Category Archives: Control Engineering

On the use of flipped classroom for control engineering classes and its problem with the required (longer) time for learning

May 10, 2018 08:21 ,

Y. Kim and C. Ahn, Effect of Combined Use of Flipped Learning and Inquiry-Based Learning on a System Modeling and Control Course, IEEE Transactions on Education, vol. 61, no. 2, pp. 136-142, DOI: 10.1109/TE.2017.2774194.

Contribution: This paper illustrates how to design and implement curricula in terms of the combined use of flipped learning and inquiry-based learning in an engineering course. Background: Elementary courses in engineering schools are conventional and foundational, and involve a considerable amount of knowledge. Throughout such courses, students are also expected to develop insight, which cannot be obtained by only listening to instructors. Having relevant discussions is also difficult for most instructors. Intended Outcomes: The combined use of flipped learning and inquiry-based learning would be beneficial to broaden student achievement. Application Design: Based on an epistemological approach about knowledge and knowing, this paper applies the combined use of flipped learning and inquiry-based learning to enhance student knowledge and advance ways of thinking on a System Modeling and Control course. Findings: The extended learning time and the collective responsibility for learning are discussed as critical issues in applying the combined use of flipped learning and inquiry-based learning in an engineering school.

Posted in: Control Engineering, Education , Tagged: ,

A mathematical study of controllers that produce paths with beautfiul shapes to reach a target point by a unicycle vehicle

April 26, 2018 07:25 ,

T. Tripathy and A. Sinha, Unicycle With Only Range Input: An Array of Patterns, IEEE Transactions on Automatic Control, vol. 63, no. 5, pp. 1300-1312, DOI: 10.1109/TAC.2017.2736940.

The objective of this paper is to generate planar patterns using an autonomous agent modeled as a unicycle. The patterns are generated about a stationary point referred to as the target. To achieve the same, the paper proposes a family of control inputs that are continuous functions of range, which is the distance between the unicycle and the target. The paper studies in detail a characterization of the resulting trajectories, which are a plethora of patterns of parametric curves (circles, spirals, epicyclic curves like hypotrochoids) and more. These appealing patterns find applications in exploration, coverage, land mine detection, etc., where the target represents any point of interest like a landmark or a beacon. The paper also investigates the necessary conditions on the control laws in order to generate patterns of desired shapes and bounds. Furthermore, to generate desired patterns with arbitrary initial conditions, a switching strategy is proposed which is illustrated using an algorithm. The paper presents a series of simulations of appealing patterns generated using the proposed control laws.

Posted in: Control Engineering, Robot motion planning

Using EKF estimation in a PI controller for improving its performance under noise

March 29, 2018 09:29 ,

Y. Zhou, Q. Zhang, H. Wang, P. Zhou and T. Chai, EKF-Based Enhanced Performance Controller Design for Nonlinear Stochastic Systems, IEEE Transactions on Automatic Control, vol. 63, no. 4, pp. 1155-1162, DOI: 10.1109/TAC.2017.2742661.

In this paper, a novel control algorithm is presented to enhance the performance of the tracking property for a class of nonlinear and dynamic stochastic systems subjected to non-Gaussian noises. Although the existing standard PI controller can be used to obtain the basic tracking of the systems, the desired tracking performance of the stochastic systems is difficult to achieve due to the random noises. To improve the tracking performance, an enhanced performance loop is constructed using the EKF-based state estimates without changing the existing closed loop with a PI controller. Meanwhile, the gain of the enhanced performance loop can be obtained based upon the entropy optimization of the tracking error. In addition, the stability of the closed loop system is analyzed in the mean-square sense. The simulation results are given to illustrate the effectiveness of the proposed control algorithm.

Posted in: Bayesian filtering, Control Engineering , Tagged: ,

Achieving smooth motion in robotic manipulators on-line through their controller, and a nice state-of-the-art of the problem of smooth motion

February 7, 2018 08:52 ,

Yu-Sheng Lu, Yi-Yi Lin, Smooth motion control of rigid robotic manipulators with constraints on high-order kinematic variables, Mechatronics,
Volume 49, 2018, Pages 11-25, DOI: 10.1016/j.mechatronics.2017.11.003.

