Showing papers on "Control theory published in 2016"

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

Abstract: This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.

Stability and Scalability of Homogeneous Vehicular Platoon: Study on the Influence of Information Flow Topologies

Abstract: In addition to decentralized controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the internal stability and scalability of homogeneous vehicular platoons moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and vehicle-to-vehicle (V2V) communications. Under linear feedback controllers, a unified internal stability theorem is proved by using the algebraic graph theory and Routh–Hurwitz stability criterion. The theorem explicitly establishes the stabilizing thresholds of linear controller gains for platoons, under a large class of different information flow topologies. Using matrix eigenvalue analysis, the scalability is investigated for platoons under two typical information flow topologies, i.e., 1) the stability margin of platoon decays to zero as $0(\mbox{1}/N^{2})$ for bidirectional topology; and 2) the stability margin is always bounded and independent of the platoon size for bidirectional-leader topology. Numerical simulations are used to illustrate the results.

Monitoring speed control and precision incrementing of motor for powered surgical instruments

Abstract: Motor speed control systems are disclosed that include a motor having an output shaft, a motor speed control system, a sensor to detect the speed of the motor, and a controller to receive a signal from the sensor and to control the speed of the output shaft A gear reduction assembly is operably coupled to the output shaft and a detectable element located in the gear reduction assembly The sensor senses the detectable element The sensor is placed in the radial path of the detectable element Another system includes a brushless motor having a housing and an output shaft, the brushless motor comprising electromagnetic field coils arrayed radially around a central magnetic shaft, a gear reduction assembly operably coupled to the output shaft, and a sensor placed in proximity to the brushless motor The sensor communicates with a controller to control the speed of the output shaft

Exponential Control Barrier Functions for enforcing high relative-degree safety-critical constraints

Abstract: We introduce Exponential Control Barrier Functions as means to enforce strict state-dependent high relative degree safety constraints for nonlinear systems. We also develop a systematic design method that enables creating the Exponential CBFs for nonlinear systems making use of tools from linear control theory. The proposed control design is numerically validated on a relative degree 6 linear system (the serial cart-spring system) and on a relative degree 4 nonlinear system (the two-link pendulum with elastic actuators.)

Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

Abstract: In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.

Fixed-Time Attitude Control for Rigid Spacecraft With Actuator Saturation and Faults

Abstract: This brief investigates the finite-time control problem associated with attitude stabilization of a rigid spacecraft subject to external disturbance, actuator faults, and input saturation. More specifically, a novel fixed-time sliding mode surface is developed, and the settling time of the defined surface is shown to be independent of the initial conditions of the system. Then, a finite-time controller is derived to guarantee that the closed-loop system is stable in the sense of the fixed-time concept. The actuator-magnitude constraints are rigorously enforced and the attitude of the rigid spacecraft converges to the equilibrium in a finite time even in the presence of external disturbances and actuator faults. Numerical simulations illustrate the spacecraft performance obtained using the proposed controller.

Adaptive Sliding Mode Control for Interval Type-2 Fuzzy Systems

Abstract: This paper is concerned with the adaptive sliding mode control problem of uncertain nonlinear systems. Interval type-2 Takagi–Sugeno (T–S) fuzzy model is employed to represent uncertain nonlinear systems. The input matrices of the nonlinear systems are allowed to be different for the sliding mode controller design. The uncertain parameters are described by the lower and upper membership functions. An integral sliding mode surface is designed for analysis of sliding motion. Based on the sliding mode surface, a novel sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Some simulation results are given to illustrate the effectiveness of the presented control scheme.

Adaptive Fuzzy Control of Strict-Feedback Nonlinear Time-Delay Systems With Unmodeled Dynamics

Abstract: In this paper, an approximated-based adaptive fuzzy control approach with only one adaptive parameter is presented for a class of single input single output strict-feedback nonlinear systems in order to deal with phenomena like nonlinear uncertainties, unmodeled dynamics, dynamic disturbances, and unknown time delays. Lyapunov–Krasovskii function approach is employed to compensate the unknown time delays in the design procedure. By combining the advances of the hyperbolic tangent function with adaptive fuzzy backstepping technique, the proposed controller guarantees the semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system from the mean square point of view. Two simulation examples are finally provided to show the superior effectiveness of the proposed scheme.

Safe controller optimization for quadrotors with Gaussian processes

Abstract: One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.

