This article presents a novel sampled‐data static output‐feedback control technique for continuous‐time linear parameter‐varying (LPV) systems. Particularly, the input‐delay approach is introduced to map the sample‐and‐hold dynamics into the continuous‐time domain with delay in the states. This, together with a new parameter‐dependent Lyapunov–Krasovskii functional, results in a bounded real lemma for the underlying closed‐loop LPV systems. Furthermore, application of convex optimization techniques transforms the controller synthesis problem into the feasibility of a group of parameterized matrix inequalities. It is also shown that the convexification procedures allow for controller dependence on time‐varying parameters. Simulation studies are conducted on a case comparison and an overhead crane model to validate the efficacy and less conservatism of the result.
Recent advances in computing and network technologies have contributed much to the successful handling of certain problems in biology, physics, economics, and so forth that until recently were thought too difficult to be analyzed.These complex systems problems tend to share a number of interesting properties from a mathematical viewpoint.A key feature of such systems is that the nonlinear interactions among its components can lead to interesting emergent behavior.The overall aim of this special issue is to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems, which may depend largely on methods from artificial intelligence, statistics, operational research, and engineering, including nonlinear dynamics, time series analysis, dynamic systems, cellular automata, artificial life, evolutionary computation, game theory, neural networks, multi-agents, and heuristic search methods.The solicited papers in this special issue should provide solutions, or early promises, to modeling, analysis, and control problems of real-world complex systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems.Topics include, but are not limited to: (1) control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems and positive systems), (2) networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks), and (3) stochastic systems (nonlinear filtering, nonparametric
Summary This article investigates the dynamic output feedback control problem for a class of networked Markovian jump systems via sliding mode control method. The stochastic communication protocol schedules the communication between sensors and the controller, by which only one sensor node can access the network at one instant. First, a compensator at the controller side is utilized to yield the available measurement signals composed of the current measurement output transmitted by the selected sensor node and the past measurement output from other sensor nodes. A joint Markovian chain incorporating the Markovian chains of the system as well as the stochastic communication protocol is introduced to model the Markovian jump system subject to the stochastic communication protocol. And then, the dynamic output feedback sliding mode controller is designed and the stochastic stability is analyzed. Moreover, a feasible solving algorithm is provided via a projection technique. Finally, the proposed control scheme is verified via an electrical motor model.
This paper examines the concept of the leader-follower formation for fractional-order multi-agent systems (FO-MASs) by utilizing dynamic event-triggered sliding mode control (SMC) method. Firstly, a dynamic event-triggered mechanism (DETM) is designed relying on the combined measurement vector, in which an auxiliary variable is introduced to dynamically adjust the threshold for each fractional-order agent system. Unlike most existing event-triggered communication mechanisms, whose threshold parameters are always fixed, the threshold parameters in our designed event-triggered conditions can be dynamically adjusted according to the fractional-order dynamic rule. Numerical results show that the proposed DETM can achieve a better system performance in reducing the sampling frequency and the expected formation performance. Secondly, the leader-follower formation control problem is transformed into checking the asymptotic stability problem of the closed-loop system. In addition, due to the memorability of fractional calculus operators, a distinctive condition is established to avoid the occurrence of Zeno behaviors reported in existing dynamic event-triggered schemes. Finally, a simulation example is presented to illustrate the effectiveness and feasibility of the dynamic event-triggered SMC method proposed in this paper.
A novel attitude tracking control scheme is presented for overactuated spacecraft to address the attitude stabilization problem in presence of reaction wheel installation deviation, external disturbance and uncertain mass of moment inertia. An adaptive sliding mode control technique is proposed to track the uncertainty. A Lyapunov-based analysis shows that the compensation control law can guarantee that the desired attitude trajectories are followed in finite-time. The key feature of the proposed control strategy is that it globally asymptotically stabilizes the system, even in the presence of reaction wheel installation deviation, external disturbances, and uncertain mass of moment inertia. The attitude track performance using the proposed finite-time compensation control is evaluated through a numerical example.
This paper investigates the H ∞ control problem for the networked control systems with stochastic sampling. The network topology structure is CAN network owing to the fact that network-induced delay is always less than a sampling period and event driven is supported in CAN network. The continuous-time model with a variable satisfying a Bernoulli distribution is established for network control systems with stochastic sampling by employing the input-delay approach. Based on this, sufficient conditions are obtained, which guarantee the mean-square exponential stability of the system with an H ∞ performance. Moreover, an H ∞ controller design procedure is proposed. The simulation shows the validity of the control strategy and less conservatism.
Mobile receiver complexity persists as one of the key areas of concern in the adoption of MIMO technologies for systems such as high-speed downlink packet access in UMTS. This is particularly the case for high-order modulation schemes such as 16- and 64-QAM, where optimum a posteriori probability detection becomes too complex, and sub-optimal detection based on serial or parallel interference cancellation performs poorly. The above issues are successfully tackled by introducing a judicious layered encoding process at the transmitter which, once appropriately exploited at the receiver, allows considerable reductions in computational complexity in addition to improvements in performance. Also, the applicability of high-order modulations with layered encoding is investigated using system-level simulations for a 2-cell and a 7-cell urban scenario. It is shown that high-order modulations can be used in a substantial area of the cell for both investigated scenarios.
