基于鲁棒控制理论的多模型分层切换控制

A new hierarchical switching control system of multiple models based on robust control theory is designed for some plant with large uncertainties. The model set and controller set are designed by robust control theory and the characteristics of robust control system are taken into account. A new kind of switching index function by estimating uncertainty is designed. Furthermore,stability of the closed system is analyzed by the small gain theorem in the sense of exponentially weighted L2 norm. And simulation is done on a plant with both parameter uncertainty and unmodeled dynamics. Both theoretical analysis and simulation results show that this new hierarchical switching control system can control the plant with large uncertainties effectively and has good performance of tracking and stability.     

基于二阶运动学模型的移动机器人主从跟踪系统的鲁棒控制

In this paper, we study the problem of modeling and controlling leader-follower formation of mobile robots. First, a novel kinematics model for leader-follower robot formation is formulated based on the relative motion states between the robots and the local motion of the follower robot. Using this model, the relative centripetal and Coriolis accelerations between robots are computed directly by measuring the relative and local motion sensors, and utilized to linearize the nonlinear system equations. A formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system including the internal dynamics. The control gains are determined by solving a robustness inequality and assumed to satisfy a cooperative protocol that guarantees the stability of the zero dynamics of the formation system. The proposed controller generates the commanded acceleration for the follower robot and makes the formation control system robust to the effect of unmeasured acceleration of the leader robot. Furthermore, a robust adaptive controller is developed to deal with parametric uncertainty in the system. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.     

一种新的非线性离散时间系统迭代学习控制的前馈－反馈机制

This paper presents a new feedback-feedforward configuration for the iterative learning control (ILC) design withfeedback, which consists of a feedback and a feedforward component. The feedback integral controller stabilizes the system,and takes the dominant role during the operation, and the feed-forward ILC compensates for the repeatable nonlinear/unknowntime-varying dynamics and disturbances, thereby enhancing the performance achieved by feedback control alone. As the mostfavorable point of this control strategy, the feedforward ILC and the feedback control can work either independently or jointlywithout making efforts to recongurate or retune the feedforward/feedback gains. With rigorous analysis, the proposedlearning control scheme guarantees the asymptotic convergences along the iteration axis.     

Conical tank level control using fractional order PID controllers: a simulated and experimental study

In this paper, simulated and experimental results on the conical tank level control are presented. PI/PID controllers of integer order (IO) as well as of fractional order (FO) are studied and compared. The tuning parameters are obtained first by using root locus (RL) and Ziegler and Nichols methods, for comparison purposes. Next, particle swarm optimization (PSO) is employed to determine the optimal controllers'' parameters using as fitness function the integral of the absolute value of tracking error (IAE). From the experimental results it is concluded that PI/FOPI are the controllers presenting the lowest IAE indexes, whereas PID/FOPID controllers present the lowest energy consumption by the control signal.     

Research on Dual Control

This paper summarizes recent progress by the authors in developing two solution frame-works for dual control. The first solution framework considers a class of dual control problems where there exists a parameter uncertainty in the observation equation of the LQG problem. An analytical active dual control law is derived by a variance minimization approach. The issue of how to determine an optimal degree of active learning is then addressed, thus achieving an optimality for this class of dual control problems. The second solution framework considers a general class of discrete-time LQG problems with unknown parameters in both state and observation equations. The best possible (partial) closed-loop feedback control law is derived by exploring the future nominal posterior probabilities, thus taking into account the effect of future learning when constructing the optimal nominal dual control.     

时变机器人系统的重复学习控制: 一种混合学习方案

Repetitive learning control is presented for finite-time-trajectory tracking of uncertain time-varying robotic systems. A hybrid learning scheme is given to cope with the constant and time-varying unknowns in system dynamics, where the time functions are learned in an iterative learning way, without the aid of Taylor expression, while the conventional differential learning method is suggested for estimating the constant ones. It is distinct that the presented repetitive learning control avoids the requirement for initial repositioning at the beginning of each cycle, and the time-varying unknowns are not necessary to be periodic. It is shown that with the adoption of hybrid learning, the boundedness of state variables of the closed-loop system is guaranteed and the tracking error is ensured to converge to zero as iteration increases. The effectiveness of the proposed scheme is demonstrated through numerical simulation.     