Droop-Based Distributed Cooperative Control for Microgrids With Time-Varying Delays

Abstract: This paper develops a droop-based distributed cooperative control scheme for microgrids under a switching communication network with non-uniform time-varying delays. We first design a pinning-based frequency/voltage controller containing a distributed voltage observer and then design a consensus-based active/reactive power controller, which are employed into the secondary control stage to generate the nominal set points used in the primary control stage for different distributed generators (DGs). By this approach, the frequencies and the weighted average value of all DGs’ voltages can be pinned to the desired values while maintaining the precise active and reactive power sharing. With the proposed scheme, each DG only needs to communicate with its neighbors intermittently, even if their communication networks are local and time-varying, and their variant delays may be non-uniform. Sufficient conditions on the requirements for the network connectivity and the delay upper bound that guarantee the stability and reliability of the microgrid are presented. The effectiveness of the proposed control scheme is verified by the simulation of a microgrid test system.

Load frequency control of interconnected power system using grey wolf optimization

Abstract: In this article an attempt has been made to solve load frequency control (LFC) problem in an interconnected power system network equipped with classical PI/PID controller using grey wolf optimization (GWO) technique. Initially, proposed algorithm is used for two-area interconnected non-reheat thermal-thermal power system and then the study is extended to three other realistic power systems, viz. (i) two-area multi-units hydro-thermal, (ii) two-area multi-sources power system having thermal, hydro and gas power plants and (iii) three-unequal-area all thermal power system for better validation of the effectiveness of proposed algorithm. The generation rate constraint (GRC) of the steam turbine is included in the system modeling and dynamic stability of aforesaid systems is investigated in the presence of GRC. The controller gains are optimized by using GWO algorithm employing integral time multiplied absolute error (ITAE) based fitness function. Performance of the proposed GWO algorithm has been compared with comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) and other similar meta-heuristic optimization techniques available in literature for similar test system. Moreover, to demonstrate the robustness of proposed GWO algorithm, sensitivity analysis is performed by varying the operating loading conditions and system parameters in the range of ± 50 % . Simulation results show that GWO has better tuning capability than CLPSO, EPSDE and other similar population-based optimization techniques.

Shared Steering Control Using Safe Envelopes for Obstacle Avoidance and Vehicle Stability

Abstract: Steer-by-wire technology enables vehicle safety systems to share control with a driver through augmentation of the driver's steering commands. Advances in sensing technologies empower these systems further with real-time information about the surrounding environment. Leveraging these advancements in vehicle actuation and sensing, the authors present a shared control framework for obstacle avoidance and stability control using two safe driving envelopes. One of these envelopes is defined by the vehicle handling limits, whereas the other is defined by spatial limitations imposed by lane boundaries and obstacles. A model predictive control (MPC) scheme determines at each time step if the current driver command allows for a safe vehicle trajectory within these two envelopes, intervening only when such a trajectory does not exist. In this way, the controller shares control with the driver in a minimally invasive manner while avoiding obstacles and preventing loss of control. The optimal control problem underlying the controller is inherently nonconvex but is solved as a set of convex problems allowing for reliable real-time implementation. This approach is validated on an experimental vehicle working with human drivers to negotiate obstacles in a low friction environment.

Adaptive Dynamic Programming and Adaptive Optimal Output Regulation of Linear Systems

Abstract: This note studies the adaptive optimal output regulation problem for continuous-time linear systems, which aims to achieve asymptotic tracking and disturbance rejection by minimizing some predefined costs. Reinforcement learning and adaptive dynamic programming techniques are employed to compute an approximated optimal controller using input/partial-state data despite unknown system dynamics and unmeasurable disturbance. Rigorous stability analysis shows that the proposed controller exponentially stabilizes the closed-loop system and the output of the plant asymptotically tracks the given reference signal. Simulation results on a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed approach.

Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Unmeasurable Premise Variables

Abstract: The problem of fuzzy observer-based controller design is investigated for nonlinear networked control systems subject to imperfect communication links and parameter uncertainties. The nonlinear networked control systems with parameter uncertainties are modeled through an interval type-2 (IT2) Takagi–Sugeno (T-S) model, in which the uncertainties are handled via lower and upper membership functions. The measurement loss occurs randomly, both in the sensor-to-observer and the controller-to-actuator communication links. Specially, a novel data compensation strategy is adopted in the controller-to-actuator channel. The observer is designed under the unmeasurable premise variables case, and then, the controller is designed with the estimated states. Moreover, the conditions for the existence of the controller can ensure that the resulting closed-loop system is stochastically stable with the predefined disturbance attenuation performance. Two examples are provided to illustrate the effectiveness of the proposed method.

