Modern consumer electronic devices and automobiles are often controlled by interfaces that sense physical gestures and spoken commands. In contrast, patient monitors and anesthesia devices are typically equipped with panel-mounted buttons, dials, and keyboards. The increased use of noncontact gesture-based interfaces in anesthesia may improve patient safety through more intuitive and prompter control of equipment and also through reduced rates of surface contamination. A novel gesture-based controller was designed and retrofitted to a standard GE Solar 8000M patient monitor. This type of technical innovation is rare, due to closely held proprietary input control systems on commercially produced clinical equipment. Nevertheless, we hypothesized that anesthesiologists would find a contactless gesture interface straightforward to use. A gesture-based interface system was developed to control a Solar 8000M patient monitor using a millimeter-wave radar sensor. The system was programmed to detect noncontact "rotate" and "press" gestures to control the patient monitor by implementing a virtual trim knob for interface control. Fifty anesthesiologists tested a prototype interface and evaluated usability by completing a short questionnaire incorporating modified Likert scales. These evaluations were performed in a nonpatient care environment so that respondents were not adversely task loaded during assessment, also allaying any ethical or safety concerns regarding use of this novel interface for patient management. Anesthesia hardware was controlled reliably with 2 distinct gestures above the gesture sensor. The gesture-based interface generally was well received by anesthesiologists (8.09; confidence interval, 8.06-8.12 on a 10-point scale), who preferred the simpler "press" gesture to the "rotate" gesture (8.45; 8.39-8.51 vs 7.73; 7.67-7.79 on a 10-point scale; P = .005). The correlation between the preference scores for the 2 gestures from each anesthesiologist was strong (Pearson r = 0.49; 0.25-0.68; P < .001). Advancing level of training (resident, fellow, attending 1-10 years, attending >10 years) was not correlated with preference scores for either gesture (Spearman ρ = -0.02; -0.30 to 0.26; P = .87 for "press" and Spearman ρ = 0.08; -0.20 to 0.35; P = .58 for "rotate"). The use of gesture sensing for controlling anesthesia equipment was well received by a cohort of anesthesiologists. Even though the simpler "press" gesture was preferred over the "rotate" gesture, the intrarespondent correlation indicates that the preference for gestures as a whole is the stronger effect. No adverse relationship was found between acceptability and anesthesia experience level. Gesture sensing is a promising new area to simplify and improve the interaction between the anesthesiologist and the anesthesia workstation.

Optimal control of timed event graphs with resource sharing and output-reference update

Abstract Timed event graphs (TEGs) are a subclass of timed Petri nets that model synchronization and delay phenomena, but not conflict or choice. We consider a scenario where a number of TEGs share one or several resources and are subject to changes in their output-reference signals. Because of resource sharing, the resulting overall discrete event system is not a TEG. We propose a formal method to determine the optimal control input for such systems, where optimality is in the sense of the widely adopted just-in-time criterion. Our approach is based on a prespecified priority policy for the TEG components of the overall system. It builds on existing control theory for TEGs, which exploits the fact that, in a suitable mathematical framework (idempotent semirings such as the max-plus or the min-plus algebra), the temporal evolution of TEGs can be described by a set of linear time-invariant equations.

Trajectory tracking control of agricultural vehicles based on disturbance test

To improve the trajectory tracking robust stability of agricultural vehicles, a path tracking control method combined with the characteristics of agricultural vehicles and nonlinear model predictive control was presented. Through the proposed method, the path tracking problem can be divided into two problems with speed and steering angle constraints: the trajectory planning problem, and the trajectory tracking optimization problem. Firstly, the nonlinear kinematics model of the agricultural vehicle was discretized, then the derived model was inferred and regarded as the prediction function plant for the designed controller. Second, the objective function characterizing the tracking performance was put forward based on system variables and control inputs. Therefore, the objective function optimization problem, based on the proposed prediction equation plant, can be regarded as the nonlinear constrained optimization problem. What’s more, to enhance the robust stability of the system, a real-time feedback and rolling adjustment strategy was adopted to achieve optimal control. To validate the theoretical analysis before, the Matlab simulation was performed to investigate the path tracking performance. The simulation results show that the controller can realize effective trajectory tracking and possesses good robust stability. Meanwhile, the corresponding experiments were conducted. When the test vehicle tracked the reference track with a speed of 3 m/s, the maximum lateral deviation was 13.36 cm, and the maximum longitudinal deviation was 34.61 cm. When the added horizontal deviation disturbance Yr was less than 1.5 m, the controller could adjust the vehicle quickly to make the test car return to the reference track and continue to drive. Finally, to better highlight the controller proposed in this paper, a comparison experiment with a linear model predictive controller was performed. Compared to the conventional linear model predictive controller, the horizontal off-track distance reduced by 36.8% and the longitudinal deviation reduced by 32.98% when performing circular path tracking at a speed of 3 m/s. Keywords: path tracking, nonlinearity, controller, robustness DOI: 10.25165/j.ijabe.20201302.4506 Citation: Liu Z D, Zheng W X, Wang N, Lyu Z Q, Zhang W Z. Trajectory tracking control of agricultural vehicles based on disturbance test. Int J Agric & Biol Eng, 2020; 13(2): 138–145.

