Control Allocation Technique Research Articles

Nonlinear PID Control for Automatic Docking of a Large Container Ship in Confined Waters Under the Influence of Wind and Currents

This study addresses the challenges of maneuvering a large container ship in confined waters under the influence of wind and currents. The proposed guidance, navigation, and control system presents a novel combination of modeling methodologies, control allocation techniques, and nonlinear control systems theory. We consider a docking scenario through a simulation of a realistic, narrow harbor accommodating area-specific currents and global wind forces. Control allocation is solved by utilizing detailed models of the actuators and hydrodynamics in a mixed-integer-like solution for optimal thrust allocation, and an inverse mapping to the control inputs. Furthermore, our approach introduces a modified Wageningen B-Series model, that is capable of modeling propellers with negative pitch dynamics. The complexity of safely docking in environments like the Antwerp harbor, where wind and currents pose significant risks, is tackled using a nonlinear PID control system integrated with Line-Of-Sight (LOS) guidance. This enables precise maneuvering of a fully actuated Roll-On/Roll-Of vessel to its docking position and orientation. Given that few studies on automated docking holistically address the intricate challenges of complex harbor environments and the concurrent impact of multiple external forces, our work proposes a comprehensive solution based on an integrated combination of guidance, navigation, and control systems.

Novel reaching law based predictive sliding mode control for lateral motion control of in‐wheel motor drive electric vehicle with delay estimation

AbstractThe lateral motion control of an in‐wheel motor drive electric vehicle (IWMD‐EV) necessitates an accurate measurement of the vehicle states. However, these measured states are always affected by delays due to sensor measurements, communication latencies, and computation time, which results in the degradation of the controller performance. Motivated by this issue, a novel reaching law based predictive sliding mode control (NRL‐PSMC) is proposed to maintain the lateral motion control of the IWMD‐EV subjected to unknown time delay. Initially, a PSMC framework is built, in which a predictor integrating with the sliding mode control is designed to eliminate the effect of time delay and generate the virtual control signals. Further, to alleviate the chattering phenomenon, a novel‐reaching law is developed, enabling the vehicle to track the desired states effectively. Subsequently, a dynamic control allocation technique is presented to optimally allocate the virtual control input to the actual control input. The accurate estimation of the aforementioned unknown delay is realized through a delay estimator. Finally, simulation and hardware‐in‐the‐loop experiments are performed for three specific driving manoeuvres, and the results demonstrate the effectiveness of the proposed controller design.

A multivariable super‐twisting‐based fault‐tolerant control with control allocation for uncertain systems

SummaryThis article contributes with a fault‐tolerant control (FTC) for uncertain systems under the effect of some possible time‐ and state‐dependent actuator faults and/or failures, and component faults, which are zero at the initial time. The proposed scheme is based on a continuous integral sliding‐mode control approach and a fixed control allocation technique. The integral sliding‐mode control strategy allows us to design a robust linear controller and a multivariable Super‐Twisting algorithm, which deals with the faults. The proposed scheme guarantees the fulfilment of an output regulation task, with an arbitrarily small regulation error, and does not require a fault diagnosis stage but full‐state availability. The performance of the proposed FTC is illustrated through some simulations on a benchmark B747 aircraft model.

Power Control and Fuzzy Pairing in V2X Communications

In this article, a joint power control and pairing-based resource allocation problem in the vehicle-to-everything (V2X) network are studied. This V2X network consists of a vehicle-to-infrastructure (V2I) link and a vehicle-to-vehicle (V2V) link. Therefore, the resource allocation problem has been formulated with the objective function of maximizing the V2I link's rate with the constraints of quality of service. Due to its NP hard nature, the original resource allocation problem is divided into two subproblems of power control and pairing. The power control subproblem is transformed into a convex problem using an exponential variable change. A fuzzy decision-making method is exploited to solve the pairing subproblem to have a highly reliable and stable V2V link. Then, the proposed joint power control and pairing-based resource allocation technique is evaluated with a simulated highway and compared with similar pairing algorithms. These performance comparisons reveal that the stability of the proposed resource allocation technique outperforms the other related methods.

Design and implementation of a fault-tolerant controller using control allocation techniques in the presence of actuators saturation for a VTOL octorotor

AbstractFault-tolerant control systems are vital in many industrial systems. Actuator redundancy is employed in advanced control strategies to increase system maneuverability, flexibility, safety, and fault tolerability. In this paper, a fault-tolerant control scheme is proposed to make an over-actuated octorotor robust, against actuators fault and saturation. A sliding mode observer is employed to determine the actuators condition. Then, a fault-tolerant control based on the control allocation methodology is proposed to distribute the control signals between the actuators by considering their condition. In a nonlinear system, an actuator fault can lead to the saturation of other actuators and steady-state errors that can cause closed-loop instability. Hence, the proposed control scheme corrects the actuator signals in a way that their limitations are considered. Finally, experimental studies are carried out and a comparison study is provided.