Fuzzy Adaptive Quantized Control for a Class of Stochastic Nonlinear Uncertain Systems

Abstract: In this paper, a fuzzy adaptive approach for stochastic strict-feedback nonlinear systems with quantized input signal is developed. Compared with the existing research on quantized input problem, the existing works focus on quantized stabilization, while this paper considers the quantized tracking problem, which recovers stabilization as a special case. In addition, uncertain nonlinearity and the unknown stochastic disturbances are simultaneously considered in the quantized feedback control systems. By putting forward a new nonlinear decomposition of the quantized input, the relationship between the control signal and the quantized signal is established, as a result, the major technique difficulty arising from the piece-wise quantized input is overcome. Based on fuzzy logic systems’ universal approximation capability, a novel fuzzy adaptive tracking controller is constructed via backstepping technique. The proposed controller guarantees that the tracking error converges to a neighborhood of the origin in the sense of probability and all the signals in the closed-loop system remain bounded in probability. Finally, an example illustrates the effectiveness of the proposed control approach.

Fuzzy Observed-Based Adaptive Consensus Tracking Control for Second-Order Multiagent Systems With Heterogeneous Nonlinear Dynamics

Abstract: In this paper, the consensus tracking control problem of second-order multiagent systems with unknown nonlinear dynamics, immeasurable states, and disturbances is investigated. The nonlinear dynamics in multiagent systems do not satisfy the matched condition. In this paper, fuzzy logic system is introduced to approximate the unknown nonlinear dynamics, and adaptive high-gain observer is designed to estimate the unmeasured states. Based on backstepping approach and Lyapunov theory, a new adaptive fuzzy distributed controller is proposed for each agent only using the information of itself and its neighbors. Then the consensus tracking is achieved under the designed distributed controller. Moreover, it is proved that all the signals in the multiagent systems are semiglobally uniformly ultimately bounded, and the consensus tracking error converges to a small neighborhood of the origin that can be designed as small as possible. Finally, the simulation result illustrates the effectiveness of the designed controller.

Output-Feedback Based Sliding Mode Control for Fuzzy Systems With Actuator Saturation

Abstract: In this paper, a novel adaptive sliding mode controller is designed for Takagi–Sugeno (T–S) fuzzy systems with actuator saturation and system uncertainty. By the delta operator approach, the discrete-time nonlinear system is described by a T–S fuzzy model with unmeasurable state. By singular value decomposition of system input matrix, a reduced-order system is obtained for the design of sliding mode surface. A new adaptive sliding mode controller based on system output is presented to guarantee that the closed-loop system is uniformly ultimately bounded. Four examples are provided to illustrate the effectiveness and applicability of the proposed control scheme.

Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form

Abstract: This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear nonstrict-feedback systems via output feedback. A novel adaptive NN backstepping output-feedback control approach is first proposed for nonlinear nonstrict-feedback systems. The monotonicity of system bounding functions and the structure character of radial basis function (RBF) NNs are used to overcome the difficulties that arise from nonstrict-feedback structure. A state observer is constructed to estimate the immeasurable state variables. By combining adaptive backstepping technique with approximation capability of radial basis function NNs, an output-feedback adaptive NN controller is designed through backstepping approach. It is shown that the proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. Two examples are used to illustrate the effectiveness of the proposed approach.

Distributed Tertiary Control of DC Microgrid Clusters

Abstract: A distributed control method is proposed to handle power sharing among a cluster of dc microgrids. The hierarchical control structure of microgrids includes primary, secondary, and tertiary levels. While the load sharing among the sources within a dc microgrid is managed through primary and secondary controllers, a tertiary control level is required to provide the higher level load sharing among microgrids within a cluster. Power transfer between microgrids enables maximum utilization of renewable sources and suppresses stress and aging of the components, which improves its reliability and availability, reduces the maintenance costs, and expands the overall lifespan of the network. The proposed control mechanism uses a cooperative approach to adjust voltage set points for individual microgrids and, accordingly, navigate the power flow among them. Loading mismatch among neighbor microgrids is used in an updating policy to adjust voltage set point and mitigate such mismatches. While the voltage adjustment policy handles the load sharing among the microgrids within each cluster, at a lower level, each microgrid carries a communication network that is in contact with the secondary control system. It is this lower level network that propagates voltage set points across all sources within a microgrid. Load sharing and set point propagation are analytically studied for the higher and lower level controllers, respectively. Experimental studies on two cluster setups demonstrate excellent controller performance and validate its resiliency against converter failures and communication losses.