Adaptive control designs for control-based continuation in a class of uncertain discrete-time dynamical systems

This article proposes a methodology for integrating adaptive control with the control-based continuation paradigm for a class of uncertain, linear, discrete-time systems. The proposed adaptive control strategies aim to stabilize the closed-loop dynamics with convergence toward a known reference input, such that the dynamics approach the open-loop fixed point if the reference input is chosen to make the steady-state control input equal 0. This enables the tracking of a parameterized branch of open-loop fixed points using methods of numerical continuation without specific knowledge about the system. We implement two different adaptive control strategies: model-reference adaptive control and pole-placement adaptive control. Both implementations achieve the desired objectives for the closed-loop dynamics and support parameter continuation. These properties, as well as the boundedness of system states and control inputs, are guaranteed provided that certain stability conditions are satisfied. Besides, the tuning effort is significantly reduced in the adaptive control schemes compared with traditional proportional–derivative controllers and linear state-space feedback controllers.

Eigenvalue assignment enabled control law for multivariable nonlinear systems with mismatched uncertainties

This paper presents an adaptive filtering output feedback control architecture for multivariable nonlinear systems with mismatched uncertainties enabled by an eigenvalue assignment method. A piecewise constant adaptive law updates the adaptive parameters which represent the uncertainty estimates by solving the error dynamics between the output predictor and the real system with the neglection of unknowns. By employing a computationally efficient eigenvalue assignment method, the multivariable nonlinear system is transformed into Frobenius canonical form. A novel filtering control law which allows the desired system to be nonminimum-phase and does not require dynamic inversion of the desired system is designed to compensate the nonlinear uncertainties and track a given trajectory, following a performance determined by the eigenvalues assigned to the controller. The uniform performance bounds are derived for the system state and control input as compared to the corresponding signals of a bounded virtual reference system, which defines the best performance that can be achieved by the closed-loop system. Numerical examples are provided to illustrate the effectiveness of the eigenvalue assignment enabled control law, comparisons between the proposed controller and funnel controller are carried out.

Adaptive nonlinear disturbance observer-based control for stochastic systems with multiple heterogeneous disturbances

The problem of anti-disturbance control is studied for a class of stochastic systems with multiple heterogeneous disturbances, which include three kinds of disturbance. One is the non-harmonic disturbance coupled with system state and control input. The other one is an unexpected nonlinear signal described as a nonlinear function. The third one is white noise. An adaptive nonlinear disturbance observer (ANDO) is constructed to estimate non-harmonic disturbance. Based on which, a new adaptive nonlinear disturbance observer-based control (ANDOBC) strategy is developed such that the composite system is asymptotically bounded in mean square. Simulation results are given to show its effectiveness of the proposed method.

Mean Square Stabilization of Multi-Input Discrete-Time Systems Over Stochastic Multiplicative and Additive White Gaussian Noise Channels

This paper studies mean square stabilization for multi-input discrete-time systems over a general fading channel, and the channel is modeled as a cascade of multiplicative noise and white Gaussian additive noise. The main objective is to determine the minimum mean capacity required to enable stabilization. The basic idea of our method is to consider stabilization from the viewpoint of a supply/demand balance. Specifically, for communication resources, each system control input is viewed as the demand side, while the channel is viewed as the supply side, and the supply resource of the channel is characterized by the mean square capacity of each channel. Stabilization of the networked control system requires the balance of supply and demand. Based on whether the channel resources are configurable, two different methods for balancing the supply and the demand are discussed. If the channel resources are configurable, the demand side can be satisfied by adjusting the supply side (channel resources); otherwise, the demand side (a certain transceiver design mechanism) can be adjusted to meet the requirements of the supplier. For both cases, sufficient and necessary conditions for stabilizing discrete-time networked control systems are given.