Control Objective Function Research Articles

Compensated Pulse Response Analysis of Ship Motor in Marine Environment Based on Electromagnetic Theory

Li, X.; Yu, M., and Hou, H., 2020. Compensated pulse response analysis of ship motor in marine environment based on electromagnetic theory. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 382–386. Coconut Creek (Florida), ISSN 0749-0208.In the geomagnetic environment, due to the existence of electromagnetic field in sea water, the vibration and time slot disturbance error of the ship's motor are caused, which affects the ship's pulse response. Firstly, the controlled object is described, and the control constraints are analyzed according to the receiving coil of the marine motor, and the control objective function is constructed to realize the control of the motor. According to the control objective function, the time slot interference error of the marine motor is compensated, and the impulse response of the motor is obtained. The BLT equation is established by applying the theory of electromagnetic topology, and the parameters of the equation are set. The scattering matrix, propagation matrix and excitation matrix of BLT equation are solved, and the compensation pulse response is analyzed. The experimental results show that the distribution of the whole magnetic field and the local magnetic field is balanced.

Research on multi-core DSP cooperative control system in environmental protection equipment

Traditional multi-servo motor control systems have the problems of poor output stability in environmental protection equipment, weak robustness, and low control ability. Based on this, the research of multi-servo motors control systems based on FPGA are proposed. The paper Construct a parameter distribution models on multi-servo motor control, using motor terminal voltage, current, and back-EMF as control constraints, calculate the difference between the output currents of the multi-servo motors, and construct a saturation function to analyze the multi-servo motor control objective function. Sampling phase current signal and PWM duty cycle as modulation parameters, combined with fuzzy adaptive PID control method to control the output robustness of multiple servo motors, calculating the equivalent mechanical power and equivalent electromagnetic power of multiple servo motors. The optimized parameter adjustment method performs self-tuning processing of the motor output, and the transient stability adjustment of the multi-servo motor is performed based on the method of the average terminal voltage analysis to realize the optimal design of the multi-servo motor control law. The test results show that the output stability of the output of the multi-servo motor control system using this method is better, the motor control is more robust and the control ability is higher.

Model predictive control method of a parallel electromagnetic isolation system based on the improved genetic algorithm

To improve the vibration isolation performance for a parallel electromagnetic isolation system, an improved genetic algorithm to optimize the Q and R matrices in the control objective function for a model predictive control approach is proposed. In this study, a parallel electromagnetic isolation system with two electromagnetic isolation units is designed to expand the vibration isolation range to isolate the large object. The dynamical equation and state equation of the parallel electromagnetic isolation system are built. The nonlinear relationship among electromagnetic force, coil current, and gap is calculated by COMSOL Multiphysics to design the model predictive control controller. Meanwhile, an improved genetic algorithm by the variable chromosome length coevolutionary method is presented to tackle two issues. The first issue is that the parameters of Q and R matrices in the control objective function are mainly selected by trial and error. The other issue is that the model predictive control approach needs to determine prediction steps which may lead to the model predictive control approach suffering from heavy computation or an inaccurate prediction model. Simulation and experimental results demonstrate that the parallel electromagnetic isolation system with model predictive control method based on the improved genetic algorithm can achieve better vibration isolation performance in comparison with the passive isolation system.

Ex-post Analysis of the WEC Control Competition Results Using a Fourier Spectral Control Approach