Control allocation based fault tolerant control of descriptor system with actuator saturation

This paper deals with the design of active fault-tolerant control (FTC) system based on control allocation (CA) technique for the uncertain descriptor system (DS) with actuator saturation. This work assumes that a fault detection unit is available to estimate the actuator fault. The proposed CA technique is responsible to redistribute the control effort to the healthy actuators taking the advantage of actuator redundancy of the DS. The so-called virtual control law is designed based on a variable gain super twisting sliding mode algorithm (VGSTSMA) to meet the admissibility criteria of continuous control input for the DS. This VGSTSMA based control strategy is designed based on the generalized regular form (GRF) of DS ensuring the robustness against the actuator faults, error in the fault estimation, and the external uncertainties. The CA scheme developed here is applicable to the DS even with a full rank input matrix which is different from the traditional design where it generally deals with the system with rank deficient input matrix. Furthermore, a static control redistribution mechanism is introduced to handle the actuator saturation which is responsible to re-allocate the excess control efforts to the available healthy actuators in the face of actuator saturation. Finally, a dual-pipe heat exchanger unit, modeled as DS, is simulated with proposed CA-based FTC such that the system can follow the time varying and constant reference trajectories in a situation of actuator faults and saturation. The simulation results are compared with the CA-based FTC where virtual control is designed based on super-twisting algorithm (STA) and with the traditional pseudo inverse-CA-based FTC to establish the superiority of the proposed scheme.

Fault-Tolerant Control with Control Allocation for Time-Varying Linear Systems by Using Continuous Integral Sliding Modes

A fault-tolerant control algorithm based on sliding modes is proposed to ensure the tracking of the desired trajectory for time-varying systems even in the presence of actuator faults. The proposed algorithm uses a continuous integral sliding mode and a linear quadratic regulator, together with control allocation and system inversion techniques, resulting in both a finite-time exact compensation of the faults and the exponential tracking of the reference.

Composite Learning Control of Overactuated Manned Submersible Vehicle With Disturbance/Uncertainty and Measurement Noise.

In this article, a novel composite learning control scheme based on nonlinear disturbance observer (NDOB), neural network (NN), and model-based state observer (MSOB) is investigated for the manned submersible vehicle. First, an MSOB is employed to reconstruct the real output signals from noise-contained measurements. Second, a composite estimation is developed where an NDOB is designed to estimate external disturbance and an NN is employed for model uncertainty. Furthermore, a control allocation technique is used to address the overactuated problem of the manned submersible vehicle. The rigorous stability analysis of the closed-loop manned submersible system is given via the Lyapunov theorem. Finally, several representative simulation results illustrate the superior control performance of the composite learning control scheme for the manned submersible vehicle.

Three-Dimensional Guidance with Terminal Time Constraints for Wide Launch Envelops

This paper proposes a three-dimensional, nonlinear guidance strategy for terminal-time-constrained interception of a nonmaneuvering target. The restrictive assumption of decoupling the three-dimensional interceptor–target engagement into two separate mutually orthogonal planes is relaxed. The guidance commands are derived using sliding mode control, with a time-to-go estimate that accounts for interceptor’s heading angle errors. It is shown that even for arbitrarily wide launch envelops, interception is guaranteed at a prespecified desired impact time. Further, a weighted control allocation technique is presented to allocate suitable lateral accelerations along the pitch and the yaw directions. Finally, numerical simulations, including extensive Monte Carlo assessments, are carried out with constant speed as well as two different realistic interceptor models that consider time-varying speed owing to aerodynamic parameter variations. Guidance strategy is shown to be effective in the presence of noisy measurements, uncertainties, and system lag. The performance of the proposed strategy is shown to be superior compared with an existing one.

A fractional-order sliding mode control for nominal and underactuated satellite attitude controls

Sliding mode control (SMC) is widely used in many existing nonlinear control solutions due to its capability against external disturbances and uncertainties, while the fractional order control (FOC) is employed as it can further enhance the control performance due to its robustness. This paper attempts to implement a fractional-order sliding mode control (FOSMC) for a small satellite with reaction wheels (RWs). In this work, a conventional SMC was initially designed to cope with the uncertainties of satellite attitude dynamics. In order to improve the attitude control performance, the FOSMC was designed accordingly and the classical chattering problem was alleviated by using the hyperbolic tangent function. This current work is the maiden work on FOSMC especially for small satellites using RWs. The FOSMC was also tested for a satellite with only two functional RWs, in which the control allocation technique is proposed to solve the underactuated satellite attitude control problem. Since the angular momentum of the reaction wheel will become saturated over time, it will be managed using the momentum unloading technique with the unique fuzzy proportional-integral (FPI) control. All control algorithms were numerically treated and analysed. The results show that the FOSMC is effective in achieving the overall desired attitude control performance for nominal and underactuated satellites.

An Automatic Collision Avoidance Algorithm for Multiple Marine Surface Vehicles

Abstract In recent years, unmanned surface vehicles have been widely used in various applications from military to civil domains. Seaports are crowded and ship accidents have increased. Thus, collision accidents occur frequently mainly due to human errors even though international regulations for preventing collisions at seas (COLREGs) have been established. In this paper, we propose a real-time obstacle avoidance algorithm for multiple autonomous surface vehicles based on constrained convex optimization. The proposed method is simple and fast in its implementation, and the solution converges to the optimal decision. The algorithm is combined with the PD-feedback linearization controller to track the generated path and to reach the target safely. Forces and azimuth angles are efficiently distributed using a control allocation technique. To show the effectiveness of the proposed collision-free path-planning algorithm, numerical simulations are performed.

A simple and efficient control allocation scheme for on-ground aircraft runway centerline tracking

To achieve a high performance level during ground operations, the lateral dynamics of an aircraft must be controlled using all available actuators (rudder, nose-wheel steering system, engines and brakes) and under various constraints, which gives rise to a challenging allocation problem. To address this issue, a simple yet accurate design-oriented on-ground aircraft model is first developed. It takes into account the effects of aerodynamics, thrust and tire–ground interactions, both laterally and longitudinally, and for several runway states. It is validated on a high-fidelity Airbus simulator and a complete set of numerical values representative of a commercial aircraft is given, as well as design objectives, so as to provide the control community with a challenging benchmark. After an extensive literature review and an evaluation of the pros and cons of many existing control allocation techniques, a novel and easily implementable algorithm is then developed, which meets actuator and implementation constraints. It automatically manages the trade-off between two antagonistic objectives, namely minimizing the control effort and attaining the maximum virtual control. Its validation on both the design-oriented model and the high-fidelity simulator shows promising results.

A new control allocation algorithm to improve runway centerline tracking at landing

Abstract To achieve a high performance level during ground operations, the lateral dynamics of an aircraft must be controlled using all available actuators (rudder, nose wheel steering system and brakes), which gives rise to a challenging allocation problem. After an extensive literature review and evaluation of the most promising techniques, a novel and easily implementable control allocation technique is developed, which meets actuator and implementation constraints. It automatically manages the trade-off between minimizing actuator use and attaining the maximum virtual control. It is tested on a realistic on-ground aircraft model, which has been previously validated against a high-fidelity Airbus simulator.

Connections between control allocation and linear quadratic control for weakly redundant systems

In this paper, connections between the control allocation and linear quadratic (LQ) control frameworks for optimally distributing control inputs in weakly input redundant systems are explored. It is also shown that, for a representative class of exogenous disturbance and reference signals, the LQ control technique is identical to the so-called optimal control subspace-based (OCS) control allocation technique. However, for this equivalence to hold, the OCS control allocation technique requires evaluation of a (generally) non-causal relationship between control inputs, while the LQ control technique requires perfect knowledge of the system, disturbance and reference states. In practice, neither of these conditions is achievable; therefore, approximations are needed. In this regard, the OCS control allocation technique is superior because it is explicit about the relationship that must be accurately approximated to attain optimality; this enables good approximations of the optimal relationship to be realized. Conversely, the LQ control technique implicitly approximates the optimal relationship via estimation of states, disturbances and/or reference signals. In a comparison with a classical example based on the Kalman filter, the OCS control allocation is shown to be superior in preserving the optimal alignment of redundant control inputs, thus introducing enhanced performance with reduced cost.

Control allocation-based adaptive control for greenhouse climate

ABSTRACTThis paper presents an adaptive approach to greenhouse climate control, as part of an integrated control and management system for greenhouse production. In this approach, an adaptive control algorithm is first derived to guarantee the asymptotic convergence of the closed system with uncertainty, then using that control algorithm, a controller is designed to satisfy the demands for heat and mass fluxes to maintain inside temperature, humidity and CO2 concentration at their desired values. Instead of applying the original adaptive control inputs directly, second, a control allocation technique is applied to distribute the demands of the heat and mass fluxes to the actuators by minimising tracking errors and energy consumption. To find an energy-saving solution, both single-objective optimisation (SOO) and multiobjective optimisation (MOO) in the control allocation structure are considered. The advantage of the proposed approach is that it does not require any a priori knowledge of the uncertainty bounds, and the simulation results illustrate the effectiveness of the proposed control scheme. It also indicates that MOO saves more energy in the control process.

