Control Input Vector Research Articles

Harnessing Deep Learning of Point Clouds for Morphology Mimicking of Universal 3D Shape‐Morphing Devices

Shape‐morphing devices, a crucial branch in soft robotics, hold significant application value in areas like human–machine interfaces, biomimetic robotics, and tools for biological systems. To achieve 3D programmable shape morphing (PSM), the deployment of array‐based actuators is essential. However, a critical knowledge gap in 3D PSM is controlling the complex systems formed by these soft actuator arrays to mimic the morphology of the target shapes. This study, for the first time, represents the configuration of shape‐morphing devices using point cloud data and employing deep learning to map these configurations to control inputs. Shape Morphing Net (SMNet), a method that realizes the regression from point cloud to high‐dimensional control input vectors, is proposed. It has been applied to 3D PSM devices with three different actuator mechanisms, demonstrating its universal applicability to inversely reproduce the target shapes. Further, applied to previous 2D PSM devices, SMNet significantly enhances control precision from 82.23% to 97.68%. In the demonstrations of morphology mimicking, 3D PSM devices successfully replicate arbitrary target shapes obtained either through 3D scanning of physical objects or via 3D modeling software. The results show that within the deformable range of 3D PSM devices, accurate reproduction of the desired shapes is achievable.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear Optimal Control for a PMLSG-VSC Wave Energy Conversion Unit

This article aims to treat the nonlinear control problem for the complex dynamics of a wave energy unit (WEC) that consists of a Permanent Magnet Linear Synchronous Generator (PMLSG) and a Voltage Source Converter (VSC). The article has developed a globally stable nonlinear optimal control method for this wave power generation unit. The new method avoids complicated state-space model transformations and minimizes the energy dispersion by the control loop. A novel nonlinear optimal control method is proposed for the dynamic model of a wave energy conversion system, which includes a Permanent Magnet Linear Synchronous Generator (PMLSG) serially connected with an AC/DC three-phase voltage source converter (VSC). The dynamic model of this renewable energy system is formulated and differential flatness properties are proven about it. To apply the proposed nonlinear optimal control, the state-space model of the PMLSG-VSC wave energy conversion unit undergoes an approximate linearization process at each sampling instance. The linearization procedure relies on a first-order Taylor-series expansion and involves the computation of the system’s Jacobian matrices. It takes place at each sampling interval around a temporary operating point, which is defined by the present value of the wave energy conversion unit’s state vector and by the last sampled value of the control inputs vector. An H-infinity feedback controller is designed for the linearized model of the wave energy conversion unit. To compute the feedback gains of this controller, an algebraic Riccati equation is repetitively solved at each time step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis.

Nonlinear optimal control for the multi-variable tumor-growth dynamics

The multivariable tumor-growth dynamic model has been widely used to describe the inhibition of tumor-cells proliferation under the simultaneous infusion of multiple chemotherapeutic drugs. In this article, a nonlinear optimal (H-infinity) control method is developed for the multi-variable tumor-growth model. First, differential flatness properties are proven for the associated state-space description. Next, the state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instant around a time-varying operating point which is defined by the present value of the system’s state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller’s gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. Finally, the performance of the nonlinear optimal control method is compared against a flatness-based control approach.

Nonlinear optimal and multi-loop flatness-based control of induction motor-driven desalination units

In this article, the control problem for the induction-motor driven reverse osmosis in desalination units is solved with the use of (i) a nonlinear optimal control method (ii) a flatness-based control approach which is implemented in successive loops. To apply method (i) that is nonlinear optimal control, the dynamic model of the induction-motor driven reverse-osmosis desalination units undergoes approximate linearization at each sampling instant with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization point is defined by the present value of the system’s state vector and by the last sampled value of the control inputs vector. To compute the feedback gains of the optimal controller an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. The global stability properties of the nonlinear optimal control method are proven through Lyapunov analysis. To implement control method (ii), that is flatness-based control in successive loops, the state-space model of the induction-motor driven reverse-osmosis desalination unit is separated into three subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the preceding (ith) subsystem become virtual control inputs for the subsequent (i+1th) subsystem. In turn, exogenous control inputs are applied to the first and third subsystem. The whole control method is implemented in three successive loops and its global stability properties are also proven through Lyapunov stability analysis. The proposed method achieves stabilization of the induction-motor-driven reverse osmosis desalination unit without the need of diffeomorphisms and complicated state-space model transformations.

Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight path. To cope with various wind conditions, this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the inner-loop. Since the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint, a scaling mechanism is introduced to tune the proportions of the compensation and nominal components. Moreover, an optimization problem is formulated to pursue a maximum wind compensation in strong winds, which can be solved analytically to yield two scaling factors. A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector field. High-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises, achieves promising accuracy in normal winds, and mitigates the deviation from a desired path in wild winds.

Nonlinear optimal control for permanent magnet synchronous spherical motors

Nonlinear optimal control for permanent magnet synchronous spherical motors

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

A nonlinear optimal control approach for voltage source inverter-fed three-phase PMSMs

Nonlinear optimal control for the rotary double inverted pendulum

AbstractThe control problem of the rotary double inverted pendulum (double Furuta pendulum) is nontrivial because of underactuation and strong nonlinearities in the associated state‐space model. The system has three degrees of freedom (one actuated and two unactuated joints) while receiving only one control input. In this article, a novel nonlinear optimal (H‐infinity) control approach is developed for the dynamic model of the rotary double inverted pendulum. First, the dynamic model of the double pendulum undergoes approximate linearization with the use of first‐order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a temporary operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. At a next stage a stabilizing H‐infinity feedback controller is designed. To compute the controller's feedback gains an algebraic Riccati equation has to be solved at each time‐step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. To implement state estimation‐based control without the need to measure the entire state vector of the rotary double‐pendulum the H‐infinity Kalman filter is used as a robust state observer. The nonlinear optimal control method achieves fast and accurate tracking of setpoints by all state variables of the rotary double inverted pendulum under moderate variations of the control input.

Nonlinear Optimal Control for the Nine-Phase Permanent Magnet Synchronous Motor

Abstract Multi-phase electric motors and in particular nine-phase permanent magnet synchronous motors (9-phase PMSMs) find use in electric actuation, traction and propulsion systems. They exhibit advantages comparing to three-phase motors because of achieving high power and torque rates under moderate variations of voltage and currents in their phases, while also exhibiting fault tolerance. In this article a novel nonlinear optimal control method is developed for the dynamic model of nine-phase PMSMs. First it is proven that the dynamic model of these motors is differentially flat. Next, to apply the proposed nonlinear optimal control, the state-space model of the nine-phase PMSM undergoes an approximate linearization process at each sampling instance. The linearisation procedure is based on first-order Taylor-series expansion and on the computation of the system’s Jacobian matrices. It takes place at each sampling interval around a temporary operating point which is defined by the present value of the system’s state vector and by the last sampled value of the control inputs vector. For the linearized model of the system an H-infinity feedback controller is designed. To compute the feedback gains of this controller an algebraic Riccati equation is repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. First it is demonstrated that the H-infinity tracking performance criterion is satisfied, which signifies robustness of the control scheme against model uncertainty and perturbations. Moreover, under mild assumptions it is also proven that the control loop is globally asymptotically stable. Additionally it is experimentally confirmed through simulation tests, that the nonlinear optimal control method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs. Finally, to apply state estimation-based control without the need to measure the entire state vector of the nine-phase PMSM, the H-infinity Kalman Filter is used as a robust state estimator.

Dynamic integral sliding mode control for interconnected delayed power systems

This paper presents the dynamic integral sliding mode control (DISMC) design for interconnected power systems (IPSs). The IPSs consider the effect of time delay related to area control error and the parametric uncertainty. Different from the previous studies, to fully adopt the characteristics of both the system state and control input vector, a novel type of integral sliding surface is defined. Also, a DISMC is designed to assure the system trajectories onto the predefined integral sliding surface despite uncertainties and disturbance. Linear matrix inequality (LMI)-based stabilization criteria are derived for resulting sliding mode dynamics with H∞ performance. Besides that, the control gains and sliding surface matrix are determined. Finally, a numerical example is given to demonstrate the effectiveness and feasibility of the developed DISMC design method.