The wave energy converter control competition (WECCCOMP) allowed several real-time control approaches to be assessed, both in numerical and physical experiments. The solution retained by IFP Énergies Nouvelles (IFPeN), which won the numerical simulation and experimental evaluation phases, consists of a receding-horizon MPC algorithm, including an estimator and a predictor for the wave excitation torque. The control objective function, solved by a quadratic programming (QP) optimiser in the real-time implementation, is weighted over the receding time horizon by means of weighting coefficients, which are optimised off-line for each sea state, in order to take into account the non-ideal power take-off (PTO) efficiency. Given the potential complexity of the interaction between the different components involved in the control implementation (estimation, prediction, QP solution, choice of weightings), it is useful to carry out an ex-post analysis, in order to understand if, and how, the solution proposed by IFPEN could have been improved. To that end, a Fourier spectral control algorithm is implemented, which is able to calculate the optimal trajectory and control torque for the totality of a signal, simulated from WeCCCOMP sea states, taking the non-ideal PTO efficiency into account. By comparing MPC results with the theoretically optimal solutions provided by the spectral method, it is found that, in the studied WECCCOMP sea states, the IFPeN MPC algorithm performance lies within approximately 10% of the optimal solution, in terms of electric power.The influence of the MPC forecast accuracy and prediction horizon is examined. Finally, somechallenges associated with the offline MPC weighting optimisation are identified.

Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor

Aiming at the needs of ultrasonic motor motion control, a new two-dimensional (2D) predictive control objective function is proposed. Different from the existing methods, the objective function consists of three terms, including the product of the control quantity and the error of the previous control process. Based on the objective function, the predictive iterative learning control (ILC) law is derived by using the design method of generalized predictive control (GPC) without specifying ILC law form in advance. An on-line identification method for model parameters is given to realize effective identification under a small amount of data circumstances, and therefore, the parameters of controller are adjusted adaptively according to the identification results. The proposed control method is validated both in simulation and experiment. The experimental results show that the proposed predictive iterative learning control strategy can obtain better control effect than GPC, and has more obvious characteristics of iterative learning control. It can maintain the expected performance under the condition of intermittent loading and replacing the motor. It presents strong robustness.

Flow Topology During Multiplexed Particle Manipulation Using a Stokes Trap

Trapping and manipulation of small particles underlies many scientific and technological applications. Recently, the precise manipulation of multiple small particles was demonstrated using a Stokes trap that relies only on fluid flow without the need for optical or electric fields. Active flow control generates complex flow topologies around suspended particles during the trapping process, yet the relationship between the control algorithm and flow structure is not well understood. In this work, we characterize the flow topology during active control of particle trajectories using a Stokes trap. Our results show that optimal control of two particles unexpectedly relies on flow patterns with zero or one stagnation points, as opposed to positioning two particles using two distinct stagnation points. We characterize the sensitivity of the system with respect to the parameters in the control objective function, thereby providing a systematic understanding of the trapping process. Overall, these results will be useful in guiding applications involving the controlled manipulation of multiple colloidal particles and the precise deformation of soft particles in defined flow fields.

Simulation of Grasping Attitude Control of Robotic Arm Based on Synovial Error Feedback

In order to improve the attitude stabilisation of robotic arms during grasping, a method of grasping attitude control of the robotic arm based on synovial error feedback is proposed. Based on it, the attitude parameter information of the robotic arm is measured and fused by using multi-sensor fusion information acquisition method to construct a constraint parameter model and control objective function for grasping attitude control of the robotic arm. The experimental results show that the proposed method has high control precision in the attitude control of the robot arm, and can accurately track the position information of the object to be captured in real time, realise zero error capture, and the parameter tracking performance is good, which improves the grip on the robot arm. Taking the control quality of the attitude, the overall has an application advantage.

Research on security trust measure model based on fuzzy mathematics

Abstract The existing safety measurement model has the disadvantages of poor reliability and stability. In order to improve the reliability of the security trust measurement structure, a fuzzy trust-based security trust measurement model is proposed. The hierarchical fuzzy transmission network is used to establish the mathematical model of the controlled object, and the control objective function is analyzed. The regression analysis model is combined with the convergence constraint function, and the fuzzy adaptive learning is performed according to the security trusted computing output weight, and the parameter analysis is performed. And adjust; on this basis, establish a mathematical model of safety confidence measurement, and perform parameter feedback adjustment and reliability control to improve the reliability of the security trust measurement structure. The simulation results show that the proposed method has good reliability and steady-state convergence, and can effectively improve the reliability calculation ability of the safety confidence measure. It has strong application advantages.

