Direct Optimal Control Research Articles

EMG-Constrained and Ultrasound-Informed Muscle-Tendon Parameter Estimation in Post-Stroke Hemiparesis.

Secondary morphological and mechanical property changes in the muscle-tendon unit at the ankle joint are often observed in post-stroke individuals. These changes may alter the force generation capacity and affect daily activities such as locomotion. This work aimed to estimate subject-specific muscle-tendon parameters in individuals after stroke by solving the muscle redundancy problem using direct collocation optimal control methods based on experimental electromyography (EMG) signals and measured muscle fiber length. Subject-specific muscle-tendon parameters of the gastrocnemius, soleus, and tibialis anterior were estimated in seven post-stroke individuals and seven healthy controls. We found that the maximum isometric force, tendon stiffness and optimal fiber length in the post-stroke group were considerably lower than in the control group. We also computed the root mean square error between estimated and experimental values of muscle excitation and fiber length. The musculoskeletal model with estimated subject-specific muscle tendon parameters (from the muscle redundancy solver), yielded better muscle excitation and fiber length estimations than did scaled generic parameters. Our findings also showed that the muscle redundancy solver can estimate muscle-tendon parameters that produce force behavior in better accordance with the experimentally-measured value. These muscle-tendon parameters in the post-stroke individuals were physiologically meaningful and may shed light on treatment and/or rehabilitation planning.

A Direct Optimal Input Determination Data-Based Predictive Current Control for PMSM Drives Without System Identification

A Direct Optimal Input Determination Data-Based Predictive Current Control for PMSM Drives Without System Identification

Finite Elements with Switch Detection for direct optimal control of nonsmooth systems

This paper introduces Finite Elements with Switch Detection (FESD), a numerical discretization method for nonsmooth differential equations. We consider the Filippov convexification of these systems and a transformation into dynamic complementarity systems introduced by Stewart (Numer Math 58(1):299–328, 1990). FESD is based on solving nonlinear complementarity problems and can automatically detect nonsmooth events in time. If standard time-stepping Runge–Kutta (RK) methods are naively applied to a nonsmooth ODE, the accuracy is at best of order one. In FESD, we let the integrator step size be a degree of freedom. Additional complementarity conditions, which we call cross complementarities, enable exact switch detection, hence FESD can recover the high order accuracy that the RK methods enjoy for smooth ODE. Additional conditions called step equilibration allow the step size to change only when switches occur and thus avoid spurious degrees of freedom. Convergence results for the FESD method are derived, local uniqueness of the solution and convergence of numerical sensitivities are proven. The efficacy of FESD is demonstrated in several simulation and optimal control examples. In an optimal control problem benchmark with FESD, we achieve up to five orders of magnitude more accurate solutions than a standard time-stepping approach for the same computational time.

Fast Direct Optimal Control for Humanoids Based on Dynamics Representation in FPC Latent Space

Fast Direct Optimal Control for Humanoids Based on Dynamics Representation in FPC Latent Space

Determining Fast Battery Charging Profiles Using an Equivalent Circuit Model and a Direct Optimal Control Approach

This work used an electrical equivalent circuit model combined with a temperature model and computational optimal control methods to determine minimum time charging profiles for a lithium–ion battery. To effectively address the problem, an optimal control problem formulation and direct solution approach were adopted. The results showed that, in most cases studied, the solution to the battery’s fast-charging problem resembled the constant current–constant voltage (CC-CV) charging protocol, with the advantage being that our proposed approach optimally determined the switching time between the CC and CV phases, as well as the final time of the charging process. Considering path constraints related to the terminal voltage and temperature gradient between the cell core and case, the results also showed that additional rules could be incorporated into the protocol to protect the battery against under/over voltage-related damage and high-temperature differences between the core and its case. This work addressed several challenges and knowledge gaps, including emulating the CC-CV protocol using a multi-phase optimal control approach and direct collocation methods, and improving it by including efficiency and degradation terms in the objective function and safety constraints. To the authors’ knowledge, this is the first time the CC-CV protocol has been represented as the solution to a multi-phase optimal control problem.