Control Allocation for Reaction Thrusters of a Moon Lander using Linear Programming

Attitude control of the lunar lander is achieved through reaction control system (RCS). RCS works in pulsing mode and needs control allocation system which takes care of attitude tracking with minimum fuel consumption. Most of the control allocation techniques are based on pulse modulation technique, which are difficult to implement for RCS, where torques generated by RCS are generally coupled with each other. In this paper, eight unilateral pulsing mode attitude thruster for attitude correction of lunar module targeted for soft landing is presented. Linear programming based control allocation technique for RCS has been discussed, which gives fuel optimal solution with accurate tracking as a constraint. Landing specific simulation is done to validate the control system.

Control Allocation under Actuator Saturation: An Experimental Evaluation

Abstract This paper focuses on the comparison of control allocation (CA) techniques based on their application to a laboratory setup which emulates the rotational dynamics of a quadrotor. CA is an intermediate layer between controller and actuators which distributes the overall control effort in case of input redundancy. The CA algorithms Weighted Pseudoinverse, Redistributed Pseudoinverse, Enhanced Redistributed Pseudoinverse, Direct Allocation, Normalized Generalized Inverse, and Weighted Least Squares are briefly outlined and compared to each other. They are used to map the virtual torques generated by a second order sliding mode controller to the real actuators. Since in a real world application actuator power is always limited the performance of the methods in situations with multiple active constraints is investigated.

Nearly dynamic programming NN‐approximation–based optimal control for greenhouse climate: A simulation study

SummaryGreenhouse production must create a suitable growth environment for its crop to improve yield while minimizing the energy used to maintain the greenhouse environment, thereby reducing production cost. To this end, this paper develops a nearly optimal control approach based on adaptive dynamic programming. In this method, 3 neural networks are used to estimate the value function and control policy and to compensate for the unmodeled dynamics of the greenhouse climate. Taking into account the greenhouse system typically being an overactuated system, the total control efforts for heat, fog, and CO2 are considered as the virtual control inputs to be generated by the optimal controller. To obtain the real control inputs, a control allocation technique is introduced to distribute the virtual control inputs to the actuators. Finally, Lyapunov stability analysis is performed to derive the update law of the neural networks to ensure the asymptotic convergence of the closed‐loop system, and simulation is carried out to illustrate the effectiveness and control performance of the proposed approach.

An efficient DCA based algorithm for power control in large scale wireless networks

In recent years, power control and resource allocation techniques for cellular communication systems are very active research areas. Power control is typically used in wireless cellular networks in order to optimize the transmission subject to quality of service (QoS) constraints. One of the most popular power control problems is based on maximizing the weighted sum of data rates under the peak power constraints for all users. It is a difficult nonconvex optimization problem for which standard approach Geometric Programming is not applicable in large scale setting. In this paper, we propose an efficient method based on DC (Difference of Convex functions) programming and DCA (DC Algorithm), an innovative approach in nonconvex programming framework for solving this problem. The purpose is to develop fast and scalable algorithms able to handle large scale systems. The two main challenges in DC programming and DCA that are the effect of DC decomposition and the efficiency of solution methods to convex subproblems are carefully studied. The computational results on several datasets show the robustness as well as the efficiency of the proposed method in terms of both quality and rapidity, and their superiority compared with the standard approach Geometric Programming.

Reconfigurable dynamic control allocation for aircraft with actuator failures

ABSTRACTIn this paper, the development of a fault-tolerant control system for an aircraft that exploits both the hardware and analytical redundancy in the system is considered. A control allocation approach is developed where the total control command is computed and distributed among the available control surfaces. The actuator’s position and rate limits are taken into account in the optimisation problem. Existing fault-tolerant control allocation techniques produce look-up tables of control gains based on certain faults in the model. In contrast, the developed reconfigurable approach presented here incorporates a new process that redistributes control efforts which is updated whenever a fault occurs. In order to correlate between control effort redistribution and the fault magnitude, a fuzzy logic scheme is implemented, which handles a wide range of fault magnitudes on-line. The approach is applied for the most severe type of fault, which is the “lock-in-place” (jam) fault. Results show that the developed approach successfully handles the faulty situations and enhances aircraft flying responses by utilising the available healthy controls.