Optimal Control Allocation Research Articles

Optimal attitude control allocation via the B-spline augmented virtual motion camouflage method

Instantaneous three-axis attitude control in many picoscale satellites is achieved by combining magnetic torque coils with other control devices such as thrusters. A B-spline augmented virtual motion camouflage method is presented to maximize the usage of magnetic torque coils and minimize that of thrusters, while calculating attitude guidance commands.

Flight Control with Optimal Control Allocation Incorporating Structural Load Feedback

Advances in sensors and avionics computation power suggest real-time structural load measurements could be used in flight control systems for improved safety and performance. A conventional transport flight control system determines the moments necessary to meet the pilot’s command while rejecting disturbances and maintaining stability of the aircraft. Control allocation is the problem of converting these desired moments into control effector commands. In this paper, a framework is proposed to incorporate real-time structural load feedback and structural load constraints in the control allocator. Constrained optimal control allocation can be used to achieve desired moments without exceeding specified limits on monitored load points. Furthermore, certain criteria can be minimized, such as loads on certain parts of the aircraft. Flight safety issues can be addressed by using system health monitoring information to change control allocation constraints during flight. The framework to incorporate structural loads in the flight control system and an optimal control allocation algorithm are described and demonstrated on a nonlinear simulation of a generic transport aircraft with flight dynamics and static structural loads.

Control allocation strategies for an electric vehicle with a wheel hub motor failure

Three fault-tolerant control strategies for electric vehicles with wheel hub motors are presented and compared, which are all based on the control allocation principle. The main objective is to maintain the directional stability of the vehicle in case of a component failure during high speed manoeuvres. Two simplified strategies that are suited for on-board implementation are derived and compared to an optimal control allocation strategy and a reference vehicle with a basic electronic stability control system. The occurring faults are considered to be in the electric high-voltage system that can arise in wheel hub motors. All three control allocation strategies show improved re-allocation of traction forces after a severe fault, and hence an improved directional stability. However, the performance of both simplified algorithms shows limitations in case of force demands outside the capabilities of the respective actuator. This work shows that vehicle safety is increased by the proposed fault-tolerant control strategies.

Nonlinear Proportional-Derivative Control Incorporating Closed-Loop Control Allocation for Spacecraft

A novel saturated proportional-derivative control law incorporated with closed-loop control allocation is proposed for spacecraft attitude stabilization in this paper. More specifically, a saturated proportional-derivative-based baseline nonlinear controller is designed to guarantee the globally asymptotic stability under control input/signal constraints. Then, a closed-loop constrained optimal control allocation scheme is employed to distribute the moments synthesized by the baseline controller over the redundant actuators in the presence of constraints due to actuator amplitude and rate constraints. The optimal control solution is to be found by penalizing the control allocation errors and rates using optimal quadratic programming algorithms. A significant feature of this work is that the asymptotic stability with the closed-loop control allocation is guaranteed theoretically. Simulation results are presented to illustrate the performance of the proposed scheme.

Dual-mode Control Allocation for Integrated Chassis Stabilization

A hierarchical control architecture for lateral stabilization in automotive vehicles is proposed in this paper. The proposed direct yaw stabilization strategy is based on the optimal coordination of two conventional stabilization systems (differential brake and traction torque transfer). Thanks to this approach, high stabilization capabilities are obtained while the impact on the longitudinal motion is reduced. The control architecture is a three layer structure composed of a high-level control, an optimal control allocation and a low-level control. The main idea consists in the use of a dual-mode control allocation formulation to handle the different operating modes of the wheel according to available actuators, i.e. conventional braking and/or torque transfer. The performance of the integrated control strategy proposed is highlighted through simulation results.

Adaptive optimal control allocation using Lagrangian neural networks for stability control of a 4WS–4WD electric vehicle

This study involves a layered vehicle dynamics control system, which is composed of an adaptive optimal control allocation method using Lagrangian neural networks for optimal distribution of tyre forces and the sliding mode yaw moment observer for robust control of yaw dynamics. The proposed optimal control allocation method eliminates the requirement of solving optimization problem in every time step and it is a convergent and stability guaranteed solution for the optimal tyre force distribution problem. The aim in the sliding mode yaw moment observer is to force the vehicle to track a reference vehicle dynamic behaviour by estimating the equivalent input extended disturbance, which is the required stabilizing virtual yaw moment. The proposed layered stability control scheme has been tested on a four-wheel drive–four-wheel steer electric Fiat Doblo Van, which is modelled in CarSim. Both the sliding mode disturbance observer and the optimal control allocation methods are the first known applications to the stability control problem of road vehicles.

Vehicle dynamics control with energy recuperation based on control allocation for independent wheel motors and brake system

This work proposes an optimal control allocation method for the brake system and the wheel motors of an electric vehicle. The realisation of the method is proposed for vehicle dynamics control and energy recuperation. The control allocation takes into account temperature of electric motors, SOC and voltage of battery, vehicle velocity, fault situations, wheel slip, and vehicle subsystem prioritisation depending on parameters of vehicle dynamics. To illustrate the functional properties of the control allocation method, the corresponding simulation study is performed for the straight-line braking and ‘sine with dwell’ cornering. The simulations use a number of mathematical models including the 14 DoF model of vehicle motion and the models of electric vehicle systems. The simulation results confirmed effectiveness of the proposed approach both for regenerative braking and vehicle stability.