Shaping the laser control landscape of a hydrogen transfer reaction by vibrational strong coupling. A direct optimal control approach.

Controlling molecular reactivity by shaped laser pulses is a long-standing goal in chemistry. Here, we suggest a direct optimal control approach that combines external pulse optimization with other control parameters arising in the upcoming field of vibro-polaritonic chemistry for enhanced controllability. The direct optimal control approach is characterized by a simultaneous simulation and optimization paradigm, meaning that the equations of motion are discretized and converted into a set of holonomic constraints for a nonlinear optimization problem given by the control functional. Compared with indirect optimal control, this procedure offers great flexibility, such as final time or Hamiltonian parameter optimization. A simultaneous direct optimal control theory will be applied to a model system describing H-atom transfer in a lossy Fabry-Pérot cavity under vibrational strong coupling conditions. Specifically, optimization of the cavity coupling strength and, thus, of the control landscape will be demonstrated.

Critical Review of Optimal Control Methods for Li‐Ion Batteries in Electric Vehicles

AbstractBattery management systems are important for the safe and efficient operation of electric vehicles. Although high hardware performance and effective configurations of batteries have been realized, a management algorithm is required for ensuring optimal system performance. This review focuses on optimal controllers for charging, thermal control, and cell balancing of electric vehicles. A potential approach for practical applications is the direct optimal control method, particularly model predictive control (MPC). The objective function, prediction model types, and manipulated variables are summarized, along with the computational performance. Typical nonlinear MPC, linear MPC, explicit MPC, and hierarchical MPC are the main formulations for the optimal control of EVs. The AI‐based approach learns the optimal control law as a function from the optimal control result data. Although few studies have applied the reinforcement approach to battery systems, additional safety considerations for constraints must be considered for real applications. Cell variations, aging factors, and uncertainty considerations have been analyzed for improving the controller design. Addressing the computational issue with a reliable optimizer is critical to the implementation of an optimal controller for EVs.

Successive Convexification for Nonlinear Model Predictive Control with Continuous-Time Constraint Satisfaction

We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluation of the running cost, without compromising computational efficiency. We leverage the recently proposed successive convexification framework for trajectory optimization, where: (1) the path constraints and running cost are equivalently reformulated by augmenting the system dynamics, (2) multiple shooting is used for exact discretization, and (3) a convergence-guaranteed sequential convex programming (SCP) algorithm, the prox-linear method, is used to solve the discretized receding-horizon optimal control problems. The resulting NMPC framework is computationally efficient, owing to its support for warm-starting and premature termination of SCP, and its reliance on first-order information only. We demonstrate the effectiveness of the proposed NMPC framework by means of a numerical example with reference-tracking and obstacle avoidance. The implementation is available at https://github.com/UW-ACL/nmpc-ctcs.

Dynamic and Nonlinear Programming for Trajectory Planning

Direct optimal control techniques, relying on numerical methods for constrained optimization, are typically used in trajectory planning tasks in high-dimensional spaces. However, general-purpose solvers often fail to find a feasible solution when facing cluttered environments. Sampling-or graph-based methods, instead, can explore complex configuration spaces but struggle with dynamic constraints. Here, we propose to combine dynamic programming (DP) and derivative-based methods to reliably solve trajectory planning problems. Specifically, we exploit DP to generate a sequence of waypoints in a lowdimensional space, which are then encoded as pointwise path constraints for a high-dimensional trajectory, whose constraint violations are then represented as a penalty within the Bellman equation to recompute the waypoints. This iterative approach, alternating path and trajectory optimization, avoids both the curse of dimensionality for DP and problematic nonconvexities (such as obstacles) for motion planning. We demonstrate our strategy using numerical experiments on a six-degree-of-freedom robotic manipulator moving in a confined space.