Composite Learning From Adaptive Dynamic Surface Control

Abstract: In the conventional adaptive control, a stringent condition named persistent excitation (PE) must be satisfied to guarantee parameter convergence. This technical note focuses on adaptive dynamic surface control for a class of strict-feedback nonlinear systems with parametric uncertainties, where a novel technique coined composite learning is developed to guarantee parameter convergence without the PE condition. In the composite learning, online recorded data together with instantaneous data are applied to generate prediction errors, and both tracking errors and prediction errors are utilized to update parametric estimates. The proposed approach is also extended to an output-feedback case by using a nonlinear separation principle. The distinctive feature of the composite learning is that parameter convergence can be guaranteed by an interval-excitation condition which is much weaker than the PE condition such that the control performance can be improved from practical asymptotic stability to practical exponential stability. An illustrative example is used for verifying effectiveness of the proposed approach.

Disturbance Observer-Based Integral Sliding-Mode Control for Systems With Mismatched Disturbances

Abstract: This paper develops the disturbance observer-based integral sliding-mode control approach for continuous-time linear systems with mismatched disturbances or uncertainties. The disturbance observer is proposed to generate the disturbance estimate, which can be incorporated in the controller to counteract the disturbance. With the help of the proposed disturbance observer, both the memoryless and memory-based integral sliding surfaces and integral sliding-mode controllers are developed, respectively, and two approaches, i.e., $H_\infty$ control and steady-state output-based approaches, are proposed to design the controller and disturbance rejection gains. Finally, the effectiveness and applicability of the proposed technique are illustrated by a numerical example and a real-time experiment.

Robust Constrained Learning-based NMPC enabling reliable mobile robot path tracking

Abstract: This paper presents a Robust Constrained Learning-based Nonlinear Model Predictive Control RC-LB-NMPC algorithm for path-tracking in off-road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off-road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low-uncertainty, non-parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real-time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, off-road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid-steered robot at speeds of up to 2.0 m/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high-performance, optimal control during later trials when model uncertainty is reduced with experience.

Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems

Abstract: This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear continuous-time systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.

Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems

Abstract: In this paper, an adaptive neural decentralized control approach is proposed for a class of multiple input and multiple output uncertain stochastic nonlinear strong interconnected systems. Radial basis function neural networks are used to approximate the packaged unknown nonlinearities, and backstepping technique is utilized to construct an adaptive neural decentralized controller. The proposed control scheme can guarantee that all signals of the resulting closed-loop system are semiglobally uniformly ultimately bounded in the sense of fourth moment, and the tracking errors eventually converge to a small neighborhood around the origin. The main feature of this paper is that the proposed approach is capable of controlling the stochastic systems with strong interconnected nonlinearities both in the drift and diffusion terms that are the functions of all states of the overall system. Simulation results are used to illustrate the effectiveness of the suggested approach.

Analysis and Optimization of Droop Controller for Microgrid System Based on Small-Signal Dynamic Model

Abstract: Droop control strategy enables the microgrid switch between grid-connected and islanded mode flexibly, and easily realizes the “plug and play” function of distributed generation and loads, which has recently aroused great concerns. However, small disturbances may occur during the changing process and eventually yield transient oscillation, thus the focus of microgrid control is how to switch smoothly within different operation modes. In order to improve the dynamic characteristics of an inverter-based microgrid, this paper derived a precise small-signal state-space model of the whole microgrid including droop controller, network, and loads. The key control parameters of the inverter and their optimum ranges, which greatly influence the damping frequency of oscillatory components in the transient response, can be obtained through eigenvalue analysis. In addition, genetic algorithm is introduced to search for optimal settings of the key parameters during time-domain simulation in MATLAB/Simulink. Simulation results verified the effectiveness of the proposed small-signal dynamic model and optimization algorithm, and enhanced the dynamic performance of the microgrid, which can be the reference for parameter design of droop control in low voltage microgrids.