An overview of various control benchmarks with a focus on automotive control

There exists a gap between control theory and control practice, i.e., all control methods suggested by researchers are not implemented in real systems and, on the other hand, many important industrial problems are not studied in the academic research. Benchmark problems can help close this gap and provide many opportunities for members in both the controls theory and application communities. The goal is to survey and give pointers to different general controls and modeling related benchmark problems that can serve as inspiration for future benchmarks and then specifically focus the benchmark coverage on automotive control engineering application. In the paper reflections are given on how different categories of benchmark designers, benchmark solvers and third part users can benefit from providing, solving, and studying benchmark problems. The paper also collects information about several benchmark problems and gives pointers to papers than give more detailed information about different problems that have been presented.     

比例-积分TCP/AQM网络稳定性分析的参数空间方法

This work focuses on deriving the necessary and sufficient stability condition of transmission control protocol (TCP) network with a proportional-integral (PI) active queue management(AQM) scheme via a parameter space approach. First,a fluid-flow TCP's nonlinear model is converted into a lin- ear time-delay system of neutral type.Second,the stability of the closed-loop system is characterized in terms of the network's and the controller's parameters.By simulation studies we inves- tigate the boundary's relations of these two kinds of parameters in the parameter space and illustrate how PI controller's parame- ters affect the stability.Finally,different stability conditions are compared to show the less conservatism of our necessary- and sufflcient-condition-based method,and simulation experiments by both Matlab and NS-2 are conducted to prove our claim.     

一种模型参考鲁棒控制的跟踪性能改善

This paper considers the tracking performance problem of a model reference robust control (MRRC) for plants with relative degree greater than one. A new algorithm is proposed based on the earlier research. It is shown that by applying a special transformation to the tracking system, the L1 bound of the tracking error can be achieved even when the high frequency gain is unknown, and both the tracking performance and the control effort can be improved significantly. Furthermore, the strictly positive real (SPR) condition, which is an essential assumption of the earlier design, can be removed.     

Networked Control Systems: A Survey of Trends and Techniques

Networked control systems are spatially distributed systems in which the communication between sensors, actuators,and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices,increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems,process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The ‘control over networks’ is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of ‘control over networks’, providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.     

Control of the common rail pressure in gasoline engines through an extended state observer based MPC

In this paper, a model predictive control (MPC) solution, assisted by extended state observer (ESO), is proposed for the common rail pressure control in gasoline engines. The rail pressure dynamic, nonlinear with large uncertainty, is modeled as a simple first order system. The discrepancy of the model from the real plant is lumped as ``total disturbance'', to be estimated in real-time by ESO and then mitigated in the nonlinear MPC, assuming the total disturbance does not change in the prediction horizon. The nonlinear MPC problem is solved using the Newton/generalized minimum residual (GMRES) algorithm. The proposed ESO-MPC solution, is compared with the conventional proportional-integral-differential (PID) controller, based on the high-fidelity model provided in the benchmark problem in IFAC-E-CoSM. Results show the following benefits from using ESO-MPC relative to PID (benchmark): 1) the disturbance rejection capability to fuel inject pulse step is improved by 12% in terms of recovery time; 2) the transient response of rail pressure is improved by 5% in terms of the integrated absolute tracking error; and 3) the robustness is improved without need for gain scheduling, which is required in PID. Additionally, increasing the bandwidth of ESO allows reducing the complexity of the model implemented in MPC, while maintaining the disturbance rejection performance at the cost of high noise-sensitivity. Therefore, the ESO-MPC combination offers a simpler and more practical solution for common rail pressure control, relative to the standard MPC, which is consistent with the findings in simulation.     