This paper presents a design for a jerk-constrained, time-optimal controller (JCTOC) that allows the smooth control of rigid robotic manipulators, in which time-optimal output responses are attained with confined jerk. A snap-constrained, time-optimal control (SCTOC) scheme is also proposed to produce even smoother output responses that are time-optimal, with a constraint on the maximum admissible snap. In contrast to conventional path-planning approaches that involve a bounded jerk/snap, the proposed JCTOC and SCTOC practically limit the corresponding high-order kinematic variables in real time. Using the structure of the computed torque control, the PD control, the JCTOC and the SCTOC are experimentally compared in terms of specific performance indices, including a chatter index, which is used to measure the unevenness of a signal.

Posted in: Control Engineering, Robot motion planning , Tagged:

Interesting study about the concepts to be taught in control system engineering

November 1, 2017 09:08 ,

R. M. Reck, Common Learning Objectives for Undergraduate Control Systems Laboratories,IEEE Transactions on Education, vol. 60, no. 4, pp. 257-264, DOI: 10.1109/TE.2017.2681624.

Course objectives, like research objectives and product requirements, help provide clarity and direction for faculty and students. Unfortunately, course and laboratory objectives are not always clearly stated. Without a clear set of objectives, it can be hard to design a learning experience and determine whether students are achieving the intended outcomes of the course or laboratory. In this paper, a common set of laboratory objectives, concepts, and components of a laboratory apparatus for undergraduate control systems laboratories were identified. A panel of 40 control systems faculty members completed a multi-round Delphi survey to bring them toward consensus on the common aspects of their laboratories. These panelists identified 15 laboratory objectives, 26 concepts, and 15 components common to their laboratories. Then an 45 additional faculty members and practitioners completed a follow-up survey to gather feedback on the results. In both surveys, each participant rated the importance of each item. While average ratings differed slightly between the two groups, the order in which the items were ranked was similar. Important examples of common learning objectives include connecting theory to what is implemented in the laboratory, designing controllers, and modeling systems. The most common component in both groups was MathWorks software. Some of the common concepts include block diagrams, stability, and PID control. Defining common aspects of undergraduate control systems laboratories enables common development, detailed comparisons, and simplified adaptation of equipment and experiments between campuses and programs.

Posted in: Control Engineering, Education

An interesting simulation educational software for control systems engineering based on controlling a quadrotor

November 1, 2017 09:04 ,

S. Khan, M. H. Jaffery, A. Hanif and M. R. Asif, Teaching Tool for a Control Systems Laboratory Using a Quadrotor as a Plant in MATLAB, IEEE Transactions on Education, vol. 60, no. 4, pp. 249-256, DOI: 10.1109/TE.2017.2653762.

This paper presents a MATLAB-based application to teach the guidance, navigation, and control concepts of a quadrotor to undergraduate students, using a graphical user interface (GUI) and 3-D animations. The Simulink quadrotor model is controlled by a proportional integral derivative controller and a linear quadratic regulator controller. The GUI layout’s many components can be easily programmed to perform various experiments by considering the simulation of the quadrotor as a plant; it incorporates control systems (CS) fundamentals such as time domain response, transfer function and state-space form, pole-zero location, root locus, frequency domain response, steady-state error, position and disturbance response, controller design and tuning, unity, and the use of a Kalman filter as a feedback sensor. 3-D animations are used to display the quadrotor flying in any given condition selected by the user. For each simulation, users can view the output response in the form of 3-D animations, and can run time plots. The quadrotor educational tool (QET) helps students in the CS laboratory understand basic CS concepts. The QET was evaluated based on student feedback, grades, satisfaction, and interest in CS.

Posted in: Control Engineering, Education , Tagged: , , ,

Value iteration applied in control systems when the model of the plant is substituted by data acquired from the plant

June 27, 2017 08:33 ,

Yongqiang Li, Zhongsheng Hou, Yuanjing Feng, Ronghu Chi, Data-driven approximate value iteration with optimality error bound analysis, Automatica, Volume 78, April 2017, Pages 79-87, ISSN 0005-1098, DOI: 10.1016/j.automatica.2016.12.019.