DETECTING FINANCIAL SUSTAINABILITY RISK OF THE ASSETS USING MAMDANI FUZZY CONTROLLER

The paper aims to develop a MAMDANI fuzzy controller for detecting the financial sustainability risk of the assets owned by the company. This type of risk indicates when an asset no longer produces economic benefits to the company, or the benefits are small enough to no longer justify the asset maintaining in working order. The proposed fuzzy controller has as input variables the asset operating expenses and the variation of this category of expenses from one analysis period to another. The controller's objective function is to keep operating costs at their initial state and thus reducing the financial sustainability risk. The controller's output variable is represented by the economic benefits variation, considered to be an essential component in the financial sustainability risk analysis. The obtained results were interpreted taking into account the objective function of the controller as well as the evolution of the input variables. Two simulations for fuzzy controllers were made, with the mention that the variation ranges for the input variables were delimited. In practice, fuzzy controllers can be generated according to company policies to keep under control the expense categories that accompany the asset exploitation.

State–space approach to adaptive fuzzy modeling for financial investment

This paper proposes a new adaptive learning framework for fuzzy system under dynamically changing environment. Especially, a state–space model with filtering algorithm, traditionally used for the estimation of unobservable state variables, is applied to online non-linear optimization problems by reinterpreting control variables and objective function as state variables and observation model, respectively. Our proposed methodology substantially improves the flexibility of the objective function, which enables to construct the adaptive fuzzy system achieving arbitrarily designed user’s objective. In addition, time-series structure is actively introduced into the parameter transition, whose proper modeling is expected to enhance the performance. Particularly, the introduction of mean-reversion process makes it possible to adaptively learn model parameters around specific predetermined levels obtained by existing learning methodologies. As an application of adaptive learning fuzzy system for financial investment, the current work focuses on the construction of the target return replication portfolio. Concretely, the target return is specified as zero floored market index considering investor’s great demand to construct a portfolio with restricted downside risk. The validity of our framework is shown by out-of-sample numerical experiments with the data of well-known high liquid instruments such as S&P500 and TOPIX, which indicates the robustness and reliability of our proposed method in practice.

Analytical design of constraint handling optimal two parameter internal model control for dead-time processes

This work presents an advanced and systematic approach to analytically design the optimal parameters of a two parameter second-order internal model control (IMC) filter that satisfies operational constraints on the output process, the manipulated variable as well as rate of change of the manipulated variable, for a first-order plus dead time (FOPDT) process. The IMC parameters are designed to minimize a control objective function composed of the weighted sum of the error between the process variable and the set point, and the rate of change of the manipulated variable, and to satisfy the desired constraints. The feasible region of the constrained IMC control parameters was graphically analyzed, as the process parameters and the constraints varied. The resulting constrained IMC control parameters were also used to find the corresponding industrial proportional-integral controller parameters of a Smith predictor structure.

Application of explicit model predictive control to a vehicle semi-active suspension system

The vehicle semi-active suspension is a typical multiple-input multiple-output system with strong couplings, actuator constraints and fast dynamics. This paper addresses the damping force regulation of shock-absorber in vehicle semi-active suspensions using an explicit model predictive control (EMPC) approach, which allows minimizing the system control objective function while satisfying the actuator constraints. The main advantage of the proposed approach is that the control law computation requirement is low, and thus the EMPC system is suitable for implementation in a standard automotive microcontroller. The design of the EMPC system consists of mathematical modeling, objective function determination, controller formulation and simulation validation. Presented simulation results verify that a superior control performance of the vehicle semi-active suspension system is achieved by the proposed EMPC control approach compared with the performance obtained using conventional control method.