Event-Triggered Mechanism for Multiple Frequency Services of Electric Vehicles in Smart Grids

This paper considers an event-triggered mechanism (ETM) for multiple frequency services of electric vehicles (EVs) in smart grids (SGs). A new model of a SG with an ETM and multiple agents of EVs is introduced. ETM is deployed to reduce the load on network bandwidth. EVs are incorporated in both secondary and primary frequency services to suppress frequency fluctuations in the system. The behaviours of EV owners are also considered by using batteries SOC controls. Then, a mathematical model of the system is derived which contains parametric uncertainties due to the incorporation of SOC control, an ETM variable arising from ETM control, and a time-varying delay in both the state and control input vectors. We derive new Lyapunov stability and stabilizability conditions to ensure robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance of the closed-loop system. The conditions are derived based on various mathematical tools including relaxed Jensen inequality, free weighting matrix technique, reciprocally convex approach, and matrix inequality for uncertainties. An effective procedure is proposed based on an approach using the state transformation and tractable linear matrix inequalities to synthesis robust static output feedback controller gains. Extensive simulations of a SG with three agents of EVs are undertaken to validate of our proposed control scheme.

Robust dynamic sliding mode control design for interval type-2 fuzzy systems

&lt;p style='text-indent:20px;'&gt;This paper discusses the problem of stabilization of interval type-2 fuzzy systems with uncertainties, time delay and external disturbance using a dynamic sliding mode controller. The sliding surface function, which is based on both the system's state and control input vectors, is used during the control design process. The sliding mode dynamics are presented by defining a new vector that augments the system state and control vectors. First, the reachability of the addressed sliding mode surface is demonstrated. Second, the required sufficient conditions for the system's stability and the proposed control design are derived by using extended dissipative theory and an asymmetric Lyapunov-Krasovskii functional approach. Unlike some existing sliding mode control designs, the one proposed in this paper does not require the control coefficient matrices of all linear subsystems to be the same, reducing the method's conservatism. Finally, numerical examples are provided to demonstrate the viability and superiority of the proposed design method.&lt;/p&gt;

A Null-Space Compensation Control for Multi-input Redundant Plant Based on Model Reference Adaptive Control

In this paper, we proposed to design a null-space compensation control system for linear time invariant systems with multi-input redundant channels. In the input redundant systems, control input vector may have null-space component of the plant parameter vector. The null-space component does not contribute to the generation of the control force which drives the plant. If a control input that does not include the null-space component can be generated, efficient control is achieved in the sense of minimizing the norm of the control input. In the proposed method, a model reference adaptive control (MRAC) method was used to design the control system that compensates for the null-space component even if the plant parameters are unknown. The effectiveness of the proposed null-space compensation control system was shown by numerical examples.

An event‐triggered integer‐mixed adaptive dynamic programming for switched nonlinear systems with bounded inputs

AbstractThis paper studies the optimal event‐triggered control for constrained‐input discrete‐time switched nonlinear systems. Firstly, the optimal time‐triggered control problem is analyzed based on the necessary optimality condition. Secondly, the optimal event‐triggered control problem is presented, and the optimal results are presented based on the periodic time‐triggered ones, and an event‐triggered integer‐mixed adaptive dynamic programming algorithm is put forward to obtain the optimal results. The proposed algorithm is only executed at trigger instants, which decreases the execution times compared with the periodic time‐triggered algorithm, and the neural networks are applied to approximate the control input and costate vector functions of each subsystem based on the triggered states. Thirdly, an event‐triggered condition is designed to make the closed‐loop switched system asymptotically stable. Fourthly, it is shown that during the iteration process, the value function sequence corresponding to the costate vector converges to the optimal value function. Finally, the simulation results demonstrate the effectiveness of the presented algorithm.

A Nonlinear H-infinity Control Approach for Autonomous Truck and Trailer Systems

A nonlinear optimal control method is developed for autonomous truck and trailer systems. Actually, two cases are distinguished: (a) a truck and trailer system that is steered by the front wheels of its truck, (b) an autonomous fire-truck robot that is steered by both the front wheels of its truck and by the rear wheels of its trailer. The kinematic model of the autonomous vehicles undergoes linearization through Taylor series expansion. The linearization is computed at a temporary operating point that is defined at each time instant by the present value of the state vector and the last value of the control inputs vector. The linearization is based on the computation of Jacobian matrices. The modeling error due to approximate linearization is considered to be a perturbation that is compensated by the robustness of the control scheme. For the approximately linearized model of the autonomous vehicles an H-infinity feedback controller is designed. This requires the solution of an algebraic Riccati equation at each iteration of the control algorithm. The stability of the control loop is confirmed through Lyapunov analysis. It is shown that the control loop exhibits the H-infinity tracking performance which implies elevated robustness against modeling errors and external disturbances. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven. Finally, to implement state estimation-based control for the autonomous vehicles, through the processing of a small number of sensor measurements, the H-infinity Kalman Filter is proposed.