Features of the data-driven approximate value iteration (AVI) algorithm, proposed in Li et al. (2014) for dealing with the optimal stabilization problem, include that only process data is required and that the estimate of the domain of attraction for the closed-loop is enlarged. However, the controller generated by the data-driven AVI algorithm is an approximate solution for the optimal control problem. In this work, a quantitative analysis result on the error bound between the optimal cost and the cost under the designed controller is given. This error bound is determined by the approximation error of the estimation for the optimal cost and the approximation error of the controller function estimator. The first one is concretely determined by the approximation error of the data-driven dynamic programming (DP) operator to the DP operator and the approximation error of the value function estimator. These three approximation errors are zeros when the data set of the plant is sufficient and infinitely complete, and the number of samples in the interested state space is infinite. This means that the cost under the designed controller equals to the optimal cost when the number of iterations is infinite.

NOTE: Another paper on the same issue in the same journal.

Posted in: Applications of reinforcement learning to control engineering , Tagged: ,

A study of the influence of uncertain, stochastic delays in the stability of LTI SISO systems

June 22, 2017 11:54 ,

T. Qi, J. Zhu and J. Chen, “Fundamental Limits on Uncertain Delays: When Is a Delay System Stabilizable by LTI Controllers?,” in IEEE Transactions on Automatic Control, vol. 62, no. 3, pp. 1314-1328, March 2017. DOI: 10.1109/TAC.2016.2584007.

This paper concerns the stabilization of linear time-invariant (LTI) systems subject to uncertain, possibly time-varying delays. The fundamental issue under investigation, referred to as the delay margin problem, addresses the question: What is the largest range of delay such that there exists a single LTI feedback controller capable of stabilizing all the plants for delays within that range? Drawing upon analytic interpolation and rational approximation techniques, we derive fundamental bounds on the delay margin, within which the delay plant is guaranteed to be stabilizable by a certain LTI output feedback controller. Our contribution is threefold. First, for single-input single-output (SISO) systems with an arbitrary number of plant unstable poles and nonminimum phase zeros, we provide an explicit, computationally efficient bound on the delay margin, which requires computing only the largest real eigenvalue of a constant matrix. Second, for multi-input multi-output (MIMO) systems, we show that estimates on the variation ranges of multiple delays can be obtained by solving LMI problems, and further, by finding bounds on the radius of delay variations. Third, we show that these bounds and estimates can be extended to systems subject to time-varying delays. When specialized to more specific cases, e.g., to plants with one unstable pole but possibly multiple nonminimum phase zeros, our results give rise to analytical expressions exhibiting explicit dependence of the bounds and estimates on the pole and zeros, thus demonstrating how fundamentally unstable poles and nonminimum phase zeros may limit the range of delays over which a plant can be stabilized by a LTI controller.

Posted in: Control Engineering , Tagged: ,

Model-based reinforcement learning with a reduced number of basis functions to aproximate the value function, a study of its convergence guarantees, and a nice state of the art on the use of (mdel-based) reinforcement learning for automatic control

June 20, 2017 11:05 ,

Rushikesh Kamalapurkar, Joel A. Rosenfeld, Warren E. Dixon, Efficient model-based reinforcement learning for approximate online optimal control, Automatica, Volume 74, 2016, Pages 247-258, ISSN 0005-1098, DOI: 10.1016/j.automatica.2016.08.004.

An infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using a state following (StaF) kernel method to approximate the value function. Unlike traditional methods that aim to approximate a function over a large compact set, the StaF kernel method aims to approximate a function in a small neighborhood of a state that travels within a compact set. Simulation results demonstrate that stability and approximate optimality of the control system can be achieved with significantly fewer basis functions than may be required for global approximation methods.

Posted in: Applications of reinforcement learning to control engineering , Tagged: ,

Value iteration applied to continuous LTI systems control

June 8, 2017 11:19 ,

Tao Bian, Zhong-Ping Jiang, Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design, Automatica, Volume 71, September 2016, Pages 348-360, ISSN 0005-1098, DOI: 10.1016/j.automatica.2016.05.003.

This paper presents a novel non-model-based, data-driven adaptive optimal controller design for linear continuous-time systems with completely unknown dynamics. Inspired by the stochastic approximation theory, a continuous-time version of the traditional value iteration (VI) algorithm is presented with rigorous convergence analysis. This VI method is crucial for developing new adaptive dynamic programming methods to solve the adaptive optimal control problem and the stochastic robust optimal control problem for linear continuous-time systems. Fundamentally different from existing results, the a priori knowledge of an initial admissible control policy is no longer required. The efficacy of the proposed methodology is illustrated by two examples and a brief comparative study between VI and earlier policy-iteration methods.

Posted in: Control Engineering , Tagged: