Control Constraints Research Articles

Composite Observer-based Finite-time Command Filtered Adaptive Control for Full-state Constraints Nonlinear Systems

In this paper, we look at a type of strict-feedback nonlinear systems that have neural adaptive finite-time command filtered control and full-state constraints. To begin with, indeterminate nonlinear functions are estimated using the NN. The system’s capacity to detect unmeasurable states and disturbances is done while using a composite observer, which significantly boosts the system’s anti-interference performance. Next, the issue of “complex explosion” that arose in the backstepping technique is resolved by adding a modified fractional-order filter. In order to address the full-state constraint issues, the BLF is also introduced. An adaptive finite-time tracking controller is created using Lyapunov’s theory analysis to make sure that all signals are confined to the finite time period. Lastly, a simulation study is conducted to evaluate the performance of the suggested control strategy.

Semismoothness for Solution Operators of Obstacle-Type Variational Inequalities with Applications in Optimal Control

We prove that solution operators of elliptic obstacle-type variational inequalities (or, more generally, locally Lipschitz continuous functions possessing certain pointwise-a.e. convexity properties) are Newton differentiable when considered as maps between suitable Lebesgue spaces and equipped with the strong-weak Bouligand differential as a generalized set-valued derivative. It is shown that this Newton differentiability allows to solve optimal control problems with -cost terms and one-sided pointwise control constraints by means of a semismooth Newton method. The superlinear convergence of the resulting algorithm is proved in the infinite-dimensional setting, and its mesh independence is demonstrated in numerical experiments. We expect that the findings of this paper are also helpful for the design of numerical solution procedures for quasi-variational inequalities and the optimal control of obstacle-type variational problems.

Dynamic Optimization of Variable Load Process for Combined Heat and Power Unit Based on Sequential Quadratic Programming and Interior Point Method Alternating Solution Method

Aiming at the problem that the modeling and solving method of combined heat and power (CHP) unit variable load control process is challenging to meet the demand for efficient analysis of complex systems, this paper proposes a method based on sequential quadratic programming and interior point method (SQP-IPM) alternating solution for dynamic optimization of the CHP unit variable load process. Firstly, by constructing the CHP unit mechanism model, multi-variable coordination control constraints, and output variable process constraints, the dynamic optimization proposition of the CHP unit variable load control process is formed. Then, the large-scale nonlinear programming (NLP) problem formed by using the orthogonal configuration method to discrete the state and control variables is optimally solved using the IPM-SQP alternating solution method. Further, from the perspective of balancing the accuracy of the solution and computational efficiency, the flexible convergence depth control (CDC) strategy is introduced into the alternative solution method to improve the real-time performance of the algorithm. Finally, the variable load control process of 300MW extraction CHP unit is simulated to verify that the proposed method reduces the calculation time for 12 consecutive variable load scenarios by about 70%, effectively improving the real-time performance of scenario applications.

Foot trajectory following control of hexapod robot based on Udwadia–Kalaba theory

This paper provides an adaptive robust control strategy for foot trajectory following control of hexapod robot on basis of the Udwadia–Kalaba theory. In this paper, the foot trajectory following control problem of the hexapod robot is transformed into the problem of solving the system control constraint force on basis of the Udwadia–Kalaba theory. Compared with the traditional control strategy, linearization or approximations are not required by using the Udwadia–Kalaba theory for nonlinear system such as the hexapod robot. Due to modeling error, measurement error and the change of working state, the system may have non-ideal initial conditions, vibration interference and other uncertain factors during operation, which affect the control accuracy. An adaptive robust controller is designed for solving uncertainties. Meanwhile, the stability is analyzed by using the second method of Lyapunov function. Finally, the accuracy and stability of the control method proposed are verified by establishing the leg model of the hexapod robot and conducting simulation analysis. The simulation results show that the provided adaptive control process has faster error convergence speed and response speed compared with the sliding mode control method.

An energy storage allocation method for renewable energy stations based on standardized supply curve

The goal of carbon emission peak and carbon neutrality requires China to vigorously develop renewable energy. However, renewable energy has obvious randomness and volatility. Therefore, it is necessary to configure energy storage systems for renewable energy stations to ensure the safe and stable operation of power systems. Given the problem of energy storage system configuration in renewable energy stations, it is necessary to consider the system load characteristics and design appropriate principles to formulate planned output curves for renewable energy stations, so that the joint output curve of renewable energy and energy storage system can match the system load curve as much as possible, which will reduce the system peak shaving pressure and promote the utilization of renewable energy. This paper first considers the impact of renewable energy stations with the different installed scales on the power system and designs the standardized supply curves differentially, and defines the supply curve deviation index to characterize the difference between the renewable energy–energy storage system joint output curve and the standardized supply curve. Then, to minimize energy storage system investment costs and supply deviation costs, an optimization model for energy storage system configuration in renewable energy stations is established, and output deviation control constraints are set to ensure that the operation of energy storage systems conforms to actual conditions. Finally, case studies analyze the energy storage system configuration results and the typical scenario operation results of a single renewable energy station and a renewable energy power generation base, which verify the rationality and effectiveness of the method proposed in this paper.

A Geometrical, Reachable Set Approach for Constrained Pursuit–Evasion Games with Multiple Pursuers and Evaders

This paper presents a solution strategy for deterministic time-optimal pursuit–evasion games with linear state constraints, convex control constraints, and linear dynamics that is consistent with linearized relative orbital motion models such as the Clohessy–Wiltshire equations and relative orbital elements. The strategy first generates polytopic inner approximations of the players’ reachable sets by solving a sequence of convex programs. A bisection method then computes the optimal termination time, which is the least time at which a set containment condition is satisfied. The pursuit–evasion games considered are games with (1) a single pursuer and single evader, (2) multiple pursuers and a single evader, and (3) a single pursuer and multiple evaders. Compared to variational methods, this reachable set strategy leads to a tractable formulation even when there are state and control constraints. The efficacy of the strategy is demonstrated in three numerical simulations for a constellation of satellites in close proximity in low earth orbit.

Enforcement of separation of duty constraints in attribute-based access control

While the verification of separation of duty (SoD) constraints on attribute based access control (ABAC) systems has been defined and examined in existing works, it still remains an open problem since the existing approaches are either inefficient or inadequate to check all SoD constraints; furthermore, the problem of solving SoD violations has not yet been addressed in the literature. In this paper, we present an integer linear programming (ILP) based approach to check the satisfiability of all SoD constraints. It can be applied to different SoDs. Hence, it is feasible in real-world scenarios. Moreover, we present an ILP based violation solution that can correct all SoD violations with a minimum number of restrictions regarding users’ normal operations on target objects. All SoD violations can be solved in a once-and-done way while simultaneously guaranteeing the satisfaction of all SoD constraints.

A Note on Existence of Solutions to Control Problems of Semilinear Partial Differential Equations

In this paper, we study optimal control problems of semilinear elliptic and parabolic equations. A tracking cost functional, quadratic in the control and state variables, is considered. No control constraints are imposed. We prove that the corresponding state equations are well posed for controls in . However, it is well known that in the framework the mappings involved in the control problem are not Frechet differentiable in general, which makes any analysis of the optimality conditions challenging. Nevertheless, we prove that every optimal control belongs to , and consequently standard optimality conditions are available.

Property-Based Brittleness Analysis of Temporal Networks

A new framework is proposed to analyze the brittleness of task networks with respect to an arbitrary set of user-specified properties, such as dynamic controllability or minimum battery state of charge of a rover. The proposed algorithms allow the detection and enumeration of activities that, with modest task execution duration variation, make the success of execution no longer guaranteed in terms of the given set of properties. A metric for measuring an activity’s brittleness, defined as the degree of acceptable deviation from its nominal duration that preserves the desired user-specified properties, is introduced and how that measurement is mapped to task network structure is described. Complementary to existing work on temporal robustness analysis, which informs how likely a task network is to succeed or not under temporal controllability constraints, the new analysis and metric not only generalize robustness analysis to a set of user-defined properties (as opposed to strong or dynamic controllability, for instance), but they also go deeper to pinpoint the sources of potential brittleness. An analyzer is developed, which helps human designers and/or automated task network generators focus on and address sources of undesirable brittleness. The approach is applied in Mars rover and biomanufacturing operations scenarios.

Adaptive Trajectory Tracking Algorithm for the Aerospace Vehicle Based on Improved T-MPSP

To deal with the uncertainty and disturbance that exist in the tracking system of an aerospace vehicle, an adaptive trajectory-tracking method based on a novel tracking model predictive static programming (T-MPSP) is proposed. Firstly, to make the proposed method more adaptive to uncertain parameter deviations, an extended Kalman filter (EKF) parameter correction strategy is designed. Then, the control constraints are considered to form a novel T-MPSP algorithm. By combining the parameter correction strategy with the improved T-MPSP algorithm, a novel adaptive tracking guidance scheme is presented. Finally, simulations are carried out to demonstrate the effectiveness of the proposed method.

Path following using velocity-based approach in quasi-static analysis

In the present work, we propose a novel implementation of arc-length method for the computation of equilibrium paths for frame-like structures. We exploit the advantages of velocity-based beam formulations and adapt it to quasi-static analysis with path following control. We show that such formulation allows the arc-length control constraint equation to be directly employed in its originally defined differential form, while the arc-length parameter is replaced by time in our adaptation. This simplifies the treatment of the extended system of equations and its solution procedure. Our approach also enables the rotational degrees of freedom to be members of control parameters. The ability of the proposed method to successfully and efficiently compute complex equilibrium paths in post-buckling regime is demonstrated by several numerical examples.

An adaptive receding horizon-based flexible mode switching control strategy of parallel hybrid electric vehicles

Mode switching is crucial to efficient drive in variable driving conditions for hybrid electric vehicles (HEVs), thus many scholars are attracted to implement its high-quality control. The receding horizon control method is an attractive method to solve this problem. However, the choice of prediction horizon is arduous due to the contradiction between the optimization performance and the computational cost. Motivated by this issue, this paper proposes an adaptive receding horizon control (RHC) method, which contains optimization layer (OPL) and feedback control layer (FCL). First, an OPL adaptive receding horizon control method is proposed to obtain the optimal control command, in which a novel adaptive factor, considering the proportional between the maximum traction motor torque and powertrain demand torque, is proposed to adjust the prediction horizon length and control constraints. Then, regarding OPL output as the reference trajectory, a lower-RHC method is designed in FCL to track the reference trajectory. Finally, the proposed control method is validated in simulation and bench experiment. Compared with the existing method, the clutch trajectory is optimized by RHC with adaptive prediction horizon and constrains, and the anti-disturbance ability is enhanced by the lower-RHC. The simulation and bench experiment results indicate that the 0–40 km/h acceleration time and jerk are reduced using the proposed method, respectively.

Safety-Critical Control for Control Affine Systems under Spatio-Temporal and Input Constraints

Safety-critical control is a type of modern control task where potentially conflicting stability, safety, and input constraints coexist. In this paper, the Prescribed-Time Zeroing Control Barrier Function (PT-ZCBF) is introduced, which can be applied as a prescribed-time stability constraint in safety-critical control tasks. Furthermore, we formulate a PT-ZCBF-based Quadratic Program (QP), which is able to mediate the potentially conflicting constraints of safety-critical control. The solution of the newly designed QP, acting as the control input of a safety-critical system, can drive the closed-loop trajectories to converge in a user-defined prescribed time period while observing the safety and input constraints. Finally, we use the Adaptive Cruise Control (ACC) problem as an example of numerical simulation to evaluate the performance of the QP-based method.

Neural Networks Simulation of Distributed SEIR System

In this paper, a neural network-based optimal control synthesis is presented for distributed optimal control problems. We deal with solutions of systems controlled by parabolic differential equations with control and state constraints and discrete time delays. The given optimal control problem is transformed into a discrete nonlinear problem and then implemented into a feed-forward adaptive critic neural network. We propose a new algorithm to reach optimal control and an optimal trajectory using a feed-forward neural network. We present a concrete application of this simulation method on the SEIR (Susceptible—Exposed—Infectious—Recovered) optimal control problem of a distributed system for disease control. The results show that the adaptive-critic-based neural network approach is suitable for the solution of optimal distributed control problems with delay in state and control variables subject to control-state constraints and simulates the spread of disease in the SEIR system.

Photovoltaic powered operational scale Membrane Capacitive Deionization (MCDI) desalination with energy recovery for treated domestic wastewater reuse

An operational scale Membrane Capacitive Deionization (MCDI) desalination system was trialled for water reuse applications in Western NSW, Australia. The 6 electrode module system had an average current efficiency of 74 %, water recovery of 84 % (77 % including pre-treatment) and delivered 1.0 m3/h of treated water with an electrode energy consumption of 0.35 kWh/m3 (including energy recovery) and total energy consumption of 1.28 kWh/m3 (using mains power) or 1.05 kWh/m3 (using photovoltaics to power the electrodes). No performance decline was noted over an operational period of 3 months that included reverse current stopped flow desorption and regular cleaning with sodium hypochlorite. The use of photovoltaics for the electrode power supply enabled an average power saving of 27 %. The inclusion of energy recovery devices on the electrode control system facilitated a 40 % reduction in electrode energy consumption. The system demonstrated that MCDI desalination is a feasible option for treated domestic wastewater reuse and can be easily coupled with photovoltaics supply to the electrodes. As one of the first larger scale MCDI operations, we demonstrate that performance trends found in bench scale studies do translate to larger units, however control constraints of the operational units may require optimisation trade-offs between performance parameters.