Biomechanical Trajectory Optimization of Human Sit-to-Stand Motion With Stochastic Motion Planning Framework

Trajectory optimization has been an important approach in biomechanics for the analysis and prediction of the limb movement. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions. However, the limitation of prevailing methods concerning differentiability hinders the implementation of non-differentiable objective functions such as metabolic energy expenditure (MEE) function, which is highly relevant for physiological systems and can even be implemented across the muscular space. This paper consolidates the implementation of the prevalent direct collocation-based optimal control method with the stochastic trajectory optimization method based on Policy Improvement with Path Integral (PI2) for comprehending the human sit-to-stand (STS) motion. PI2 method, which utilizes reinforcement learning of Dynamic Movement Primitive (DMP) to learn a goal-based trajectory is implemented and validated by comparing with the experimental result in joint-space and muscle-space.

Computational evaluation of psoas muscle influence on walking function following internal hemipelvectomy with reconstruction.

An emerging option for internal hemipelvectomy surgery is custom prosthesis reconstruction. This option typically recapitulates the resected pelvic bony anatomy with the goal of maximizing post-surgery walking function while minimizing recovery time. However, the current custom prosthesis design process does not account for the patient’s post-surgery prosthesis and bone loading patterns, nor can it predict how different surgical or rehabilitation decisions (e.g., retention or removal of the psoas muscle, strengthening the psoas) will affect prosthesis durability and post-surgery walking function. These factors may contribute to the high observed failure rate for custom pelvic prostheses, discouraging orthopedic oncologists from pursuing this valuable treatment option. One possibility for addressing this problem is to simulate the complex interaction between surgical and rehabilitation decisions, post-surgery walking function, and custom pelvic prosthesis design using patient-specific neuromusculoskeletal models. As a first step toward developing this capability, this study used a personalized neuromusculoskeletal model and direct collocation optimal control to predict the impact of ipsilateral psoas muscle strength on walking function following internal hemipelvectomy with custom prosthesis reconstruction. The influence of the psoas muscle was targeted since retention of this important muscle can be surgically demanding for certain tumors, requiring additional time in the operating room. The post-surgery walking predictions emulated the most common surgical scenario encountered at MD Anderson Cancer Center in Houston. Simulated post-surgery psoas strengths included 0% (removed), 50% (weakened), 100% (maintained), and 150% (strengthened) of the pre-surgery value. However, only the 100% and 150% cases successfully converged to a complete gait cycle. When post-surgery psoas strength was maintained, clinical gait features were predicted, including increased stance width, decreased stride length, and increased lumbar bending towards the operated side. Furthermore, when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. These results suggest that retention and strengthening of the psoas muscle on the operated side may be important for maximizing post-surgery walking function. If future studies can validate this computational approach using post-surgery experimental walking data, the approach may eventually influence surgical, rehabilitation, and custom prosthesis design decisions to meet the unique clinical needs of pelvic sarcoma patients.

Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study

The current body of sprinting biomechanics literature together with the front-side mechanics coaching framework provide various technique recommendations for improving performance. However, few studies have attempted to systematically explore technique modifications from a performance enhancement perspective. The aims of this investigation were therefore to explore how hypothetical technique modifications affect accelerative sprinting performance and assess whether the hypothetical modifications support the front-side mechanics coaching framework. A three-dimensional musculoskeletal model scaled to an international male sprinter was used in combination with direct collocation optimal control to perform (data-tracking and predictive) simulations of the preliminary steps of accelerative sprinting. The predictive simulations differed in the net joint moments that were left ‘free’ to change. It was found that the ‘knee-free’ and ‘knee-hip-free’ simulations resulted in the greatest performance improvements (13.8% and 21.9%, respectively), due to a greater knee flexor moment around touchdown (e.g., 141.2 vs. 70.5 Nm) and a delayed and greater knee extensor moment during stance (e.g., 188.5 vs. 137.5 Nm). Lastly, the predictive simulations which led to the greatest improvements were also found to not exhibit clear and noticeable front-side mechanics technique, thus the underpinning principles of the coaching framework may not be the only key aspect governing accelerative sprinting.

A Semidistributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections

In this article, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders. We formulate the problem using direct optimal control and exploit the structure to construct a semidistributed primal-dual interior-point algorithm to solve it by parallelizing most of the computations. Differently from standard distributed optimization algorithms, where the optimization problem is split, in our approach we split the linear algebra steps, such that the algorithm takes the same steps as a fully centralized one, while still performing computations in a distributed fashion. We analyze the communication requirements of the algorithm, and propose an approximation scheme which can significantly reduce the data exchange. We demonstrate the effectiveness of the algorithm in hard but realistic scenarios, which show that the approximation leads to reductions in communicated data of almost 99% of the exact formulation, at the expense of less than 1% suboptimality.

Optimal dosage strategies for filter aid filtration processes with compressible cakes

We establish optimal time-dependent dosage strategies for compressible filter aid materials, focusing on dead-end filter aid filtration processes operated in constant flow mode. To this end, we apply a direct optimal control approach by repeatedly evaluating a mathematical filtration model with filter aid concentration as the unknown time-dependent control function. Our algorithm iteratively constructs the sought-for trajectory to minimize a cost functional representing previously defined performance goals. We evaluate our optimal control approach for two different model formulations. In the first one, all impurity particles are separated by pure surface filtration on top of the existing filter cake, whereas in the second one, the model is extended toward depth filtration in the filter cake. Particularly for the latter, optimally controlled dosage strategies yield a significant performance improvement of up to 30% reduced filter aid consumption compared with the already optimized but constant filter aid dosage.

Intuitive LTI energy-maximising control for multi-degree of freedom wave energy converters: The PeWEC case

Energy-maximising wave energy conversion control strategies are commonly based upon direct optimal control theory, where the control problem is discretised and transcribed into a nonlinear programme, and a solution is found via numerical routines. Though appealing from an optimality viewpoint, the real-time application of such strategies to realistic (complex) wave energy systems, such as the PeWEC device, can become potentially challenging, due to its intrinsic multiple degree-of-freedom (DoF) nature. Furthermore, this pendulum-based system is not only multi-DoF in its nature, but also underactuated, i.e. only one mode, associated to the pendulum mechanism installed inside the wave-excited floating body, can be effectively actuated. We propose, in this paper, a set of four simple and intuitive energy-maximising controllers for the PeWEC system based, upon linear time-invariant (LTI) systems. We achieve this by deriving the so-called impedance-matching conditions for the PeWEC, and extending well-established LTI controllers, originally designed for fully actuated single-DoF systems, to this multi-DoF underactuated case. In particular, we explore, design, and synthesise both feedback, and feedforward configurations, making explicit emphasis in their main characteristics. Furthermore, we provide a performance assessment for each of the proposed controllers, showing their energy-maximising capabilities for the wave resource characterising the Mediterranean Sea.

Manipulating the dynamics of a Fermi resonance with light. A direct optimal control theory approach

Direct optimal control theory for quantum dynamical problems presents itself as an interesting alternative to the traditional indirect optimal control. The method relies on the first discretize and then optimize paradigm, where a discretization of the dynamical equations leads to a nonlinear optimization problem. It has been applied successfully to the control of a bistable system where the wavepacket had been approximated by a parameterized Gaussian, leading to a semiclassical set of equations of motion (A. R. Ramos Ramos, O. K\"uhn, Front. Phys. 9 (2021) 615168). Motivated by these results, in the present paper we extend the application of the method to the case of exact wavepacket propagation using the example of a generic Fermi-resonance model. In particular we address the question how population of the involved overtone state can be avoided such as to reduce the effect of intramolecular vibrational energy redistribution. A methodological advantage is that direct optimal control theory offers flexibility when choosing the running cost, since there is no need to compute functional derivatives and coupling terms as in the case of indirect optimal control. We exploit this fact to include state populations in the running cost, which allows their optimization.