Direct Yaw-Moment Control through Optimal Control Allocation Method for Light Vehicle

This paper presents a Direct Yaw-moment Control (DYC) strategy to prevent light vehicles from entering the unsteady state and improve the handling stability. A novelty of this work is the ability to achieve superior performance through the lower workload of the actuators by using the optimal control allocation method to distribute the active yaw moment. In the main-loop, the DYC controller is designed based on the classical PID algorithm with the yaw rate and sideslip angle feedback. Simulation tests are carried out on the conditions of sine steering and single lane change steering. Results indicate that the working potential of each actuator can be fully utilized and a significant improvement in handling stability can be achieved from the DYC system.

System stability improvement through optimal control allocation in voltage source converter-based high-voltage direct current links

Control of both active and reactive power in voltage source converter (VSC)-based high-voltage direct current (HVDC) links could be very effective for system stability improvement. The challenge, however, is to properly allocate the overall control duty among the available control variables in order to minimise the total control effort and hence allow use of less expensive converters (actuators). Here relative gain array and residue analysis are used to identify the most appropriate control loops avoiding possible interactions. Optimal allocation of the secondary control duty between the two ends of the VSC HVDC link is demonstrated. Active and reactive power modulation at the rectifier end, in a certain proportion, turns out to be most effective. Two scenarios, with normal and heavy loading conditions, are considered to justify the generality of the conclusions. Subspace-based multi-input–multi-output system identification is used to estimate and validate linearised state-space models through pseudo random binary sequence probing. Linear analysis is substantiated with non-linear simulations in DIgSILENT PowerFactory with detailed representation of HVDC links.

Fast and Global Optimal Energy-Efficient Control Allocation With Applications to Over-Actuated Electric Ground Vehicles

This paper presents a fast and global optimization algorithm for an energy-efficient control allocation (CA) scheme, which was proposed for improving the operational energy efficiency of over-actuated systems. For a class of realistic actuator power and efficiency functions, a Karush-Kuhn-Tucker (KKT)-based algorithm was devised to find all the local optimal solutions, and consequently the global minimum through a further simple comparison among all the realistic local minima and boundary values for such a non-convex optimization problem. This KKT-based algorithm is also independent on the selections of initial conditions by transferring the standard nonlinear optimization problem into classical eigenvalue problems. Numerical examples for electric vehicles with in-wheel motors were utilized to validate the effectiveness of the proposed global optimization algorithm. Simulation results, based on the parameters of an electric ground vehicle actuated by in-wheel motors (whose energy efficiencies were experimentally calibrated), showed that the proposed global optimization algorithm was at least 20 times faster than the classical active-set optimization method, while achieving better control allocation results for system energy saving.

A cooperative line-of-sight guidance law for a three-dimensional phantom track generation using unmanned aerial vehicles

A cooperative guidance law for a team of unmanned aerial vehicles is addressed to deceive air defense radar networks by generating a so-called phantom track. The problem is viewed as a line of sight guidance problem in which unmanned aerial vehicles track the line of sight of a phantom. A guidance law based on input–output feedback linearization is developed in order to deal with a situation in which the phantom condition is not satisfied. The resultant solution is obtained in a feedback form. Since the system is over-actuated, a control allocation optimization problem is formulated and solved by adding an additional objective term to the cost function that forces unmanned aerial vehicles to fly parallel to the phantom using a predictive control method. A closed-form solution is constructed for the unmanned aerial vehicles trajectory corresponding to the pre-specified phantom track. The proposed method is expanded through a scenario in which multiple unmanned aerial vehicles cooperatively generate a coherent phantom track toward a desired waypoint by engaging with the same number of radars. The performance of the proposed guidance law is demonstrated through a simulation study.

Delay Sensitive Communications over Cognitive Radio Networks

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to the dynamic resource availability induced by the licensed users' activities. In this paper, we derive the optimal admission control and channel allocation decisions in cognitive overlay networks to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performances with low complexities.

Utility Maximization with Packet Collision Constraint in Cognitive Radio Networks

SUMMARY We study joint rate control and resource allocation with a packet collision constraint that maximizes the total utility of secondary users in cognitive radio networks. We formulate and decouple the original optimization problem into separable subproblems and then develop an algorithm that converges to optimal rate control and resource allocation. The proposed algorithm can operate on different time-scales to reduce the amortized time complexity.