Conceptual Design of an Integrated Autonomous Production Control Model in association with a Prescriptive Maintenance Model (PriMa)

Autonomous production control models facilitate coping with co-occurrent and ever-growing production system complexity. The choice of the appropriate control model and objective function in manufacturing industry depends on several interrelated factors such as machine and process flexibility, plant availability, process stability, resource utilization, environmental and economic sustainability. Numerous theoretical models and approaches for autonomous production control have been proposed. Yet, it is hard to determine the effectiveness of efforts in real-life applications, due to neglecting or incomplete consideration of the aforementioned factors. This paper concentrates on the correlative relation between production planning and control (PPC) and maintenance planning. An integrated approach is presented that establishes the link between autonomous production control and prescriptive maintenance model (PriMa). Traditionally, PPC and maintenance planning are carried out apart from one another. In the light of PriMa, various aspects of data-driven maintenance planning and the missing link with autonomous production control are discussed. To this end, this paper presents a conceptual model, which encompasses three sub-systems, namely i) maintenance system, ii) autonomous production control system, and iii) production planning. The proposed model is intended to reduce planning complexity and at the same time achieve an improved plant availability and resource efficiency.

Optimization of Full Trajectory Pitch Tracking Control for Low Altitude Penetration of UAV

The low altitude penetration stage of UAV is easily affected by parameter perturbation, which leads to poor anti-damage in the process of penetration. In order to improve the stability of low altitude penetration of UAV, a full trajectory pitching control algorithm based on adaptive dynamic feedback tracking and parameter fusion is proposed. The control objective function of low altitude penetration of UAV is constructed at the constraint cost of minimum damage probability, and the parameters such as pitch angle, steering angle and acceleration are used as the control constraint parameters, and the adaptive learning training for tracking control of the whole trajectory is carried out. The dynamic inverse feedback parameter adjustment model of low altitude penetration is established, the whole trajectory control variable is qualitatively treated, the equivalent mathematical model of anti-interception of UAV is constructed, and the yaw angle is corrected by adaptive dynamic feedback tracking method. The whole trajectory steady-state tracking control of UAV with low altitude penetration is realized. The simulation results show that the algorithm has good steady-state property and strong yaw correction ability in the whole trajectory pitch tracking control of UAV at low altitude penetration stage, which improves the ability of anti-damage in low-altitude penetration stage, it has good control stability and robustness.

Separable and Localized System-Level Synthesis for Large-Scale Systems

A major challenge faced in the design of large-scale cyber-physical systems, such as power systems, the Internet of Things or intelligent transportation systems, is that traditional distributed optimal control methods do not scale gracefully, neither in controller synthesis nor in controller implementation, to systems composed of a large number (e.g., on the order of billions) of interacting subsystems. This paper shows that this challenge can now be addressed by leveraging the recently introduced system-level approach (SLA) to controller synthesis. In particular, in the context of the SLA, we define suitable notions of separability for control objective functions and system constraints such that the global optimization problem (or iterate update problems of a distributed optimization algorithm) can be decomposed into parallel subproblems. We then further show that if additional locality (i.e., sparsity) constraints are imposed, then these subproblems can be solved using local models and local decision variables. The SLA is essential to maintain the convexity of the aforementioned problems under locality constraints. As a consequence, the resulting synthesis methods have $O(1)$ complexity relative to the size of the global system. We further show that many optimal control problems of interest, such as (localized) LQR and LQG, $\mathcal {H}_2$ optimal control with joint actuator and sensor regularization, and (localized) mixed $\mathcal {H}_2/\mathcal {L}_1$ optimal control problems, satisfy these notions of separability, and use these problems to explore tradeoffs in performance, actuator, and sensing density, and average versus worst-case performance for a large-scale power inspired system.