In this paper, we present the design of temperature control for industrial heat treating furnace by using Intelligent Fuzzy Logic and PID controllers. Temperature control is important in heating processes as it can disqualify materials in terms of their physical properties when not well performed. Obviously PID temperature controller is the most used in industries to control non-linear processes. Consequently, it has been found that the output response from Fuzzy Logic is very accurate in terms of overshoot and steady state error when compared to that of PID. Moreover, both temperature controllers are modeled and simulated using MATLAB software.
The paper presents a comprehensive, complex, numerical, optimization methodology (computational framework) dedicated for supporting structures of small-scale wind turbines. The small wind turbine (SWT) supporting structure is one of the key components determining the cost of such a device. Therefore, the supporting structure optimization will allow cost reduction and, hence, popularization of these devices around the world. The presented methodology is based on the following: single-objective (aggregation-approach to multi-objective problem) evolutionary algorithm driven optimization, finite-element structural analyses, estimation of wind energy capture efficiency (coupled aero-servo-elastic numerical simulations), and economic evaluation (based on real meteorological data). Then, the methodology is proposed for a guy-wired mast structure of an arbitrary chosen SWT model. The optimization of chosen design features of the structure is performed and as a result the optimal solution for given assumptions is presented and scaling factor for that case is identified (total mass of the foundations). The successful use of combined numerical methods (genetic algorithms, FE method analyses, coupled aero-servo-elastic numerical simulations, pre-/post-processing scripts, and economic evaluation models) is the main novelty of this work.
This paper develops a cascaded sliding mode observer method to reconstruct actuator faults for a class of descriptor linear systems. Based on a new canonical form, a novel design method is presented to discuss the existence conditions of the sliding mode observer. Furthermore, the proposed method is extended to general descriptor linear systems with actuator faults. Finally, the effectiveness of the proposed technique is illustrated by a simulation example.
In this paper, some small-gain theorems are proposed for stochastic network systems which describe large-scale systems with interconnections, uncertainties and random disturbances. By the aid of conditional dissipativity and showing times of stochastic interval, small-gain conditions proposed for the deterministic case are extended to the stochastic case. When some design parameters are tunable in practice, we invaginate a simpler method to verify small-gain condition by selecting one subsystem as a monitor. Compared with the existing results, the existence-and-uniqueness of solution and ultimate uniform boundedness of input are removed from requirements of input-to-state stability and small-gain theorems.
Electronic throttle is widely used in modern automotive engines. An electronic throttle system regulates the throttle plate angle by using a DC servo motor to adjust the inlet airflow rate of an internal combustion engine. Its application leads to improvements in vehicle drivability, fuel economy, and emissions. In this paper, by taking into account the dynamical behavior of the electronic throttle, the mechanism model is first built, and then the mechanism model is transformed into the state-space model. Based on the state-space model and using the backstepping design technique, a new backstepping controller is developed for the electronic throttle. The proposed controller can make the actual angle of the electronic throttle track its set point with the satisfactory performance. Finally, a computer simulation is performed, and simulation results verify that the proposed control system can achieve favorable tracking performance.
Due to the complexity of determining the initial rotor flux and detecting errors, conventional rotor flux observation methods are easily affected by direct current (DC) components and harmonics. To address this issue, this paper proposes an in-phase filter (IPF)-based rotor flux observation strategy for sensorless control of permanent magnet synchronous machines (PMSMs). The core components of the IPF consist of a double second-order generalized integrator (DSOGI) and a phase angle compensation transfer function (PACTF). The DSOGI provides a accurate electrical angular frequency, while the PACTF implements a phase correction to the v q ′ signals. By employing IPF structure, accurate observations for rotor flux, electronic speed, and rotor position are achieved, which can be effectively used in the sensorless control of PMSMs, eliminating the need for magnitude and phase compensations. Finally, the proposed observation strategy is applied to an experimental bench of a PMSM, and its effectiveness is illustrated by experimental results. From experimental results, it can be concluded that the IPF is significantly better than the LPF, and 5% more accurate than the observer based on cascade second-order generalized integral(CSOGI) overall.
No abstract is provided for this article.
In this paper, a mathematical model for vehicle-occupant frontal crash is developed. The developed model is represented as a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The springs and dampers in the model are nonlinear piecewise functions of displacements and velocities respectively. More specifically, a genetic algorithm (GA) approach is proposed for estimating the parameters of vehicle front structure and restraint system. Finally, it is shown that the obtained model can accurately reproduce the real crash test data taken from the National Highway Traffic Safety Administration (NHTSA). The maximum dynamic crash of the vehicle model is 0.05% less than that in the real crash test. The displacement of the occupant is 0.09% larger than that from the crash test. Improvement of the model accuracy is also observed from the time at maximum displacement and the rebound velocities for both the vehicle and occupant.