Design of the landing guidance for the retro-propulsive vertical landing of a reusable rocket stage

Launcher reusability is the most effective way of reducing access to space costs, but remains a great technical challenge for the European aerospace industry. One of the challenges lies in the recovery GNC strategy and algorithms, in particular those of the powered-landing phase, which must enable a precise landing with low fuel margins and significant dispersions. While state-of-the-art solutions for Navigation and control problems can be applied, namely, hybrid Navigation techniques and robust control, for the powered descent guidance problem novel techniques are required to enable on-board optimization, that is necessary to achieve the landing accuracy required to recover the first stage of a launcher. This paper presents the GNC solution currently in development by DEIMOS Space for RETALT (Retro Propulsion Assisted Landing Technologies), an EU Horizon 2020 funded project for studying launch system re-usability technologies for different classes of vertical take-off vertical-landing vehicles. At first, the architecture of the GNC solution identified for the return mission of the launcher is presented. Then, the paper focuses on the landing phase guidance solution, whose performance is critical to enable the recovery and, therefore, the reusability of the launcher making use of retro-propulsion. The guidance strategy is based on direct optimal control methods via on-board optimization, which is necessary to satisfy the pinpoint landing requirement in a high uncertain dynamic system, such as a booster recovery mission. Online convex optimization and successive convexification are explored for the design of the guidance function. The proposed guidance solution was integrated and tested in a high-fidelity simulator and the performance was preliminary assessed. The guidance assessment allowed selecting the best algorithms to be further consolidated and integrated in an end-to-end GNC solution for the return mission.

Energy-maximising tracking control for a nonlinear heaving point absorber system commanded by second order sliding modes

Energy-maximising control has proven to be of fundamental aid in the pathway towards commercialisation of wave energy conversion technology. The WEC control problem is based upon the design of a suitable control law capable of maximising energy extraction from the wave resource, while effectively minimising any risk of component damage. A particularly well-established family of WEC controllers is based upon a composite structure, where an optimal velocity reference is generated via direct optimal control procedures, followed by a suitable tracking control strategy. This paper presents the design and synthesis of a second order sliding mode controller to attain a reference tracking for a wave energy system. The presented approach can inherently handle parameter uncertainty in the model, which is ubiquitous within hydrodynamic modelling procedures. Furthermore, the proposed sliding mode controller has relatively mild computational requirements, and finite-time convergence to the designed surface, hence being an ideal candidate for real-time energy-maximising control of WEC systems.

Direct optimal control for time-delay systems via a lifted multiple shooting algorithm

<p style='text-indent:20px;'>Aiming at efficient solution of optimal control problems for continuous nonlinear time-delay systems, a multiple shooting algorithm with a lifted continuous Runge-Kutta integrator is proposed. This integrator is in implicit form to remove the restriction of smaller integration step sizes compared with delays. A tangential predictor is applied in the integrator such that Newton iterations required can be reduced considerably. If one Newton iteration is applied, the algorithm has the same structure as direct collocation algorithms whereas derives a condensed nonlinear programming problem. Then, the solution of variational sensitivity equation is decoupled from forward simulation by utilizing the implicit function theorem. Under certain conditions, this function evaluation and derivative computation procedure is proved to be convergent with a global order. Complexity analysis shows that the computational cost can be largely reduced by this lifted multiple shooting algorithm. Then, parallelizable optimal control solver can be constructed by embedding this algorithm in a general-purpose nonlinear programming solver. Simulations on a numerical example demonstrate that the computational speed of multi-threading implementation of this algorithm is increased by <inline-formula><tex-math id="M1">\begin{document}$ 36\% $\end{document}</tex-math></inline-formula> compared with non-lifted one, and increased by a factor of <inline-formula><tex-math id="M2">\begin{document}$ 6.64 $\end{document}</tex-math></inline-formula> compared with traditional sequential algorithm; meanwhile, the accuracy loss is negligible.</p>

Intelligent optimal control of crushing process of solid materials

Information about the intelligent method of direct optimal control and the method of system thinking in computer modeling of crushing process is given, which shows four hierarchical structures in the analysis of the control object, i.e. crusher. Computer models of the processes in the crusher are built starting the analysis from a sub-object of a deep hierarchical level that is a piece of material. The computer model introduced into the microprocessor allows determining the required calculated concentration of the crushed product and offering automated system for intelligent control of the crushing process of solid materials.