Optimal control of quantum systems with SU(1,1) dynamical symmetry

$\SU(1,1)$ dynamical symmetry is of fundamental importance in analyzing unbounded quantum systems in theoretical and applied physics. In this paper, we study the control of generalized coherent states associated with quantum systems with $\SU(1,1)$ dynamical symmetry. Based on a pseudo Riemannian metric on the $\SU(1,1)$ group, we obtain necessary conditions for minimizing the field fluence of controls that steer the system to the desired final state. Further analyses show that the candidate optimal control solutions can be classified into normal and abnormal extremals. The abnormal extremals can only be constant functions when the control Hamiltonian is non-parabolic, while the normal extremals can be expressed by Weierstrass elliptic functions according to the parabolicity of the control Hamiltonian. As a special case, the optimal control solution that maximally squeezes a generalized coherent state is a sinusoidal field, which is consistent with what is used in the laboratory.     

Bifurcation Control,Manufacturing Planning and Formation Control

The paper consists of three topics on control theory and engineering applications, namely bifurcation control, manufacturing planning, and formation control. For each topic, we summarize the control problem to be addressed and some key ideas used in our recent research. Interested readers are referred to related publications for more details. Each of the three topics in this paper is technically independent from the other ones. However, all three parts together reflect the recent research activities of the first author, jointly with other researchers in different fields.     

高频增益符号未知的模型参考鲁棒控制

The problem of controlling a single-input-single-output plant without prior knowledge of high frequency gain sign is addressed by using the model reference robust control approach. A switching method is proposed based on a monitoring function so that after a finite number of switchings the tracking error converges to zero exponentially. Furthermore, it is shown that if some initial states of the closed-loop system are zero, only one switching is needed.     

一类串联系统的分散闭环辨识与控制器设计

Decentralized closed-loop identification and controller design for a kind of cascade processes composed of several sub-processes are studied. There are only input couplings between adjacent sub-processes. First, the cascade process is divided into several two-input two-output systems and coupling models of adjacent sub-processes are obtained via decentralized identification. TITO sys- tem is decoupled equivalently into four independent single open-loop processes with the same input signal. Then, a distributed model predictive control algorithm is proposed based on the coupling models of adjacent sub-processes.     

一类带有传感器故障的混合系统的容错控制

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.     

Output feedback adaptive DSC for nonlinear systems with guaranteed L-infinity tracking performance

In this paper, the problem of output tracking for a class of uncertain nonlinear systems is considered. First, neural networks are employed to cope with uncertain nonlinear functions, based on which state estimation is constructed. Then, an output feedback control system is designed by using dynamic surface control (DSC). To guarantee the L-infinity tracking performance, an initialization technique is presented. The main feature of the scheme is that explosion of complex- ity problem in backstepping control is avoided, and there is no need to update the unknown parameters including control gains as well as neural networks weights, the adaptive law with one update parameter is necessary only at the first design step. It is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded and the L-infinity performance of system tracking error can be guaranteed. Simulation results demonstrate the effectiveness of the proposed scheme.     

Robust control for electric vehicle powertrains

This paper considers the application of robust control methods ($\mu$- and ${\rm H}_{\infty}$-synthesis) to the speed and acceleration control problem encountered in electric vehicle powertrains. To this end, we consider a two degree of freedom control structure with a reference model. The underlying powertrain model is derived and combined into the corresponding interconnected system required for $\mu$- and ${\rm H}_{\infty}$-synthesis. The closed-loop performance of the resulting controllers are compared in a detailed simulation analysis that includes nonlinear effects. It is observed that the $\mu$-controller offers performance advantages in particular for the acceleration control problem, but at the price of a high-order controller.     

Cooperative visibility maintenance in SE(3) for multi-robot-networks with limited field-of-view sensors

Coordination of motion between multiple robots requires the robots to sense their relative poses. Common sensors depend on maintaining a line-of-sight between the sensor and the object being sensed. When the robots move relative to one another, they must coordinate their motions to ensure that these lines-of-sight are preserved. We propose a control method to preserve desired lines-of-sight in a multi-robot system where each robot has configuration space SE(3). A key feature of the control method is its ability to reject persistent bounded disturbances, enabling its use alongside additional control tasks. We demonstrate the effectiveness of the edge-preserving control in simulations involving additional input velocities that tend to break the edges.