Model predictive control scheme for coordinated voltage control of power systems at the presence of volatile wind power generation

This study proposes an optimal voltage control scheme to deal with long-term voltage stability of power systems. The control of voltage is accomplished by model predictive control (MPC) scheme. The control objective function considers the control efforts and the difference between the predicted and reference voltages. Also, this study considers the detailed non-linear dynamic model of the system including doubly-fed induction generator wind turbines, over excitation limiter and under-load tap changer, which are important elements, should be considered in voltage stability evaluation of power systems. The proposed approach composed of the following two major stages at each time instance: first, the power system non-linear dynamic equations are linearized and optimal control laws are obtained by MPC technique;in the second stage, the system dynamic behavior is investigated via time-domain simulations by applying the attained optimal control signals at the first step. The proposed MPC-based voltage control scheme is implemented on a well-known test system, under variable wind speed and fault conditions. Also, this method's performance is compared with state feedback control technique. The obtained numerical results validate the capability of the proposed control scheme to preserve voltage stability at the presence of stochastic wind speed variations and severe disturbances.

Signal Progression Model for Long Arterial: Intersection Grouping and Coordination

Signal progression has been proven as an effective way to improve the operational efficiency of traffic signals at local arterial corridors. Conventional two-way progression models have shown their promising in providing desirable green bandwidth to two-way through traffic along the arterial. However, they may not offer an effective progression plan when a long arterial contains many intersections. Under such condition, it is critical to divide the arterial corridor into a set of subgroups for progression design. Since progression effectiveness is significantly impacted by the way an arterial is decomposed, conducting arterial decomposition as a separated step may keep the result from optimality. To tackle this issue, a novel progressive model is developed to concurrently determine the arterial decomposition strategy and optimize the resulting signal progression plan within each subgroup. With an integrated control objective function, the proposed model can minimize the required number of subgroups while satisfying the operational need (i.e., a minimum bandwidth is required). Also, the proposed model is formulated with a mixed-integer-linear-programming technique that can guarantee a global optimal solution. A numerical example on a field arterial which consists of 15 signalized intersections has verified the effectiveness of the proposed model.

Forecast-Based Anticipatory Frequency Control in Power Systems

The imbalance of load and generation can be significant when large amounts of renewable generation are being integrated in power systems. If not dealt effectively, this uncertainty can result in excessive deviations of frequency and tie-line flows. A model predictive control method, termed anticipatory frequency control, is used with predicted disturbances (e.g., from solar or wind generation) within the conventional automatic generation control (AGC) framework to minimize system frequency and tie-line deviations. The controller is shown to reduce the frequency and tie-line deviations over time in a multiarea test power system when compared with conventional control methods. In addition, the impact of imperfect forecasts and controller objective function weighting on the performance of anticipatory control is carefully examined.

UAV Flight Stability Control Method Based on Steady - state Error Correction0

UAV flight stability control system is a multivariable, non - linear, strongly coupled system, the steady-state error is easy to occur with the traditional fuzzy control method. An UAV flight stability control method is proposed based on steady - state error correction, the steady state dynamic model of UAV flight is constructed, and the control constraint model is constructed with the roll angle, the pitch angle, the longitudinal inclination angle and the acceleration as the constraint parameters. The construction of UAV flight stability control objective function is obtained, and the linearization treatment is carried out near the equilibrium point of the flight of the unmanned aerial vehicle, and the equivalent control law of the sliding mode surface is constructed. Based on the conventional synovial surface, an integral term of tracking error is added to reduce the steady-state error and realize adaptive correction of flight parameters. The simulation results show that the stability of the flight control of the UAV is better by the method, and the self - adaptive correction capability of the flight state parameters is strong.

Finite-horizon inverse optimal control for discrete-time nonlinear systems

In this note, we consider the problem of computing the parameters (or weights) of an optimal control objective function given optimal closed-loop state and control trajectories. We establish a method of inverse optimal control that exploits the discrete-time minimum principle. Under a testable matrix rank condition, our proposed method is guaranteed to recover the unknown objective-function parameters of finite-horizon discrete-time nonlinear optimal control problems.