Nonlinear optimal control for autonomous hypersonic vehicles

The article proposes a nonlinear optimal (H-infinity) control method for a hypersonic aerial vehicle (HSV). The dynamic model of the hypersonic vehicle undergoes approximate linearization around a temporary operating point which is recomputed at each iteration of the control method. This operating point consists of the present value of the system’s state vector and of the last value of the control inputs vector that was applied on the HSV. The linearization relies on Taylor series expansion and on the computation of the associated Jacobian matrices. For the approximately linearized model of the hypersonic aerial vehicle, an optimal (H-infinity) feedback controller was designed. To compute the controller’s feedback gains, an algebraic Riccati equation had to be repetitively solved at each iteration of the control algorithm. The global asymptotic stability of the control method is proven through Lyapunov analysis. The control scheme remains robust against model uncertainties an external perturbations.

A Nonlinear Optimal Control Approach for Industrial Production Under an Oligopoly Model

A nonlinear optimal ( H -infinity) control method is proposed for industrial production under an oligopoly model. First, the dynamics of the oligopoly undergoes approximate linearization around a temporary operating point (equilibrium), which is recomputed at each time step of the control method. The equilibrium comprises the present value of the production system's state vector and the last value of the control inputs vector that was exerted on it. The linearization procedure makes use of the first-order Taylor series expansion and of the computation of the Jacobian matrices of the state-space description of the system. For the approximately linearized model of the system, an H -infinity (optimal) feedback controller is designed. For the computation of the controller's feedback gain, an algebraic Riccati equation is solved at each time step of the control method. The global asymptotic stability properties of the control scheme are analyzed with the use of the Lyapunov method.

Model Predictive Fault Tolerant Control for Omni-directional Mobile Robots

This paper describes the design of a Fault Tolerant Control scheme for an omni-directional mobile robot with four mecanum wheels. We consider actuation faults in which the wheels are not able to receive commands, but still can rotate freely due to the friction with the ground. A Non-linear Model Predictive Controller is employed, in order to appropriately exploit the inherited actuation redundancy of the omni-directional robot, and provide on the fly a unified accommodation solution for multiple combinations of actuation faults. A basic Fault Detection and Isolation scheme is responsible for identifying and isolate the faulty wheels. Accordingly, the control input vector and consequently the dynamic model of the vehicle are reformulated depending on the identified faults. Hence, the predictive control scheme is dynamically updated and calculates the optimal actuation solution, given the faults occurred. The proposed scheme, is able to guide the vehicle to any goal configuration within the workspace, while simultaneously satisfying state (e.g obstacle avoidance) and input (e.g motor limits) constraints. The efficacy of the proposed scheme is evaluated via a set of realistic simulation scenarios, where an omni-directional mobile robot executes a way point tracking mission with obstacle avoidance, inside a restricted workspace and in the presence of different combinations of actuation faults.

Adaptive Control Allocation with Communication Delay for In-Wheel Propulsion Electric Vehicles

In this paper, an integrated, fault-tolerant control approach for yaw stability of ground vehicles is outlined in the presence of actuator and sensor network delay. In order to simultaneously control all actuators in the vehicle, an adaptive control allocation method is used. The proposed control scheme consists of a high level controller that creates a virtual control input vector and a low level control allocation that distributes the virtual control effort among redundant actuators. Virtual control input consists of desired traction force, desired yaw moment and required lateral force correction to ensure stability while following a given reference. Based on this virtual control input vector, the allocation module determines front steering angle correction, rear steering angle, traction forces at each wheel. Presented control structure is simulated by using a 10 Degree of freedom vehicle model. Results show that the proposed approach can follow the references despite the loss of actuator effectiveness in the driving cycle with the presence of delay in the communication system while the system with no adaptive allocation fails to stay on course.