Optimal Topology Control and Power Allocation for Minimum Energy Consumption in Consensus Networks

Consensus algorithms have generated a lot of interest due to their ability to compute globally relevant statistics by only exploiting local communications among sensors. However, when implemented over wireless sensor networks, the inherent iterative nature of consensus algorithms may cause a large energy consumption. Hence, to make consensus algorithms really appealing in sensor networks, it is necessary to minimize the energy necessary to reach a consensus, within a given accuracy. We propose a method to optimize the network topology and the power allocation over each active link in order to minimize the energy consumption. We consider two network models: a deterministic model, where the nodes are located arbitrarily but their positions are known, and a random model, where the network topology is modeled as a random geometric graph (RGG). In the first case, we show how to convert the topology optimization problem, which is inherently combinatorial, into a parametric convex problem, solvable with efficient algorithms. In the second case, we optimize the power transmitted by each node, exploiting the asymptotic distributions of the eigenvalues of the adjacency matrix of an RGG. We further show that the optimal power can be found as the solution of a convex problem. The theoretical findings are corroborated with extensive simulation results.

Cascaded Control Allocation in Overactuated Aircraft via Mixed Optimization

This paper presents the principle and application of a class of cascaded optimal control allocation approach for overactuated aircraft. The design is based on mixed optimized targets consisting of two parts: the tracking error of virtual control and the effectors’ error related to the preferred control vector. At each step, the unconstrained least square problem is solved through simple calculation to obtain the optimal solution. If any optimal commands exceed the position limit or rate limit, a scheme is designed to choose at most one effector set to its limit and the remaining virtual controls are redistributed among other unsaturated control effectors. The Simulation based on the ADMIRE model shows that the effectiveness of the proposed approach comparing with Cascaded Generalized Inverse and Fixed-Point Method.

Nonnegative Matrix Inequalities and their Application to Nonconvex Power Control Optimization

Maximizing the sum rates in a multiuser Gaussian channel by power control is a nonconvex NP-hard problem that finds engineering application in code division multiple access (CDMA) wireless communication network. In this paper, we extend and apply several fundamental nonnegative matrix inequalities initiated by Friedland and Karlin in a 1975 paper to solve this nonconvex power control optimization problem. Leveraging tools such as the Perron–Frobenius theorem in nonnegative matrix theory, we (1) show that this problem in the power domain can be reformulated as an equivalent convex maximization problem over a closed unbounded convex set in the logarithmic signal-to-interference-noise ratio domain, (2) propose two relaxation techniques that utilize the reformulation problem structure and convexification by Lagrange dual relaxation to compute progressively tight bounds, and (3) propose a global optimization algorithm with -suboptimality to compute the optimal power control allocation. A byproduct of our analysis is the application of Friedland–Karlin inequalities to inverse problems in nonnegative matrix theory.

Control allocation and management of redundant control effectors based on bases sequenced optimal method

For an advanced aircraft, the amount of its effectors is much more than that for a traditional one, the functions of effectors are more complex and the coupling between each other is more severe. Based on the current control allocation research, this paper puts forward the concept and framework of the control allocation and management system for aircrafts with redundancy control effectors. A new optimal control allocation method, bases sequenced optimal (BSO) method, is then presented. By analyzing the physical meaning of the allocation process of BSO method, four types of management strategies are adopted by the system, which act on the control allocation process under different flight conditions, mission requirements and effectors working conditions. Simulation results show that functions of the control allocation system are extended and the system adaptability to flight status, mission requirements and effector failure conditions is improved.

Vehicle stabilisation in response to exogenous impulsive disturbances to the vehicle body

Multi-event traffic crashes typically result in a higher toll than single-event crashes do. One type of multi-event crash includes the case where the initial harmful event leads to a subsequent loss of directional control. Vehicle stabilisation countermeasures in response to exogenous impulsive disturbances are addressed here. Based on a dedicated collision model, a sensing scheme is proposed to detect the crash events. The stabilisation controller, developed from the model predictive supervisory control approach and optimal control allocation, is then activated to attenuate vehicle motions. This stabilisation scenario can be deemed as a functional extension to Electronic Stability Control (ESC) systems.

Admission control and resource allocation in a heterogeneous OFDMA wireless network

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

Power allocation in wireless multi-user relay networks

In this paper, we consider an amplify-and-forward wireless relay system where multiple source nodes communicate with their corresponding destination nodes with the help of relay nodes. Conventionally, each relay equally distributes the available resources to its relayed sources. This approach is clearly sub-optimal since each user experiences dissimilar channel conditions, and thus, demands different amount of allocated resources to meet its quality-of-service (QoS) request. Therefore, this paper presents novel power allocation schemes to i) maximize the minimum signal-to-noise ratio among all users; ii) minimize the maximum transmit power over all sources; iii) maximize the network throughput. Moreover, due to limited power, it may be impossible to satisfy the QoS requirement for every user. Consequently, an admission control algorithm should first be carried out to maximize the number of users possibly served. Then, optimal power allocation is performed. Although the joint optimal admission control and power allocation problem is combinatorially hard, we develop an effective heuristic algorithm with significantly reduced complexity. Even though theoretically sub-optimal, it performs remarkably well. The proposed power allocation problems are formulated using geometric programming (GP), a well-studied class of nonlinear and nonconvex optimization. Since a GP problem is readily transformed into an equivalent convex optimization problem, optimal solution can be obtained efficiently. Numerical results demonstrate the effectiveness of our proposed approach.