Finite-time controllability and stabilization of probabilistic logical systems with state-dependent constraint via subset transition method

Constraints are very common for practical control systems. For logical systems, the existing technique of pre-feedback is an effective way of treating state-dependent constraints in control when the state is measurable. However, it is inapplicable for the case when measurement information is not available. In this situation, in order for the control input to not violate the state-dependent constraint, the control at each step must be selected from the common admissible controls of all possible states. Motivated by this observation, in this study, we propose a novel technique, termed the subset transition method, for finite-time controllability and stabilization of probabilistic logical dynamic control system (PLDCS) with a state-dependent control constraint. The main idea of this method is to construct an unconstrained deterministic logical control system over the power set of the state space, called the subset transition system (SubSTS), characterizing the transitional dynamics between subsets under common admissible controls. We prove that a control sequence is admissible with respect to all states in an initial subset if and only if it does not steer the SubSTS from the initial subset to the empty set. Based on this, necessary and sufficient conditions for set controllability and set stabilizability are obtained. Examples are presented to demonstrate the application of the obtained results.

Constraints Optimal Classical Continuous Control Vector Problem for Quaternary Nonlinear Hyperbolic System

This paper is concerned with the quaternary nonlinear hyperbolic boundary value problem (QNLHBVP) studding constraints quaternary optimal classical continuous control vector (CQOCCCV), the cost function (CF), and the equality and inequality quaternary state and control constraints vector (EIQSCCV). The existence of a CQOCCCV dominating by the QNLHBVP is stated and demonstrated using the Aubin compactness theorem (ACTH) under appropriate hypotheses (HYPs). Furthermore, mathematical formulation of the quaternary adjoint equations (QAEs) related to the quaternary state equations (QSE) are discovere so as its weak form (WF) . The directional derivative (DD) of the Hamiltonian (Ham) is calculated. The necessary and sufficient conditions for optimality (NCSO) theorems for the proposed problem are stated and proved.

A Passive-Constrained Observer and Controller Applied to an In-Wheel Synchronous Machine Implemented on a Fixed-Point DSP

In this paper, we describe a passivity-based control (PBC) approach for in-wheel permanent magnet synchronous machines that expands on the conventional passivity-based controller. We derive the controller and observer parameter constraints in order to maintain the passivity of the interconnected system and thus improve the control system’s robustness to varying loads and plant parameters. The advantages of the proposed PBC technique are demonstrated in contrast with conventional PBC. The control technique is implemented on an electronic development board equipped with a fixed-point digital signal processor and coupled to a gallium nitride voltage source inverter and 350 W electric machine. Test bench results and numerical simulations in Matlab-Simulink demonstrate the control performance and behaviour under varying loads and parameters.

Adaptive critic design for nonlinear multi-player zero-sum games with unknown dynamics and control constraints

Adaptive critic design for nonlinear multi-player zero-sum games with unknown dynamics and control constraints

Novel V2G regulation scheme using Dual-PSS for PV islanded microgrid

The incorporation of electric vehicles (EVs) into residential/industrial microgrids (MGs) presents some limitations such as fluctuations in voltage, frequency, and lack of power supply. According to the literature, the control methods for vehicle-to-grid (V2G) in MGs are insufficiently coordinated for charging and regulation, limited by narrow charging control constraints, complex, and computationally demanding. Moreover, the power system stabilizer (PSS) is employed only in the generation side. To this regard, a PSS is proposed in this paper along with the proportional-integral-derivative (PID) controller, which is tuned adaptively using Harris hawks optimizer (HHO) for frequency, voltage, and power-sharing regulations through the parked EVs. The considered islanded MG is consisting of a diesel generator as a backup generation source, dealing with the intermittent nature of the solar photovoltaic (PV) plant and residential load. It also includes an EV aggregator. In case of partial shading of the solar PV farm and triggering the load demand, the proposed control strategy provides a complementary signal to promote short response time during transients, boost the transient stability performance, and offer damping characteristics adequate for V2G regulation with the islanded residential MG. To examine the efficacy of the proposed scheme, a detailed simulation model using Matlab/Simulink was built. The final findings show that the proposed direct adaptive PID-PSS controller gives significantly enhanced voltage and frequency with spinning reserve compared to the conventional PI-controller both considering the inclusion of V2G on the MG regulation side and conventional controller without V2G and PSS on the regulation side. Also, it can provide power flow in a bi-directional way during the partial shading contingency that the residential MG may experience within 24 hr.